DEV Community: Adamo Software

Delivery challenges: how technology disrupting supply chain errors

Adamo Software — Tue, 13 Sep 2022 04:06:31 +0000

Many firms have faced delivery challenges. Discover how technology helps tackle service delivery challenges in the service industry. Find more about this!

E-commerce has transformed the way people shop, enjoy shopping, and do business. The covid-19 pandemic makes customers rely on e-commerce for essential items. Consumers turn to e-commerce to fulfill their needs during the pandemic. Business owners jump into the e-commerce bandwagon to reach more customers and explore the benefits.

With the e-commerce growth, service delivery has surged as well. Customers expect their orders to be fast and delivered without any problems. Merchants either choose third-party online marketplaces or build channels in which they can track their orders via custom apps. Either building delivery channels or hiring a third-party delivery service, enterprises might have experienced delivery challenges. Explore how technology solves service delivery challenges in various fields, especially e-commerce.

What is e-commerce delivery?

E-commerce involves online shopping in which consumers and brands can buy and sell products and services from a website or mobile app. After that, delivery will transport those products to buyers. E-commerce delivery is an approach that transports ordered online products to the place of buyers.
Consumers increasingly rely on online shopping platforms, so brands need to sell and handle the delivery logistics. In many circumstances, service delivery is not a core competency. They will partner with e-commerce delivery providers to fulfill their orders and build trust with customers. Therefore, people hire offshore development teams to maintain e-commerce delivery management software.

Delivery methods in e-commerce you should not miss

The evolution of e-commerce has boosted delivery service growth, especially delivery models in e-commerce. You might wonder: how can a brand sell its products online with a low budget? Check out various delivery models in e-commerce to know-how.

Warehousing - The oldest distribution model but still common

Warehousing is a proper alternative for small and large businesses that sell a limited and controllable variety of items. Using the delivery methods in e-commerce allows you to respond immediately in case of a complaint or a return request. There are no delays or problems related to your inventory management. However, you need to hire warehouse employees or implement all the hard lifting yourself.

Outsourcing - One of the most popular e-commerce delivery methods

When outsourcing order fulfillment, you ship your staff to the warehouse of a third-party logistics company. You have to find a reputation fulfillment firm that does not offer higher prices. It is hard to set a balance between having competitive prices and profitability. Brands spend more time on promotion and marketing. Many startups use outsourcing software development services to build software applications for delivery services.

Dropshipping - A recent trend in the delivery method in e-commerce

This delivery method has recently been a trend in the e-commerce industry. Brands can hire the physical labor necessary to operate a business on social channels. Besides, brands have no interaction with the merchants. Dropshipping is a choice for widening your product line and launching a new venture. Nevertheless, you don't have control over your products. The time for return is slow, which might lead to complaints.

Subscription - An emergent trend in the e-commerce delivery service

The delivery method in e-commerce provides consumers with convenience and savings. Fulfillment firms will become a channel to communicate with customers. Brands cannot define product types with low quality since you offer the same product type every month.

Service delivery challenges for delivery providers & customers

Here are some service delivery challenges that providers and customers face with delivery management.

Hard to manage distinguished customers

E-commerce firms are potential customers for delivery providers since the delivery company is associated with many e-commerce companies. It is hard to have an effective management system to deal with various orders, keep COD records, and others. Hiring offshore development teams is a feasible solution to create software applications for delivery services that can work best in various firms.

Delivery delays

Transport consignments might cause some losses for delivery providers. It can also increase the shipping fees, leading to customer dissatisfaction.

Poor visibility of the delivery operations

Transparency in the delivery operations makes things intact. It ensures people deliver products on time. Yet companies without visibility have to pose difficulty in transporting orders on time. This service delivery challenge might result in customer dissatisfaction.

Minimize reliance on human resources

It is one of the biggest service delivery challenges for delivery providers. Using physical delivery teams for management poses a high operational cost.

Ensure goods delivery without damage

Transporting products safely to end-customers places is another service delivery challenges for delivery providers. Any damage to your product will harm your reputation and pose a loss.

Managing delivery operations consist of order segregation, order allocation, choosing the right vehicle for shipping, and other manual tasks. It requires a big team of employees, a considerable amount of time, and effort to execute those tasks. People need to use e-commerce delivery management software to calculate which shipping routes are economical, etc.

How e-commerce delivery solutions deal with those challenges

E-commerce delivery management software

Delivery providers have faced managing delivery operations, human resources, shipping costs, etc. Our e-commerce delivery solution is by using e-commerce delivery management software. Many delivery providers hire offshore development teams to build custom apps for managing delivery processes. Outsourcing software development services is another option for delivery companies to build software applications for delivery management.

E-commerce delivery management software boosts customer experience, ensures profitability, drives deliveries, and promotes brands to gain visibility. E-commerce delivery solution streamlines the picking up, and delivery process, and packages. The e-commerce delivery management software allows delivery providers to track packages from websites and shipments that have been imported or exported.

Once shippers deliver a package to the warehouse, they enter package details into the systems. It is easy to locate the package position in storage or out for transporting. E-commerce delivery solutions reduce the issues regarding misplaced packages and customer complaints. It helps retailers build effective e-commerce delivery strategies. Digital evidence of where, when, and how shippers transport your products helps resolve any problems. Besides, e-commerce firms can access package information on a web server whether shippers are on the road or in the office.

How delivery becomes a strategic advantage for business success

E-commerce delivery plays a vital role in e-commerce success since customer satisfaction directly affects product shipments. The package and the safe product delivery is a factor that keeps clients satisfied and coming back to the e-commerce website. Thus, an e-commerce business needs to build an effective delivery strategy for shipping goods and services. It will become a competitive differentiator in the market.

When the e-commerce industry relies on shipping, the shipment services should be top-notch and reliable with an extensive network. Business owners hire outsourcing software development services to apply advanced technology in delivery management software. The e-commerce delivery management software helps track and monitor the delivery process.

International projects of Euromonitor state that the growth for the global retail field will be digital by 2025. E-commerce sales will reach 1.4 trillion USD. The e-commerce business relies on shipment and transportation to deliver its products and services. Many delivery providers invest in innovative technology solutions that analyze data for cost savings and integrate human resources. Regardless of small businesses and startups, e-commerce delivery has supported them to enter the market. It promotes outsourcing software development services and even offshore development teams to build software applications that boost delivery management.

E-commerce delivery options for customers

E-commerce delivery options are one of the most important factors consumers consider when deciding to make an online purchase. They want their orders delivered quickly but do not pay more for shipping. Here are some e-commerce delivery options for your online store and customers.

Free shipping - The gold standard for online shopping

If you offer free shipping, it will remove an essential friction factor and enhance the way to your “Buy” button. Free shipping isn’t free. It comes from your profit margins, so business operators design your pricing strategies. One of the challenges when offering free shipping is that shipping costs may vary for every order.

Flat rate shipping

When using flat-rate shipping, you don’t have to use software applications to calculate actual shipping costs for each order. Consumers do not surprise when receiving high shipping charges for online shopping. This e-commerce delivery option rewards larger purchases. The more consumers buy, the smaller the delivery cost is.

At-cost shipping rates

Applying the actual costs for service delivery is an option that works best for e-commerce brands. Consumers who are familiar with online shopping will no longer be unpleasant. When you provide customers with the actual delivery cost to customers, your firm will get benefits.

You can charge less for your items due to excluding shipping costs. Brands use outsourcing software development services to build custom apps for delivery services. Consumers can see the delivery fee when making online purchases via delivery management software.

Expedited shipping

The e-commerce delivery option helps save time for customers. Sometimes, people order an item and want those item to arrive earlier. They are willing to pay an extra fee for expedited shipping at a flat rate or the actual cost for expedited delivery.

Wrapping Up

E-commerce delivery services have promoted the e-commerce industry growth recently. In a fast-paced world, delivery management challenges that business operators face. They can include managing delivery operations, human resources, delivery routes, technologies, etc. The article provides e-commerce delivery solutions that help brands handle delivery challenges while running their business.

The rise of Remote patient monitoring services in Healthcare

Adamo Software — Thu, 21 Jul 2022 07:41:27 +0000

Remote patient monitoring services are using digital technologies to monitor patient and medical care outside conventional settings. Take a look to know more.

Have you heard about the term Remote Patient Monitoring? Remote patient monitoring services position healthcare providers in the United States with tremendous benefits. The Center for Connected Health Policy states that remote patient monitoring services help keep people healthy, support older and disabled individuals to live at home longer.

Remote patient monitoring services have gained popularity before the covid-19 healthcare crisis. After the covid-19 outbreak, the healthcare software solution came into the spotlight as one of the most lucrative medical care management. Many hospitals are using offshore development teams of outsourcing software development services to build their remote patient monitoring programs. Let’s explore below to get insights into Remote Patient Monitoring.

What is Remote Patient Monitoring?

The adoption of virtual healthcare services and delivery care systems is one of the largest trends of healthcare industries. This healthcare trend includes remote patient monitoring services (RPM), also known as remote physiologic monitoring. Remote patient monitoring was a concept for most people before 2020; however, using remote patient monitoring services is changing since providers adopt customized services and prescribe to their patients.

According to the United States Government Accountability Office, remote patient monitoring is a coordinated system that uses one or more mobile devices. These remote patient monitoring devices transmit essential sign data and information on daily activities viewed by healthcare professionals. Now hospitals hire offshore development teams to create custom software development projects to deliver remote patient monitoring services for patients.

How Remote Patient Monitoring works

After understanding what is remote patient monitoring? you might want to know about how remote patient monitoring works.

Define the conditions

Providers define the conditions to monitor remotely and start programs through remote patient monitoring devices in healthcare. It means they start offering remote patient monitoring services to patients. They will collect sets of data related to blood pressures, heart rate, blood sugar levels, etc through various remote patient monitoring devices in healthcare.
Determine healthcare data captured through remote patient monitoring

They determine which types of healthcare data captured via remote patient monitoring devices in healthcare are beneficial for patients. With patients’ consent, healthcare providers prescribe remote patient monitoring services.

Provide devices for patients

Hospitals provide patients with remote patient monitoring devices to gather health information. They connect remote patient monitoring devices in healthcare electronically via cellular networking or Bluetooth. The most popular remote patient monitoring devices in healthcare are blood pressure monitors, blood glucose meters, weight scales, etc.

Other remote patient monitoring devices include pulse oximeters and ECG machines. When they set up devices appropriately, which helps capture health data and transmit it from patient to healthcare providers. Then providers analyze sets of data and give patients appropriate treatments and directions based on collected results.

To set up remote patient monitoring services, healthcare providers have to execute other steps. They include defining coverage for non-medicare beneficiaries, establishing a patient base, selecting devices. Besides, hospitals set up patient programs, develop policies and procedures, and train staff. Many hospitals use offshore development teams to build remote patient monitoring programs. Therefore, they don’t need to accomplish other steps and receive further assistance from outsourcing software development services.

Regarding patients, remote patient monitoring services support them primarily depending on the design or the complexity of various devices to use. For some custom software development projects, patients require in-person or virtual assistance to use remote patient monitoring systems.

Remote Patient Monitoring Services: Pros & Cons

Remote patient monitoring services have been trendy for medical and healthcare organizations in the United States. Many hospitals hire offshore development teams to build remote patient monitoring programs that boost their operations and improve patients’ experience. Besides, using outsourcing software development services is another solution to create custom software development projects for remote patient monitoring. Using a remote patient monitoring service in a healthcare system has its pros and cons. Thus, hospital operators are taking time to consider the advantages of remote patient monitoring and its disadvantages.

Advantages of Remote Patient Monitoring

On one hand, the cost of remote patient monitoring services has been more affordable. Many outsourcing software development services or hiring more offshore development teams are developing custom mobile apps and various types of remote patient monitors. Hence, people introduce remote patient monitoring services with many options and prices on the market. Moreover, remote patient monitoring services are easier to execute and more reliable, allowing healthcare organizations to operate more efficiently.

Disadvantages of Remote Patient Monitoring

On the other hand, remote patient monitoring service requires an investment. Not only do remote patient monitoring services need capital to operate, but the equipment for users, patients, and staff. Since the remote patient monitoring system might increase data, organizations need to hire more healthcare professionals. They need professionals reviewing and interpreting collected data and information, making better decisions related to patients’ treatment.

The advantages of remote patient monitoring outweigh potential disadvantages for healthcare organizations worldwide in monetary and moral terms. Remote patient monitoring services will offer a significant return on investment for their convenience and effectiveness.

Telehealth and Remote Patient Monitoring

Remote patient monitoring services refer to the specialized technology to enhance interaction between patients and clinicians outside hospitals. Telehealth is a broader terminology referring to the whole industry, methods, and technologies which improve healthcare experiences.

Regardless of Telehealth and Remote Patient Monitoring, RPM is a telehealth delivery system; however, telehealth can refer to various things. Thus, a telehealth technology can be a telephone, a video conference, or an IVR system. Telehealth and remote patient monitoring encompasses different technologies; however, users are applying these innovative technologies to collect data and exchange information.

There is a little bit of evolution with different technologies for telehealth and remote patient monitoring. In some cases, remote patient monitoring management will be considered as a type of telehealth. Hence, users can have a chance to connect to clinicians, but it is a holistic process while you are thinking about remote patient monitoring. It means if patients are connecting to clinicians, but the extended team. Telehealth and remote patient monitoring are terms used interchangeably but not the same. Many outsourcing software development services create custom apps for telehealth and remote patient monitoring.

Future of Remote Patient Monitoring

With its benefits to operations, remote patient monitoring services are becoming sought-after for many healthcare practices and even patients. Many organizations use offshore development teams to create custom apps for remote patient monitoring services. A remote patient monitoring platform helps practices expand healthcare outside the wall.

The CTA research stated that around two out of three use remote patient monitoring technology to manage their healthcare conditions and treatments in the future. Healthcare IT News article (2019 Spyglass Consulting) reported that nine out of ten hospitals had invested in remote patient monitoring services since transitioning to a value-centered care model. People carried out those studies before the covid-19 pandemic, so the figures are forecast to rise.

Wrapping Up
Remote patient monitoring services have been evolving for decades and become an integral part of the healthcare system. With its benefits to patients and healthcare providers, remote patient monitoring services contribute to healthcare services. Now it is up to those practices to meet the demand. If you are looking for an outsourcing software development company to build an app for remote patient monitoring, contact us. Moving forward, remote patient monitoring will continue rising and technologies will be more sophisticated which spurs further adoption.

Database Testing: Concepts, Types and Tools

Adamo Software — Thu, 23 Jun 2022 07:42:46 +0000

In software testing, database testing helps check the schema, tables, triggers of the database. The database testing finds errors to eliminate them.

The database is an important part of any custom software process. Thus, dedicated development teams have to monitor and create consistency and integrity in line with various applications. In any software development project, database testing is an essential stage that developers cannot overlook. If you want to understand more about database testing in software testing, please contact us for more information. You will have a chance to gain knowledge related to database testing. Let’s explore the database testing procedures further to understand their importance, types, and tools.

What is database testing?

Introduction to Database Testing

A database is a prearranged collection of data and helps manipulate the information. Users can easily manage and retrieve a set of databases. We can establish data sources into tables, rows, columns, and indexes, which makes it easier to define the data. Data management has become an easy task since people use the databases as tables for storing data, functions, triggers for manipulating data, and views. Nowadays, the database is more difficult over time owing to massive amounts of data in a software system.

In software testing, database testing helps analyze the schema, tables, triggers, etc under the test. Besides, this software testing also assesses data integrity and consistency. It might include creating difficult queries to stress test the database and review its responsiveness. Generally, it has a layered process, involving the data access, user interface, business layers, and database layers.

Objectives of the Database Testing

Ensuring data mapping: Database testing checks whether the industries in the user interface or front-end forms are mapped with the corresponding fields. Software development teams might consider whether database testing ensures data mapping or not.
ACID Properties of transactions: Each transaction that a database performs has to stick to four categories such as atomicity, consistency, durability, and isolation.
Data integrity: Software development teams have to ensure data integrity. The updated values of shared data are appearing on all screens. People should not update values on one screen and display the older value on another one. Besides, it is essential to update simultaneously the status of database testing.
Business rules accuracy: Sophisticated databases lead to complex components such as relational constraints, triggers, and stored procedures. Thus, software development teams come up with feasible SQL queries to validate the objects.

How to implement database testing?

People are using many database testing tools and techniques for this database testing. They will differ depending on the application, queries, and preferences of the software developers. The fundamental aim of database testing remains the same. Transactions in database testing is a technique. This process helps ensure ACID properties work correctly.

Moreover, there is schema testing in software testing. This type of database testing ensures that each object in the scheme works as it expects. Software development teams implement database testing through various thorough checks of each object. People test the objects such as databases and devices, names, settings, tables, etc.

People carry out stress testing and performance testing to ensure that basic database procedures are working properly. Thus, testers create certain queries and run them through various systems to see how the custom software responds.
No matter what people use procedure and testing methods, testers have a strong grasp of both SQL (Structured Query Language) and DML (Data Manipulation Language).

Types of database testing

There are three types of database testing that custom software development services might consider when creating custom apps. They include structural database testing, functional database testing, and nonfunctional database testing.

Structural database testing

This type of database testing is a technique that validates all components inside a database. People use these data elements for storage and end-users cannot directly manipulate sets of data. Moreover, the validation of database servers is an essential part of structural database testing. Successful database testing needs mastery in SQL queries.

Functional database testing

Functional database testing is a kind of database testing to validate various functionalities of a database from the end-users side. This type of database testing helps test whether users can implement transactions and operations related to the databases or not. Here are some basic conditions being observed for the validations of database testing:

Whether the industry is mandatory while accepting NULL values?
Whether the length of each industry is a sufficient size?
Do similar industries have the same names across tables?
Do there any computed aspects showcase in the database?

The type of database testing is a validation of the industry mapping from the end-user perspective. In a specific case, software development teams would implement an operation at the database level. Then, dedicated development teams navigate to the relevant user interface to observe and validate whether the validations have been completed or not.

Non-functional database testing

In the custom software development project, people categorize non-functional testing into different categories by the business requirements. Besides database testing in software testing, these can be loading testing, stress testing, usability testing, and compatibility testing. The loading testing and stress testing can be grouped under the performance testing serving two specific purposes.

Why database testing is important

A web application or mobile application cannot be put to feasible use without a completed database. The database is where people store information, and acts as an essential back-end structure of any custom software. Web applications rely on a completed database to store and create information and respond to various queries.

The key point of database testing ensures the values and information stored in the database system. When implementing database testing, testers need to have complete knowledge of various applications. They define all possible queries related to custom software. Software development teams also contribute to this. The database testing will help see if every query works since it should with regards to its specific features.

Without appropriate database testing, a web application and mobile apps may have hidden errors. When using custom software products, this could mean that data is lost and incorrectly stored. It has a main effect on the whole experience and effectiveness of web applications.

Database testing helps ease the complication of calls to the backend. This type of software testing also works to ensure whether there is no harmful and incorrect data being stored or not.

Database testing examples

There are many database testing tools that software development uses for the software testing process. Nevertheless, the basic functionalities remain the same. The software testing process is involving essential steps:

Preparing the development environment
Running the software
Checking results
alidating these results
Reporting the findings For instance, when preparing the development environment, testers and software developers come up with all queries that run through the applications. The software testing involves running these queries and checking the data integrity. It means that the resulting data needs to be truthful, accurate, complete, and verifiable. The software testing includes monitoring data mapping, the differences amongst ACID properties, and business rules accuracy.

Database testing tools

Database testing tools help generate, manage test data and perform database testing such as load testing and regression testing. Here are a few database testing tools:

Category	Description	Examples
Load Testing Tools	Database testing tools help put high usage loads on the database. It enables testers to determine whether your system landscape stands up to business needs.	Web performance/ Rad view/ Mercury
Data Security Tools	Database testing tools implement compliance and standards for the information security regulations.	IBM optim data privacy
Test Data generator tools	Testers use the database testing tools to create the test data for a database system. It requires a huge amount of data and testers need to perform database testing techniques. It is common for Load Testing and Stress Testing	Data factory / DTM data generator/ Turo data
Test data management tool	The database testing helps maintain version control for test data. Thus, testers need to define expected results compared to actual outcomes of tests.	IBM optim test data management
Tools to carry out Unit Testing	The database testing performs regression testing on the database.	SQLUnit, TSQLUnit, DBFit, DBUnit

Database testing with Selenium

Let’s see the specific procedure of implementing database testing with Selenium:

Step 1: Testers need to create a database.
Step 2: Once testers create and insert values. Thus, they can establish a connection to the database.
Step 3: After setting the connection, testers implement the queries and process the records on the database.

Wrapping Up

Have you got some fundamental concepts related to database testing? Database testing takes different patterns through many database tools on offer. Nevertheless, the whole results of all testers need to remain the same. Besides, with many features, factors, and processes on a database, manual testers need to get key concepts of this process. Testers will be sure to test the database properly when creating custom software products. Hopefully, you will find fundamental concepts essential and complete for beginners.

Scrum Framework for Beginners: setting roles, components and methodology

Adamo Software — Thu, 19 May 2022 08:20:10 +0000

As a project management methodology, Scrum framework helps boost the code quality while adapting to change quickly. Read on the components & roles of scrum framework.

As being used by software development teams, the scrum framework is the most common agile methodology. About 70% of software development teams use a scrum framework in the custom software development process. Scrum has widened other fields such as IT and marketing which are moving forward in complexity and ambiguity.

Leadership in the team is basing their agile management on scrum. Besides, it often combines scrum with lean and kanban methodologies which are subgroups of agile project management. The article will give an overview of the scrum framework regardless of benefits, components, and best practices.

Scrum framework: A brief overview

What is scrum framework?

People think scrum and agile are the same since scrum is centered on continuous improvement and a fundamental principle of agile. Nevertheless, scrum is a framework for having work done in which agile methodology is a mindset. You can use the scrum framework to deliver value to customers and build agile principles into daily communication and workflow.

The scrum framework helps solve possible problems based on the previous experience of software developers. It’s based on continuous improvement and adjustment to changing factors. Before a project, the team doesn’t know everything and needs to evolve through previous experience. Thus, the scrum framework is essential to help teams adapt quickly to changing conditions and customer requirements. Moreover, your software development team learns and improves much with a quick-and-short release life cycle.

The scrum framework is well-structured; however, it’s always rigid. Developers can implement the scrum framework which is tailored to any organizations’ needs. Clear communication, transparency, and devotion to continuous improvement are the fundamental center of whatever framework or methodology you choose.

Benefits of scrum framework over the agile methodology

Scalability
Scrum framework makes software development process iterative and handles the processes within specific periods. The scrum framework helps the software development team focus on different functionalities for a while. Therefore, the scrum framework in agile achieves better delivery with the specific needs of end-users and gives the ability to scale the modules. Those modules can be functionality, scope, design, and features transparently and simply.

Obedience of expectations
Customers set their expectations that each project has, so the team estimates them thanks to the information of the Product Owner. On a regular thing, the Product Owner defines the requirements and transmits them to the team.

Flexible to change
The scrum framework allows software development teams to react quickly to changes according to the customer needs or market development.

Ensuring software quality
Flexible workflows and a fulfilled functional version after interaction will produce a high level of the software product. Sometimes, customers use the features of the software product before developers complete the software.

Timely prediction
Using the scrum framework, developers estimate the average speed of the team through sprint. Therefore, it’s possible to write the features that are still in the backlog.

Reducing risks
Implementing the most valuable features in the first place and the speed of work help software development teams advance in the project. The scrum framework in agile helps eliminate risks effectively in advance.

Agile and Scrum methodology: What’s related to agile management?

Scrum framework is a subgroup of agile methodology. Agile features values and principles that depicts daily interactions and activities amongst different software development team members. Agile methodology is not a specific or prescriptive concept.

The scrum framework follows sets of values and principles of agile methodology in the custom software development process. However, it provides further definitions and specifications in a certain software development practice. Though the agile methodology and the scrum framework are not the same, agile scrum has become a preferred framework for custom software development projects.
What are the key components of scrum in agile methodology?

Scrum Artifacts: People consider scrum artifacts as a tool to handle problems. There are three artifacts such as a product backlog, a sprint backlog, and an increment.
A product backlog: The product backlog is a primary list of work that the product owner or product manager will maintain. It’s a list of characteristics, requirements, and enhancements which act as an input for a sprint backlog. The product backlog in the scrum framework in agile is the team’s “To Do” list. The product owner will constantly revisit, re-prioritize, and maintain the product backlog since we can learn more or even adapt to market changes.
A sprint backlog: The sprint backlog is a list of items, customer stories, or bug fixes that the software development team selects to implement the current sprint cycle. Each sprint in the scrum framework, the team chooses which items work on for the sprint from the product backlog. The sprint backlog might be flexible and even evolve in the sprint cycle. Nevertheless, the basic goal of what teams want to achieve from the current sprint cannot be negotiated.
An increment (or Sprint Goal): The increment is a usable end-product from the sprint product. Demonstrating the increment in the end-of-sprint demo allows the team to define what was done in the sprint. The word “increment” refers to the team’s definition of “Done” regarding a milestone, a sprint goal, or even a full version. It depends on how the teams define “done” and how they define the sprint goals.

For instance, the teams will release something to their customers at the end of each sprint product. The definition of “done” refers to “shipped”. Nevertheless, it might not be realistic for other types in the artifacts of scrum framework. Thus, the teams in the software outsourcing services company work on a server-based product. You might work in 2-week sprints; however, the definition of “done” can be a finishing part of a larger project. The longer it takes to release software products, the higher the risk that custom software might miss the mark.

Scrum ceremonies/events

Scrum ceremonies, sequential events, or meetings are some of the key components of scrum framework that scrum teams use. The ceremonies allow the teams to see the most variations in the scrum framework. For instance, software development teams find doing these ceremonies unmanageable and repetitive while others use it as a necessary check-in. It’s essential to use all of these events for two sprints and see how it works. Here is a list of all key ceremonies that a scrum team might need:

Organize the backlog (known as backlog grooming): Backlog grooming takes the responsibility of the product owner. It helps drive the product towards its product vision and have a pulse on customers and the market. Thus, the product owner maintains this list using feedback from end-users and the software development teams. The teams help prioritize and keep the list ready to work at any time.
Sprint planning: The entire development team plans the scope and context of the scrum framework. Scrum master leads the meeting and decides on the sprint goal. Besides, they can add specific stories to the sprint from the product backlog. The whole scrum team always aligns with sprint goals and evaluates the feasibility to implement these goals during the sprint. Every member will understand the scrum framework clearly on what and how can be delivered in the sprint.
Sprint: is an actual time when the team using the scrum framework will work together to complete an increment. Two weeks is a typical length for a sprint. However, it’s easier to scope the sprint within a week and to deliver an increment in a month. When using the scrum framework, the teams can renegotiate scope with the product owner if necessary.

All the meetings and ceremonies from planning to retrospectives occur during the sprint. Once the team establishes a time interval for the sprint in the scrum framework, it remains consistent throughout scrum software development. It helps learn from previous experiences and apply that insight to future sprints.
Daily scrum or stand up

This is a daily short meeting happening at the same time (often mornings) and a place to keep it simple. Many scrum software development teams finish the meeting within 15 minutes with a detailed guideline. People also call a daily stand-up meeting that needs to be completed quickly. The daily scrum meeting aims to align with the sprint goal and get plans for the next activities.
Sprint review

At the end of the sprint, the team works together to view a demo of the increment. The custom software development team represents finished backlog products to stakeholders and other teammates for collecting feedback. Using the scrum framework in agile, the product owner decides whether to release the increment or not.
The product owner also implements the sprint review meeting when he/she reworks the backlog product based on the current sprint. It can feed into the next sprint planning phase.

Sprint retrospective

When using sprint retrospectives, the team comes together to document and discuss what works and doesn’t work in a sprint. Besides, software development teams can define other activities in a project, people or relationships, tools, or even for essential ceremonies. Scrum framework, especially in the sprint retrospective creates a place for the team to focus on what works well and what needs to improve.

Three scrum framework roles in agile methodology

In the scrum framework, there are three roles such as product owner, scrum master, and software development team. Since the scrum teams are cross-functional amongst different positions such as testers, designers, UX specialists, software developers, and ops engineers.

Product owner

Product owners will be the champions of their custom software development products. They are trying to understand business sectors, customers’ needs, and market requirements. Moreover, they prioritize which work needs to be done by the software engineers accordingly. So what makes an effective product owner when applying the scrum framework:
Build, manage and maintain the product backlog in the scrum framework.

Work closely with the business analysts and the custom software development teams to understand different phases in the product backlog.

Provide the team with clear guidelines on which parts of the software product to deliver next.
Ensure the software product is shipped with a predisposition towards more frequent.

The product owner isn’t always the product manager. They play an essential role in ensuring the custom software development team delivers the most value of software products to the business. Furthermore, the product owner needs to be individual. Since the custom software development teams don’t want mixed guidelines from different product owners.

Scrum master

Scrum masters are considered champions for scrum framework within their software development teams. They will coach teams, product owners, and business analysts on the scrum framework process, and evaluate for ways to fine-tune its practice.

An effective scrum master understands which parts need to be done by the team and helps optimize the transparency and delivery flow. The scrum master in the scrum framework is a facilitator-in-chief who schedules resources in the custom software development process. These resources can be humans and logistics for sprint planning, stand-up, sprint review, and the sprint retrospective.

Scrum development team

A scrum development team is a champion for sustainable development practices when using the scrum framework. An effective scrum development team is tight-knit, co-located, and has five to seven members.

The scrum development teams will have various skills and cross-training so no one can become a bottleneck during the delivery process. They can self-organize and implement each custom software development project with a clear attitude. All members have responsibilities to help other members to complete a successful sprint product.

The team when using the scrum framework drives the plan for each sprint. They predict how much work they can finish in the iteration through their historical velocity as a guideline. Following the iteration, the length gives the custom software development team essential feedback on their delivery process. It helps make their forecasts increase over time.

Top scrum framework examples for online use

Hygger

Hygger suits high-skilled scrum software development teams that need to work together dynamically and focus on specific problems. This scrum framework example combines the power of Jira and the convenient use of Trello. Thanks to functionality, teams can easily organize scrum frameworks regarding planning processes, schedule sprints and user stories, and task boards. The scrum board helps visualize a big picture in the custom software development project and personalize it with scrum boards and features.

Scrumwise

Scrumwise is a scrum framework example for managing tasks while using the scrum framework. Thus, it helps create custom task boards to manage the custom software development team and projects. Users consider scrumwise as an ideal management tool without additional frills and other management features.
Amongst the key features of the scrum framework board, you can get task management, backlog management, task filters, activity history, integration with GitHub, etc.

Zoho sprints

Zoho sprints is an ideal scrum framework example for online managing the custom software development project. It features an intuitive design and useful characteristics that allow taking full advantage of scrum software development without being overwhelmed. Thanks to Zoho Sprints, people can add user stories to a backlog, set priorities to work items, and stay on track with specific task boards.

Orangescrum

Another approach for an online scrum framework example is Orangescrum. It helps realize all advantages of scrum methodology for different business sizes and industries. People recommend the scrum software development tool for remote team management since accurate information and transparency help users to be more efficient.

Furthermore, Orangescrum provides all scrum framework features in a place such as creating epics, stories, backlogs, sprints, task boards, and velocity charts.

Targetprocess

Targetprocess is one of the most popular scrum framework examples that benefit the custom software development teams. It provides an ideal customized project management solution with reliable functionalities for software outsourcing services using the scrum framework. Moreover, all features of user-friendly project management platforms provide flexible chances to oversee project development. Targetprocess provides simple features adaptable to any company’s structure, size, and management style.

The bottom line

What do you think about the scrum framework? The methodologies, roles, artifacts, events, and components are easy to understand. The semi-prescriptive method helps remove the ambiguities in the custom software development project while providing sufficient space for firms to boost their productivity. Nevertheless, the scrum framework takes time to understand, especially when software development teams are customized to a typical waterfall framework. To learn scrum framework and other agile methodologies with Adamo Software, check out the blogs.

Machine learning in healthcare got popular

Adamo Software — Wed, 20 Apr 2022 05:00:34 +0000

Machine learning in healthcare has emerged in recent years. The use of machine learning in healthcare helps to analyze complex medical data in the treatment process.

From its undoubted reception as a buzzword to multiple applications in healthcare, machine learning has emerged. We go through core concepts, challenges, and uses of machine learning in various aspects, especially healthcare. Machine learning in healthcare is a study of computer science for automation through past experiences. Machine learning is a subset of AI developing computer systems to perform tasks that require humans.

Machine learning in healthcare plays an important role in healthcare such as bioinformatics, gene detection for cancer, etc. This article provides the basis of machine learning in healthcare such as definition, benefits, applications, and trends. Healthcare software development also promotes the adoption of machine learning in healthcare. So what defines what makes machine learning in healthcare popular in recent years?

Machine learning in healthcare: A brief introduction

What is machine learning in healthcare?

Before going to the definition of machine learning, we take a look at the Artificial Intelligence concept. AI refers to any technique that makes computers imitate human behaviors to solve problems. A subset of AI, machine learning uses statistics to recognize patterns by learning and improving through experiences on previous data. Computers gather data and statistics and then learn from that data by developing algorithms.

Machine learning in healthcare helps patients and clinicians in many approaches. The most common machine learning applications in healthcare are automating medical billing, clinical decision, and clinical care guidelines. Researchers have developed the first machine-learning algorithm to predict toxicities in patients in their radiation cancer therapy for head and neck.

Besides, machine learning in healthcare of Google helps to detect breast cancer with 89% of accuracy. Machine learning in healthcare has also promoted the introduction of medical software development in treatment.

Types of machine learning in the medical field

Machine learning develops foundational models to address problems through data and statistics. This method has boosted the learning process and provided accurate results. In particular, types of machine learning in the medical field are supervised, unsupervised, semi-supervised, and reinforcement learning.

Supervised learning: Supervised learning involves training the model based on labeled data. Then supervised learning allows this model to make predictions for a new set of data. It will classify data into training and testing.
Unsupervised learning: It involves training data with an unlabeled value or target value. Machine learning will cluster the type of data by defining the hidden data patterns. This unsupervised learning tries to discover the data patterns.
Semi-supervised learning: a blend of supervised learning and unsupervised learning. It takes both labeled and unlabeled data. This semi-supervised learning detects outliers.
Reinforcement learning: This type of machine learning develops a system which improves business performance by collecting feedback. It is an interactive process that means learning from the environment with any help from humans.

As the healthcare market has emerged, there is health information. So doctors can not process without any help from machines. So machine learning in healthcare gives techniques to recognize data patterns and uses algorithms to predict possible outcomes.

Besides, different types of healthcare data have come in the healthcare sector such as clinical, sensor, omics data, etc. Healthcare software development also helps to identify patterns of data and predicts results of people’s health. Many distinctive methods extract relevant information and then different algorithms give a better prediction. Here are some healthcare data that people put in machine learning in healthcare for analyzing:

Clinical data: people collect clinical data during patient treatment such as laboratory tests, radiology images, allergies, etc. For example, a report by Weng et al. cited by iomcworld.org used machine learning in healthcare algorithms such as random forest, logistic regression, neural networks.
Sensor data: is an ordered sequence of pairs such as time-series signals. This data can be simple numerical, categorical data, or even more complex processed by computing devices. Moreover, it can be wearable sensor data collected from arms, hands, and thighs, or even wireless sensors using mobile apps.
Omics data: is a collection of complex and high dimensional data such as genomics, transcriptomic, and proteomics. Handling this data requires various techniques in machine learning in healthcare algorithms. So many healthcare software development services join hands to develop medical software to solve complicated cases.

Machine learning and healthcare: Benefits & How to use

Use of machine learning in healthcare

The fundamental goal of machine learning in healthcare is to support doctors’ works by providing better service and care. Take a look at some examples of the use of machine learning in healthcare.

Use of machine learning in healthcare in personal treatment
One of the cases of machine learning in healthcare is customized treatment. People use personal data and analysis techniques to improve personalized healthcare services. Machine learning algorithms develop personalized treatment systems based on patients’ statistics and symptoms. By combining clinical, socioeconomic, and pharmaceutical data with ML algorithms, doctors observe the effectiveness of personal treatment. Additionally, healthcare software development services can also develop custom apps to manage medical treatment for individuals.

Machine learning and healthcare service management
As stated in pharmaceutical research, machine learning in healthcare thrives in many areas of operations and management. Nevertheless, the treatment journey within hospital zones creates administrative tasks and departments for patients. So these processes will impact doctors, nurses, and patients. It is a complicated journey and leads to high costs for hospital operators.

Medical software development services introduce healthcare apps that help to automate administrative tasks to simplify treatment journeys. The adoption of machine learning in healthcare journeys improves care, supports medical staff in their medical studies, and optimizes costs.

Providing virtual assistants for patients
Virtual assistants are a healthcare technology solution through chatbots or even dedicated applications. In particular, machine learning in healthcare has recently emerged to support patients during their treatment journeys. For example, virtual nurses are available to answer questions or requests immediately and provide advice or directions. This virtual assistant can record interactions and disease information so doctors use them in the treatment process for patients.

Furthermore, virtual assistants can generate significant savings for hospitals by reducing stresses related to medical care. The adoption of healthcare software development can remind patients if they have medical checks for each month.

Innovative developments of new drugs
The use of machine learning in healthcare at the early stage of drug discovery has potential for various services. Machine learning in healthcare can help to develop new drugs from initial screening to the expected success rate. It also helps to predict the effectiveness and safety of new drugs. Machine learning in healthcare is beneficial for drug development since each compound has a specific purpose. It will limit the drug development to a small number of compounds.

Diagnoses
The use of machine learning in healthcare is essential for doctors to provide diagnoses for patients during their treatment. Machine learning in healthcare can help to save time and optimize correct diagnosis through healthcare software development services. Furthermore, it opens more perspectives in the prediction of possible diseases.

Heart disease diagnosis: people are using machine learning in healthcare to diagnose heart disease. Heart disease diagnosis is a topic of much research globally. Thus, the automated heart disease diagnostic program is one of the human achievements in the 21st century.
Liver disease: The use of machine learning in healthcare helps to predict liver disease. Predicting liver disease is a challenging task since many possible diseases can affect the liver. Besides, it requires a large amount of data on liver disease.
Cancer detection: Machine learning in healthcare can detect different types of cancer thanks to the adoption of AI. Scientists have upgraded the algorithms which observe and detect different forms of cancer.
Diabetes: Diabetes damages many body parts such as the heart, kidneys, and nervous system. ML in healthcare will identify markets of diabetes at the early stage to save patients’ lives. Healthcare staff can use algorithms of machine learning in healthcare to predict diabetes.

Benefits of machine learning in healthcare

Medical imaging - One of the largest benefits of machine learning in healthcare
Computer visions are one of the most significant healthcare sectors owing to the technology in healthcare. Many firms in the medical industry are combining cognitive computing and genomic tumor sequencing to introduce healthcare software development. It will help to give precision medicines. Executing machine learning in healthcare will define diabetic retinopathy and macular edema in the photographs. Thus, improving medical imaging is one of the best benefits of machine learning in healthcare that you might know.

Robotic surgery: Recently, robotic surgery has gained massive popularity. Machine learning in healthcare technology uses robots for surgical processes in healthcare. Replacing human surgeons with robotics brings tremendous benefits such as operations in small spaces, radically increasing the chances of human-based problems such as shaking hands. Machine learning in healthcare, particularly robotic surgery focuses on machine vision. Besides, it can evaluate distances to a higher degree of accuracy and identify specific body parts or even organs within a body.
Generating electronic healthcare records: There is a series of patient data during their treatment processes. Hospital operators in the healthcare industry should record healthcare data from patients. Thus, machine learning applications in healthcare will develop electronic smart records for the treatment processes. It will keep medical records, health conditions and even suggest treatment plans if required through healthcare software development services.

Machine learning applications in healthcare

Chatbot - An example of machine learning in healthcare

A chatbot is a computer system that allows humans to use natural human languages to interact with people. Some examples of chatbots are AliMe, DeepProbe, RubyStar, etc. Healthcare software development also supports hospitals to introduce chatbots through mobile apps. These chatbots can answer questions or requests from patients 24/7. Initially, people define targeted topics or conversations and then develop model conversations based on these topics. A chatbot recognizes the user input via many forms and accesses the information to provide knowledge.

Patient care during and post their treatment journeys

Chatbots have the potential to improve medical communication between clinic-patient and doctor-patient. It helps meet the high demand for healthcare services such as remote testing, monitoring of medical check-ups, and telephone consultations. The use of machine learning in healthcare can conduct health surveys, set up personal medical reminders, book appointments, etc.

Additionally, chatbots provide fast responses to questions while seeking specific symptoms in possible diseases through mobile apps. Healthcare software development services can collect data in several ways such as text-based services, mobile applications.
A study shows the effectiveness of healthcare software (iDecide bot) on predicting cancer. This healthcare software provides cancer knowledge, which helps to reduce anxiety and depression after using iDecide bot.

Machine learning applications in healthcare: Radiology and Radiotherapy

People use machine learning in healthcare to automatically classify patient data. These data can be behaviors, demographics, decisions, emotions, clinical factors, etc. A recent study shows that 80% of people are looking for medical-related information via online channels. Online support groups give them a comfortable space to get advice, express emotions, and share information through mobile apps.

Radiology educational posts on several social media sites are also beneficial. For instance, OnBot uses suitable mapping techniques to transform information and knowledge into a database and then utilize the data to run a chatbot.

Machine learning in healthcare can take advantage of the big data cloud. A mobile healthcare app calculates the monitor unit and even provides the treatment dose or daily dose. Telehealth is a kind of service administered through telecommunications or technology in healthcare. It allows remote bidirectional communication such as visual and audio interaction with users. For example, patients with cancer follow telephone communications in prostate, brain, colorectal, and bladder cancer.

Education and knowledge transferring systems

To provide practical healthcare workforces, interprofessional education through mobile apps is essential. It allows more than one professor to learn from others or each other, which increases collaboration and promotes healthcare service quality.
Moreover, people can use high-qualified healthcare information from the online discussion for conducting a chatbot. The chatbots present high potential in far-distance education and even act as undergraduate advisors. The use of machine learning in healthcare can reduce costs dramatically and make treatment sessions more engaging to experienced doctors.

Quick responses, especially during a covid-19 outbreak

In emergencies, a few minutes can save one’s life. So quicker healthcare and accessibility can save more lives. Machine learning in healthcare can distribute medical supplies on-site in many emergency cases. People can apply this system in urban, semi-urban, and remote areas. ML in healthcare allows hospitals to provide specialized treatments despite the absence of medical staff.

The covid-19 outbreak affects the cardiovascular system of patients. So it requires fast and specific tools to identify the symptoms early. Many countries are over-stretching their healthcare sources to prevent the pandemic spread. Artificial Intelligence helps to monitor and control the pandemic in some areas. It also helps to detect the disease at the early stage through healthcare software. Moreover, AI plays an important role in analyzing data to aid covid-19 research.

Final thoughts

Machine learning in healthcare is a robust and powerful tool that can benefit the healthcare industry. The use of machine learning in healthcare helps physicians and scientists to develop personalized therapies and improve the quality of people’s life. So what makes machine learning in healthcare so popular? Machine learning is promising to develop advancements in the healthcare industry so extensive that we cannot imagine.

What make UX research important

Adamo Software — Thu, 31 Mar 2022 04:57:24 +0000

UX research as a way of studying user interactions leverages many UX research methods before designing software development products. What to learn from UX research? Read below.

The practice of UX research has evolved significantly in recent years. To develop software development products, you must know your users' portraits and their needs. It is where UX research or user experience comes in. The definition of UX research has changed continually.

UX researchers need to study end-users to collect and analyze data that informs the product design process. UX research serves various purposes via the design process. It helps identify and prove the assumptions and recognize the needs and goals. Overall, UX research informs our workflow, boosts understanding, and improves work results. Firms offering software outsourcing services consider the application of UX research for their custom software development.

What is UX research?

UX research includes numerous investigative methods to add context and insights to the custom software design process. Unlike other sub-aspects of UX, people didn’t develop much research on the other fields. It mainly translates from other forms of research. It means that UX practitioners have applied many techniques from scientists, market researchers, and others. Nevertheless, some types of research are unique to the UX research world.

The main goal of UX research methodologies is to inform the design process from the perspective of distinguished end-users. The purposes of UX research and user-centered design are to design with the end-user in mind. Thanks to UX research, people know who that person is, what context they use this product or service, and what they need from customers.

UX research has two parts collecting and synthesizing data to improve usability. At the beginning of the software development project, UX research focuses on learning about project requirements from stakeholders and learning the needs of end-users. UX researchers conduct interviews, collect surveys, observe prospective users, and review data or analytics.

Through the design process, the UX research shifts to usability. UX researchers can conduct usability tests or A/B tests, interview users about the process, and test assumptions that improve the design process.

What does a UX researcher do?

We have provided you a better understanding of what UX research is, let’s find out what you might do as a UX researcher?

Significant tasks and responsibilities

The job of a UX researcher will vary based on the project you are working on or the software development company you are working for it. Here are a few of the responsibilities and tasks they may perform on the job:

Cooperating with designers and stakeholders to find out user needs and requirements
Defining research questions and choosing suitable methods of data collection
Planning budgets and timelines for research projects
Recruiting participants for the research process
Carrying out design research studies and analyzing data
Transforming findings into understandable insights
Showcasing your findings to designers, developers, and other stakeholders

Skills and tools for UX researchers

The UX researchers develop a set of skills to gain insights into current and prospective users. If you want to be a UX researcher, there are a few skills you need to focus on now.

Communication skills: The job of UX researchers involves working with a design team and communicating with research participants to answer questions.
Empathy: Understanding the end user's expectations, frustrations, and goals help you develop solutions for the user's needs.
Design thinking: Each stage of the designing process offers the opportunity to learn more about target audiences
Problem-solving: Thinking critically about what questions helps you select the appropriate UX research methodology.
Curiosity: A sense of curiosity prompts insightful questions and explores meaningful insights.
Collaborating: You are working with software developers, designers, product managers, and others to bring the best software development product.

Various UX research methodologies

UX research has driven a culture of user-centered design from the product itself to marketing campaigns. Let’s take a look at some of the popular UX research methodologies that help software development teams collect insights for every role.

Remote usability testing

Amongst different UX research methodologies, usability testing is one of the most popular methods. Remote usability testing helps people conduct effective UX research. This UX research methodology uses a platform to record the screen of test participants.

Since they can interact with your software product or experience in the natural environment such as at the home, office, and a specific location. Firms offering software outsourcing services should use the UX research method to develop software development processes.

Designers, product managers, and UX researchers can explore and understand how people respond to custom software products and experiences. Through usability testing, people can get the areas for improvement. The valuable insights are now eye-opening.

Diary studies

Diary studies are another UX research methodology for the software design development process. It is a form of longitudinal research, allowing users to self-report their activities to create a log of their activities and thoughts. This UX research method helps capture organic feedback on past activities that are repetitive, or unpredictable.

Card sorting - A qualitative UX research methodology

Card sorting helps group, label, and analyze information more efficiently based on various feedback from end-users. This UX research method requires you to create cards to represent items. People group or categorize these cards in several ways that make sense to them. Besides, card sorting helps navigate the website or the organization's content since it helps evaluate information architecture.

Surveys

Through questionnaires, surveys allow you to emphasize end-users to collect quantitative insights that are invisible to developers, managers, and marketers. Listening to and understanding customers helps you exploit new ideas, solve problems, and gather feedback. This UX research methodology makes the UX design process more efficient and receptive.

Interviews

Last but not least, interviews are one of the popular UX research methodologies to collect qualitative insights. Dynamic discussions allow us to observe verbal. non-verbal cues, ask open-ended questions to explore details that other UX research methodologies cannot. Interviewing is an effective UX research methodology for understanding complicated feelings and experiences since it allows you to ask follow-up questions.

Benefits of UX research

Making informed decisions with data: One of the extreme benefits of UX research in the design and software product development process is to make informed decisions based on data. It helps you understand end-user behaviors and make better decisions in business operations.

Removing bias from the UX design process: Psychologists might identify over 100 cognitive biases that can influence your decision and the software development projects we are building. Thus, an effective way to remove bias from the UX design process is to ask questions. Furthermore, when you communicate with others early and consistently to understand their mental models, it helps remove biases and improve the software product.

Testing and validating concepts: UX research empowers you to test and validate ideas without waiting for launch day to collect feedback. Thus, testing and validating concepts is one of the benefits of UX research for saving time.

Providing solutions that bring real value to clients: Another benefit of UX research is that UX research allows product managers to manage risks. On the one hand, the software development team wants to understand various problems that users have faced. So, it helps offer plans and solutions that satisfy clients with different needs. On the other hand, people ensure they monetize in a sustainable approach to further invest in the software development product.

Successfully delivering software products to market: UX research plays a crucial role in helping marketers understand clients’ needs and effectively communicate the value to the market. The more you comprehend your clients, the better you create products or services that meet expectations. Besides, you tailor your strategy to customers' specific needs and increase the chances of success. Thus, the benefits of UX research help software development teams successfully deliver software products to the target market.

Different types of UX research for best practices

Qualitative vs quantitative research - Common types of UX research

When it comes to understanding your end-users, you may consider which types of UX research regarding qualitative and quantitative. Since two types of UX research helps discover very different insights into the UX research process.

To get the whole picture of the UX research process, you should analyze where and why it happens. If you only have quantitative information, that leads to missing out on essential insights that help understand end-users better. Moreover, if you only carry out qualitative research, which is unuseful, it can’t be representative of a whole popular world, leading to biased information.

Attitudinal vs. behavioral research

Like quantitative and qualitative research, attitudinal and behavioral research are other types of UX research when assessing concurrently. Attitudinal research engages in the assessment of users’ preconceived attitudes towards experiences. For instance, it involves asking an end-user why they like and dislike features on the site before using it. However, behavioral research is another type of UX research focusing on what the end-user does.

Designing another parallel followed the difference between quantitative and qualitative approaches, behavioral research allows you to know what’s happening. Meanwhile, attitudinal research helps provide reasons why. Firms offering software outsourcing services should consider attitudinal and behavioral research in the UX research process.

Generative vs evaluative research

The purposes of generative and evaluative research are various. Generative research helps define the issue you are designing a solution for the software development process. While evaluative research allows you to evaluate an existing design such as prototype, final, and others. Therefore, both types of UX research should be beneficial for the software development process, especially the UX research process.
UX research tools.

Dscout: When doing exploratory UX research for product innovation, it is essential to understand your end-users, their daily context, and their needs. So Dscout is one of the UX research tools that experts should consider. Dscout enables you to organize massive data with visual data filtering and sorting different capabilities and provides automatic video transcriptions.
Miro: As one of the most common UX research tools, Miro has become an important tool to plan and prepare for the UX research process, organize, and socialize findings.
Hotjar: People use Hotjar for user testing and insights. It features interactive heat maps to visualize actions. This UX research has also introduced the ability to define polls and feedback from users based on user attributes.
Axure: This UX research tool allows users to test more complicated interactions with users early on in a project. Thus, Axure is the only product that provides us the flexibility in the UX research process.

Closing thoughts

So what makes UX research essential to product success? We have just given you a detail of what is UX research, its types, UX research methodologies, tools, and its benefits. Readers might have some ideas to apply to their UX research process and improve end-user experiences. As an outsourcing software company, we are always trying to focus on every aspect of the software development process, especially the design process.

Unit testing in real world: what to consider

Adamo Software — Wed, 23 Feb 2022 08:23:06 +0000

Unit testing (known as component testing) refers to a type of software testing. In which software development teams test each individual unit. Explore more in the article below!

Unit testing is an essential part of any software development project toolbox. Nevertheless, it sometimes can be hard to write a good unit test for a piece of code. Having trouble with code testing, software developers often imply their difficulties coming from the lack of fundamental testing knowledge.

The problems of complicated unit testing are a result of poorly-designed and untestable code. In this article, we have provided you with definitions, types, benefits, and tools of unit testing. We discuss what makes code difficult for testing, and anti-pattern and bad practices software development team should avoid. So that it can improve testability and benefits we can get by writing testable code. Hopefully, you will gain some ideas from our post.

What is unit testing?

Unit testing is a type of software testing in which a software development team will test individual units or software elements. The concept of unit testing is to validate every unit of the software code compared with expectation. Besides, the unit testing isolates a code section and identifies its correctness. It means the software unit can be a function, method, procedure, or even module.

In software development life cycle, unit testing is the first level of the software testing process before integration testing. Unit testing in software engineering is a white box testing technique that developers will execute. In a practical landscape, quality assurance also does unit testing owing to time crunch or even reluctance of developers.

Types of unit testing in the software development life cycle

Software development teams can implement unit testing in two ways regardless of manual testing and automated testing.

Manual Testing

A tester manually implements test cases without using any automation tool. She will manually execute each stage of unit testing. Manual testing is quite tedious and time-consuming, especially for tests that are repetitive.

Besides, it requires more effort to implement every test case. This type of unit testing does not require any background in unit testing tools. In fact, it is impossible to use 100% of automation testing during software development projects. Thus, there are some levels of manual testing executed. For some custom software development projects, manual testing is a fundamental part.

Automated Testing

Another type of unit testing is automated testing. Software development teams use unit testing tools for automating test and test cases. The automation tool records and saves your time by replaying as many times as needed without any further interventions.

This unit testing type uses various automation tools to enter test data into the system. Furthermore, automated testing can compare the expected outcomes to the actual ones and automatically create the reports. Nevertheless, the initial cost of building the unit testing automation tools is pretty high.

For unit testing, software development teams utilize manual testing or automated tests to meet customer requirements. This unit testing can be an individual function, method, procedure, and module in the custom software development project.

What are the tremendous benefits of unit testing?

Software development teams use unit testing to design robust software elements that maintain code and diminish issues inside code units. It is essential to perceive the importance of identifying and fixing defects in the early stages of the software development life cycle. This unit testing serves various purposes. Here are the tremendous benefits of unit testing.

Process becomes agile software development: To add more new features to the existing software solution, we have to change the old source code. However, changing things in the already tested code can be risky and costly.

Improving the code quality: Using unit testing can automatically improve the code quality. Software development teams identify the bugs throughout this unit testing process before sending them for the integration testing phase. It results in a robust software development product since developers write test cases by understanding specific features.

Detecting bugs in the early stage: Running unit testing, developers detect bugs and resolve them early in the software development life cycle. There can be flaws, missing parts, and bugs in the programmers’ implementation.

Easy to change and simplify integrations: Using unit testing makes it easy to restructure, change, and maintain the code. Besides, it makes testing the code more efficient after integration. Fixing a problem in the unit testing process can alter other parts of the software development process and testing stages.

Lowering cost: When software development teams can detect bugs and resolve them during unit testing, they help save cost and development time. Without unit testing, testers might detect bugs in a further stage after integration testing. So it is hard to trace and resolve the bugs, making it more time-consuming and costly.

Available for documentation: Developers can refer to the unit testing documentation when looking at the functionality at the further app development stage. Besides, it is easy to find the unit testing interface and work fast.

Easy to debug: Unit testing helps simplify the debugging processes. If the unit testing fails at any custom software development stage, developers need to debug the code again.

Demonstrating the code completeness: This is more useful in the agile software development processes. The software development company don’t get functionality built until they finish the integration process. People cannot justify the code completion since it will show that you have checked the code. However, running unit testing will demonstrate code completion.

Different unit testing tools in 2022

Jtest

As an IDE plugin, Parasoft Jtest can leverage an open-source framework such as JUnit, Mockito, PowerMock, and Spring with guidelines. It also provides one-click actions to generate, scale, and maintain unit testing. Besides, this unit testing tool takes advantage of time-consuming aspects of unit testing. Thus, it can focus on business logic and make more meaningful test cases. Jtest has become one of the most popular units testing tools in 2022.

JUnit

It is a free unit testing tool for the Java programming language. JUnit helps provide support for assertions to define test approaches. It is a unit testing tool that tests data first then inserts them into a piece of code.

NUnit

Being used as a unit testing framework, NUnit is another unit testing tool for 2022. NUnit is an open-source tool for writing scripts manually. Moreover, NUnit also supports data-driven tests.

JMockit

As an open-source testing tool, JMockit is a code coverage tool. It helps mock API with recording and verification of syntax. JMockit also offers line coverage and data coverage.

EMMA

This unit testing tool is an open-source toolkit for analyzing and reporting code in a Java programming language. EMMA supports coverage types such as method, line, and basic block.

PHPUnit

It is a unit testing tool for the PHP language. PHP Unit takes a small percentage of code called units, and testers will test each unit separately. This testing tool uses predefined methods to assert that the system should run in a specific manner.

Unit testing vs. integration testing

Another thing is the difference between unit testing and integration testing. Unit testing in software engineering verifies the performance of a small piece of code from other parts. Unit testing allows us to cover all test cases with a narrow scope, ensuring operation correctly in all parts.

However, integration testing shows that various parts of a system work together in the real-life programming environment. It can be a user performing high-level operations within the system. Besides, it enables validating complex situations that required external resources (databases, web servers)

When combining the unit testing and integration testing, tests ensure that each unit works correctly and independently from each other. Furthermore, all these unit testings work correctly when being integrated. It leads to a high level of confidence that the whole system will work as expected.

We need to detect what kind of test we are executing regardless of a unit and integration test. The difference can be deceiving. If we tend to write a unit test to verify some edge case in a business class, it will require external resources such as web servers and databases. Thus, software development teams might understand the differences between unit testing and integration testing when using each unit testing separately or both of them.

Unit Testing Example

It is suitable for you to do function testing whether a loop and statement in a program are working accurately or not. A beneficial unit testing example can be JUnit, XUnit, etc. Here are some explanations of unit testing examples:

Module Interface: In the module interface test, testers check whether they properly flow into the program unit or module.
Local data structures: The software development team inquires about the local data within various modules to be stored properly or not.
Boundary limitations: For example, many custom software development products often fail at boundaries regardless of conditions. That’s why the boundary related conditions will be tested. It makes sure that the program is appropriately working at that boundary.
Independent & errors solving paths: The software development teams use unit testing to see they are implementing their tasks at the end of the program. Besides, testers will review the unit testing to review in case of solving the errors properly by themselves or not. At last, it concludes that the unit testing focuses on the different modules of the custom software development products.

Closing thoughts

It is essential to conclude that tests can only test each software function independently. This type of unit testing saves time and cost. Furthermore, there is sometimes a requirement to generate mock objects to fulfill the dependency of a function. In this post, we have only provided the importance of unit testing regardless of definition, benefits, types, and tools. Besides, we also define the difference between unit testing and integration testing. Software development teams might consider various aspects of unit testing to develop custom software development projects.

Deep learning application in healthcare and wellness

Adamo Software — Fri, 31 Dec 2021 07:10:05 +0000

Deep learning has been an increasingly popular research direction, reforming modern performances such as object recognition, images, and machine translation. In the world of savvy-tech, data-driven machine health monitoring is more common owing to the widespread use of low-cost sensors.

Deep learning provides useful tools for processing and analyzing data, especially in the healthcare and medical fields. This article gives you definitions, benefits, and applications of deep learning in healthcare such as computer vision, natural language process, etc. In addition, it discusses some new trends of DL-based healthcare that might boom in the upcoming years.

Deep learning in healthcare: Definition & Examples

What is deep learning in healthcare contexts?

Deep learning, a subfield of machine learning, has experienced a dramatic emergence in the past few years. The increasing uses of computational power and the availability of massive databases have driven the demand for deep learning. Healthcare and medical fields have witnessed striking advances in the ability to analyze data such as images, language, and speech. The healthcare industry stands to benefit from deep learning due to data, the increasing proliferation of medical devices, and digital records.

Deep-learning models will scale to large datasets and continue improving with more data. This deep learning system can accept or read multiple data types as its input. For example, the most common deep learning models use supervised learning in which datasets are data points. Thus, many healthcare software development firms apply deep learning in healthcare to handle a large number of datasets.

Examples of deep learning in healthcare and medical aspects

Genomics

As an example of deep learning in healthcare aspects, Genomics uses deep learning techniques to help patients undergoing treatment. Professional clinical get an idea that might affect patient treatments in the future. Genomics is a steadily growing aspect. This deep learning technique supports clinical practice to give more accurate diagnoses.

Cell scope

Cell scope is one of the most effective examples of deep learning in healthcare. Thanks to deep learning in healthcare, helping parents monitor the health conditions or health status of their kids. People can see these deep learning techniques on any device, reducing the visits of parents to hospitals.

Insurance fraud

Insurance fraud is another example of deep learning in healthcare, which is used for medical insurance claim fraud. This deep learning technique is considered as predictive analysis which predicts possible fraud claims. Besides, deep learning also helps the insurance industry to send discounts or offers to target patients.

Medical imaging

Medical imaging is another example of deep learning in healthcare with healthcare software development such as CT scan, ECG, MRI, etc. This technique helps define and diagnose diseases for patients such as heart attacks, cancer, brain tumor, etc. Thus, deep learning in healthcare supports doctors to analyze patients’ diseases and give them useful advice.

Discovery drug

Deep learning in healthcare helps to discover drugs and develop them. Thanks to deep learning in healthcare, we gain insights from patients' tests and disease-related symptoms. Therefore, discovering drugs is one of the examples of deep learning in healthcare.

Alzheimer’s disease

One of the most crucial challenges people are facing is Alzheimer’s disease, especially the elders. So deep learning in healthcare helps detect Alzheimer’s disease at its initial phase, making it convenient for doctors to treat.

Benefits of deep learning in the medical field

To define how deep learning can benefit people in healthcare and medical aspects, let’s look into the healthcare treatments. So people apply deep learning in healthcare to assist professionals in the medical sciences and lab technicians. Here are a few benefits of deep learning in healthcare that you might know:

Deep learning in healthcare can be a learning tool collecting data and recording information about patients, their symptoms, and treatments. Doctors or professionals in the medical field can use this information as a future preference for the patients’ treatments.
Deep learning in healthcare allows you to create a model based on data sources available when you require a risk score on administration. Furthermore, healthcare software development firms apply deep learning techniques in healthcare to provide accurate and timely risk scores. That boosts confidence and appropriately allocates the resources.
When using deep learning in healthcare, people can reduce costs and get improved outcomes. For example, electronic health records (EHR) and digital healthcare applications can make data more accessible to trained algorithms than ever.
Thanks to deep learning in healthcare, health staff give more accurate and faster diagnostics during patient treatments. Doctors can identify patterns by connecting custom healthcare software. For instance, deep learning in healthcare can determine whether the skin lesions are cancerous or not like any certificated dermatologists.

Top applications of deep learning in healthcare

Computer vision: One of the largest successes of deep learning in healthcare

Computer vision emphasizes images and videos and handles tasks such as object classification, segmentation, and detection. This deep learning technique is useful in determining whether patients’ radiograph contains malignant tumors.

Medical imaging, for example, can benefit from advances in object classification and image detection. Many studies have proved the results in complicated diagnostics spanning dermatology, radiology, and pathology. Furthermore, deep learning in healthcare could support physicians by giving second opinions and providing concerning areas in images.

Remarkably, the models of deep learning in healthcare have obtained physician-level accuracy at a variety of diagnostic tasks. Thanks to the custom healthcare software, people can identify moles from melanomas, diabetic retinopathy, cardiovascular risk, and spinal analysis with magnetic resonance imaging.

Natural language processing

Natural language processing is one of the top applications of deep learning in healthcare. This application of deep learning in the medical field focuses on analyzing text and speech to infer meaning from words. When developing custom healthcare software, software engineers use deep learning algorithms to process inputs such as language, speech, and time-series data.

Significant successes of natural language processing include machine translation, image captioning, and text creation. In terms of the healthcare industry, sequential deep learning and healthcare languages benefit electronic health records (EHR). For example, a hospital typically generates about 150,000 pieces of data. With such a huge amount of data, the applications of deep learning in healthcare can solve many problems.

Reinforcement learning

As one of the most successful applications of deep learning in healthcare, reinforcement learning is a technique to train computational agents. This reinforcement learning can happen through trial and error, demonstration, or a hybrid method. Healthcare software development firms use reinforcement learning in which health fields accomplish better goals through learning expert demonstration. People can learn to predict the expert’s actions through imitation or by inferring the experts’ objectives.

Another healthcare domain that can benefit from deep reinforcement learning is robotic-assisted surgery (RAS). Deep learning can promote the robustness of RAS by using computer vision models to adapt to surgical environments and learn from physical motions.

Generalized deep learning

Beyond computer vision, natural language processing, etc, generalized deep learning is adaptable to healthcare domains where data requires customized processes. More specifically, modern genomic technologies collect various measurements of proteins in their blood.

Thus, deep learning in healthcare can use these data to analyze these measures, so it helps to provide more accurate treatments and diagnosis. Moreover, deep learning can further boost additional modalities such as medical images, and wearable device data.

Future of deep learning: A ray of hope for medical and health fields

The future of deep learning in healthcare has never been more promising. Artificial Intelligence and Machine Learning not only bring such a precious opportunity to develop custom healthcare software meeting specific needs. Moreover, deep learning in healthcare is beneficial for supporting clinical and patient care.

High-dimensional bio-medical information remains a challenging issue in shaping the healthcare industry. Different types of data are rising in the world of medical sciences such as images, sensor text, sensor data, etc. Healthcare software development firms use deep learning algorithms to solve this problem. Deep learning in healthcare makes unstructured data more successful representations. The latest application of deep learning in healthcare provides efficient paradigms to get the end-to-end learning models for complex data.

The use of electronic health records (EHR) helps advance clinical research and better decisions made during patient treatments. This custom healthcare software prevents the practice of learning models by synthesizing and demonstrating the data. Deep learning in healthcare can support and even affect decision-making processes in the clinical environment.

Deep learning is a set of many computational methods allowing an algorithm to demonstrate desired outcomes. The massive applications of deep learning in healthcare have brought many opportunities for the future of deep learning. For example, there is a further assessment and validation of medical images.

Healthcare software development services have used deep learning to train algorithms with feasible outcomes and measures. The algorithms in detecting reference diabetic retinopathy (RDRs) can be moderate. So the deep learning in healthcare-trained algorithms evaluates two operating points selected from high specificity and high sensitivity. That leads to better results. Thus, the future of deep learning in healthcare can be a ray of hope for the medical and healthcare industry.

Al and deep learning in healthcare: New trends of deep learning-based healthcare

Artificial Intelligence and deep learning in healthcare have boosted the healthcare and medical industry with emerging trends. The deep learning algorithm is convolutional networks. It helps analyze medical images, medical classification, segmentation, and other tasks. People use deep learning in healthcare areas such as retinal, digital pathology, and neural. Healthcare software development services see deep learning as an emerging trend in the field of data analysis. These deep learning algorithms have been named one of the 10 breakthrough technological applications in 2013.

Another trend of deep learning in healthcare that you might know is its application in healthcare predictions. People usually use deep learning algorithms in custom healthcare software to improve clinical predictions. Therefore, deep learning is an essential machine learning tool in imaging, neural networks, computer vision, etc.

Health informatics is also an emerging trend of deep learning in healthcare. Thanks to the applications of deep learning in healthcare, professional clinical doctors make predictions of disease to provide personalized services. Biomedical data in the healthcare industry has obtained knowledge from many applications of deep learning in healthcare based on techniques.

The healthcare field nowadays has various strategies which benefit individuals and societies owing to their spectrums. We have experienced advancements in Machine Learning and Artificial Intelligence in numerous fields so does deep learning in healthcare. Thus, deep learning in healthcare has emerged in recent years. A large number of data sets from clinical management systems feature the demand for healthcare services. This provides an opportunity for the application of deep learning in healthcare.

Closing

Deep learning is an element of machine learning, based on data learning methodologies. People use deep learning in healthcare such as recognizing speech, computer vision, natural language processing, etc. That has led to changes in the healthcare and medical fields. Besides, it helps boost the development of the healthcare industry. Healthcare software development services use deep learning algorithms to help doctors give more accurate diagnoses.

Deep learning in Healthcare and telemedicine field

Adamo Software — Thu, 30 Dec 2021 10:39:00 +0000

Deep learning in healthcare: Definition & Examples

What is deep learning in healthcare contexts?

Examples of deep learning in healthcare and medical aspects

Genomics

Cell scope

Insurance fraud

Medical imaging

Discovery drug

Alzheimer’s disease

Benefits of deep learning in the medical field

Deep learning in healthcare can be a learning tool collecting data and recording information about patients, their symptoms, and treatments. Doctors or professionals in the medical field can use this information as a future preference for the patients’ treatments.
Deep learning in healthcare allows you to create a model based on data sources available when you require a risk score on administration. Furthermore, healthcare software development firms apply deep learning techniques in healthcare to provide accurate and timely risk scores. That boosts confidence and appropriately allocates the resources.
When using deep learning in healthcare, people can reduce costs and get improved outcomes. For example, electronic health records (EHR) and digital healthcare applications can make data more accessible to trained algorithms than ever.
Thanks to deep learning in healthcare, health staff give more accurate and faster diagnostics during patient treatments. Doctors can identify patterns by connecting custom healthcare software. For instance, deep learning in healthcare can determine whether the skin lesions are cancerous or not like any certificated dermatologists.

Top applications of deep learning in healthcare

Computer vision: One of the largest successes of deep learning in healthcare

Natural language processing

Reinforcement learning

Generalized deep learning

Future of deep learning: A ray of hope for medical and health fields

Al and deep learning in healthcare: New trends of deep learning-based healthcare

Closing

Monolithic and microservices? Which architecture is better

Adamo Software — Tue, 30 Nov 2021 09:29:35 +0000

Monolithic and microservices are basic architectures for software design and development. Comparing microservices and monolithic is to define which fits your company.

In recent years, microservices architecture has become so common for its tangible benefits to software developers. These benefits may include an increase in flexibility, scalability, etc.

Nowadays, high-tech companies such as Netflix, Google, Amazon, and other firms have successfully shifted from monolithic to microservice architecture. Moreover, some corporations consider applying the microservice software architecture as a great way to boost their growth.

Monolithic architecture, however, is a model for developing a software solution. The applications of the monolithic software architecture tend to go down due to codebases, adopting new technologies, scaling, deployment, etc. Thus, is it worth it for businesses to switch the software system from a monolithic to microservices? Whether developing a microservices architecture will help you to reach business goals or not.

This article will give you an overview of monolithic and microservices and compare these software architectures to see which is suitable for your business.

An introduction to monolithic and microservices

Monolithic architecture

Monolithic architecture is a conventional approach to build software architecture with a single and indivisible element. This technology software solution usually includes a customer-side application, a server-side interface, and a database.

These elements will unite all functions that are controlled and served in a place. Regarding monolithic applications, all functionalities of a project appear in a single codebase. People design monolithic applications in different layers such as presentation, service, and persistence, and deploy that codebase in a single file.

Monolithic applications architecture shares a large codebase and lacks modularity. If software developers want to change anything, they have to access the same codebase. Thus, software developers will make changes in the whole stack at once.

Benefits of monolithic software architecture

Less cross-cutting concerns: Cross-cutting concerns affect the whole monolithic application regarding logging, handling and performance monitoring. When a project is running, the area of functionality focuses on only an application, making it easier to solve it.
Easier for software developers to test: Monolithic application is easy to debug and test since it’s a single indivisible element.
Simple to deploy and develop: Between monolithic and microservice, the monolithic architecture is easier to deploy since you don’t have to deal with many deployments, just one file. Besides, this software architecture is a standard way to build software when comparing monolithic and microservice. Any software developer has the knowledge and capabilities to develop a monolithic application architecture.

Drawbacks of monolithic application architecture
Understanding: Between monolithic and microservice, the monolithic architecture seems too hard to understand and manage when scaling up. Software developers need to redeploy the whole application, even a small change. For example, if any developer wants to join the project, it’s hard to understand the logic of existing applications, even for a single functionality. Furthermore, bug tracking and fixing are hard to handle when the monolithic architecture application becomes big and fast development.
Making changes: When teams want to make changes for this project, they will face some difficulties. It’s hard to execute changes in a large and complicated application with tight coupling. Any change will affect the whole application; thus, it requires thorough coordination from team members. That will lead to the overall process lasting much longer than usual.
Scalability: Developers cannot scale indivisible components independently, only the whole monolithic application. If a part of the application is handling traffic, the team needs to deploy the application on many servers.
New software technology barriers: Monolithic architecture applications will get problems when adopting new technologies since software engineers have to write the application again.

Microservices architecture

Microservices architecture is an approach for software development where an application divides into different components. With each element, software engineers will develop, package and deploy in an individual process. So all services feature their logic, the database and carry out distinctive functions.

This microservices architecture can communicate through mechanisms such as HTTP REST APIs. These different services in a microservices architecture have to write in different programming languages. Between monolithic and microservice, the microservices architecture has more benefits compared to the other. Microservice architecture has an impact on the relationship between the software application and the database.

Each application will have its database rather than share a single database with other microservices architecture. So it often leads to the duplication of some data; however, it’s important if you want to take advantage of microservices architecture.

Principles of microservices architecture

Taking single responsibility: Between monolithic and microservice, single responsibility is one of the most fundamental principles of microservice architecture. It features a single unit, even a method or a class, microservice architecture will take its responsibility. Each microservice architecture takes a single responsibility and also provides a distinctive functionality. The database in a microservice architecture is decentralized and each microservice owns a unique database.
Developing around business capabilities: There is a technology that will fit best for executing a unique functionality. When comparing monolithic and microservice, applying monolithic architecture is a big drawback. Since software developers can’t use different technologies for each functionality, it has to compromise in specific areas. Nevertheless, a microservice can easily adopt a new technology stack or backend database storage. That technology is useful for solving business problems. It means the microservice architecture can utilize different technology depending on business requirements.
Ready to deal with failure: Comparing monolithic and microservice, this microservice designs for failure cases. It must take advantage of this software solution. Fixing one microservice will not affect the whole application, so functionalities remain easily accessible to users. If there is a failure in one module, it results in the collapse of the whole application when applying monolithic architecture.

Advantages of microservices software architecture

When comparing monolithic and microservice, microservices can solve the drawbacks of monolithic architecture.

Independent elements:

Software developers deploy and update related services independently, giving more flexibility. Moreover, if you want to bug one microservice, it has just affected a particular area in the application. It’s easy to add features to a microservices architecture when comparing monolithic and microservice.

Easy to understand:

Classifying into smaller and simpler elements, microservices architecture is easier to manage and understand. Software developers need to focus on the service related to the business goals.

Having better scalability:

Between monolithic and microservice, the microservice application will scale independently. Thus, the whole process is much more cost-saving and time-effective than using monolithic architecture. When comparing monolithic and microservice applications, monolithic architecture has to be scaled if required.

Additionally, every monolithic application software has limitations regarding scalability. Therefore, the more users you have, the more troubles you have with using monolithic architecture. Many firms end up rebuilding their monolithic application software.

Disadvantages of a microservices software

Complexity: Between monolithic and microservice, microservice architecture requires more complexity. As a microservice architecture is a distributed software application, developers have to select and build connections amongst modules. Additionally, each microservice software architecture consists of a unique service, so software developers deploy them independently.
Being handled carefully: Between monolithic and microservice, this microservice software architecture is a complicated system. So skilled software developers have to connect all of them carefully.
Requiring cross-cutting issues: When developing microservice, software developers solve cross-cutting concerns comparing monolithic and microservice. They might include logging, metrics, health checks, etc.
Testing & checking: Between monolithic and microservice, the microservice structure makes the testing and checking processes more complicated and harder.

When to use monolithic and microservices architecture

Monolithic application

Monolithic software architecture may apply in those projects which don’t require a real-time response. It also can withstand downtime in web development. So these projects will set limitations to a size. If software developers want to expand the size of web applications, microservice architecture can handle it. Web applications use monolithic software architecture.

Microservices application

Microservice architecture is often used in large projects which require either real-time responses or maybe cannot withstand downtime. It can lead to abnormal spikes sometimes. For example, projects can use microservice software architecture such as TV networks, eCommerce platforms, food delivery apps, etc.

Differences between monolithic and microservices architecture

A website development application has two fundamental components regarding a relational database and a server-side application. First of all, the server-side application does the heavy lifting of whatever operations occur. It’s responsible for dealing with the HTTP requests from customers, retrieving data, updating the databases, and giving HTML responses to the customers.

Monolithic architecture is a conventional way to build web applications. All business logic to serve clients’ requests will package into a single process. For instance, if the web application is in Java, developers will package data in a war file, deploy it to a tomcat or a jetty server. If we use Python Django or Rails, the same web application has to deploy the same structure.

Now let’s check how microservices architecture can handle all drawbacks of monolithic architecture.

Monolithic to microservices: Which software architecture is suitable for your business solutions?

Features	Monolithic architecture	Microservice architecture
1. Basic	Monolithic architecture with one codebase builds as large distribution systems.	Microservice architecture creates many small independent modules that fit business functions.
2. Scale	It requires an entire application to scale although only one part of the distribution system is scaled.	Scaling of each element is possible since each will be decoupled to different services. Software developers can scale the application horizontally and individually.
3. Deployment	It’s quite difficult since a small change in the application will require the deployment of the whole application	Continuing deployment is possible since each service is separate from the other, so deployment as well.
4. Database	It’s a shareable database	Each module and service will have its database
5. Adopting new technologies	It’s hard to change programming languages or technologies since all of them depend on each other.	It’s easy to change framework, technology, or language as each module is independent.

Applying monolithic architecture

Between monolithic and microservice, each of them will have its advantages and disadvantages. So there will be different projects that will fit with different kinds of software architectures depending on many factors.

Small project: If you’re a startup or working on a small project, you don’t need to solve the complexity of microservice architecture. Comparing monolithic and microservice, the monolithic application can satisfy all business needs.
Simple web development: Small applications will not require much business logic and large scalability, so choosing a monolithic architecture is your best choice.
There’s no expertise on microservice: Between monolithic and microservice, the microservice application requires knowledge. If you apply microservice software architecture without any technical expertise, it will waste your resources.
Fast launch: If you want to develop a web or software application and launch it right away, the monolithic architecture is your best option. It will work well with fewer initial resources and validate your business ideas.

Using microservices architecture

Microservice expertise/knowledge: Developing a microservice software architecture is extremely risky if you don’t have any support from experts. Besides, knowing software architecture is not enough. You have to get DevOps and Containers knowledge as web development concepts are associated with microservices. Domain modeling knowledge is also important.

A complicated and scalable application: A microservice architecture scales and adds new capabilities to your web application. Thus, if you want to develop a big application with complicated customer journeys, the microservice architecture is the best option.

Enough human resources: Between monolithic and microservice, the microservice will require large human resources. Since a project using microservice architecture includes many teams being responsible for many services, engineering skills from the software developer team is a must.

Closing
Which software architecture is better between monolithic and microservice? Each software architecture has its own advantages and disadvantages for every software system distribution. Monolithic architecture fits with a small application and does not have much logic and modules. Meanwhile, microservice architecture fits best with big and complex applications with many things.

[free template] App UI package for Food ordering

Adamo Software — Wed, 29 Sep 2021 03:44:40 +0000

As social distancing spread globally, we saw a surge of demand in developing food ordering websites and apps. Unfortunately, there have some ambiguities in concept and features deterring companies from defining what they really want.

Accordingly, today we give you a small step of what a realistic app should be? Despite spending money buying UI design in ThemeForest or other design marketplaces, we give you FREE.

Ready-to-used template for everyone

We offer a full set of 15 app screens of featured delivery app, specialize in user apps. It contains interfaces for user login, placing orders, and tracking orders.

Screens	Description
Log-in/sign-up screen	For users access
Profile management screen	Payment, notification, languages, and more
Home Screen	Dishes and drinks exploring
Store screen	Menus and information display
Order and Payment	Order review and placing payment

Completed UI design for ordering app

As developers, you intend to explore the fundamental feature set to be applied in an app development project. In fact, each project has its own architecture and UI depending on the unique requirement of product owners. However, in terms of Food ordering apps, some common authorism could be shared.

As a UX/UI designer, it is a rare opportunity for you to capture a completed package UI for FREE. Our latest UI template comes from experienced designers who contributed to many real projects. As software outsourcing companies, we have a chance to execute several projects related to on-demand Food ordering and on-demand food delivery, which turned into a valued experience.

As a client planning a new project regarding food orders to your business, our template would give you the initial concept before talking further.

How to deal with Big data challenges

Adamo Software — Mon, 30 Aug 2021 09:57:07 +0000

Vast amounts of data from business are being generated and wrapped in a huge data package which is Big Data. Which drives the concern of big data challenges?

Currently, Big Data is considered the next revolution in the field of Software Development. Software companies are now making huge investments in big data analytics due to the advantages of data in today's development processes. Additionally, Big Data can leverage a custom software system to store and process data; it also helps the business keep up with travel, healthcare app development trends and predict the user's needs.

However, only 37.1% of the companies successfully applied Big Data solutions to their business, according to a NewVantage Partners survey. Commonly, many outsourcing software companies get stuck at the initial stage of their Big Data projects due to a lack of awareness of the Big Data challenges. In this article, we will discuss the challenges and the solution to tackle Big Data issues.

Overview of big data service

Generally, Big Data is a set of data that is huge in volume and growing exponentially with time. This set of data usually are difficult to process when using traditional data processing applications. With the use of Big Data analytics, multiple operations can be performed on a single platform. The tools can store a huge amount of data, preprocess, and analyze the data and then visualize it. Big Data is currently integrated into a custom software system to help software developers analyze every component of their software from the user perspective.

In practice, Big Data is classified into three types:

Structured Data
Unstructured Data
Semi-Structured Data

Structured Data

In the Structured type, data can be processed, stored, and retrieved in a fixed format. Every piece of information is grouped into rows and columns, which is much easier for a program to sort through and collect data. Structured data is the simplest type, which requires less preparation before processing, and each field in structured data is discrete and can be assessed separately or jointly along with data from other areas.

Unstructured Data

On the other hand, unstructured type refers to the data that lacks a specific form or design, which makes it difficult and time-consuming to analyze and process. Unstructured data is placed in a lake that contains the raw format of the data. Nowadays, organizations have a wealth of data available; however, they are difficult to derive value out of it since it is the raw form or unstructured format. That's why the ability to extract value from unstructured is one of the significant impacts behind the substantial growth in Big Data.

Semi-Structured Data

Semi-structured data is a form of structured data; however, it does not conform with the formal structure of data models. The semi-Structured type splits the gap between structured and unstructured data, it can be more challenging to work, but the limits and complexity in structured data don't exist. Still, semi-structured data is considerably easier to analyze than unstructured data.

Challenges of Big Data analytics

Undoubtedly, Big Data gives users the ability to aggregate various data from hundreds of sources in real-time. However, the fact is only a small number of organizations described their big data project as successful. From the user perspective, adopting this software development means they take the Big Data challenges below:

Handling Voluminous Data

The modern world is the world of sharing and connecting; as a result, the amount of data will be sufficient to fill a stack of tablets, which is equivalent to 6.6 times the distance from the earth to the moon, according to the IDC report-in 2020.

Unsurprisingly, Big Data management challenges are becoming more and more dramatic. Additionally, the rise of complex data sources such as the Internet of Thing (IoT), video, social media, and smart devices, which drive the online business transaction up to 450 billion per day; and the number of connected smart devices reaching 50 billion in just five years will generate a massive amount of data.

Big data security problems

Surely, Big Data involves dealing with data from numerous sources, and these sources mostly use a distinct format and collection method, which will increase the chances of hackers exploiting a vulnerability in the system. Due to the criticality of the data, a minor attack from cybercrimes can result in huge losses. Especially in application development, cybercrimes' attack leads to malware infections, data breaches, and service disruptions.

Confusion while Big Data tool selection
Users tend to be confused while selecting the best tool for Big Data analysis and storage, and sometimes they are unable to choose the right one. Selecting an ineffective analysis tool is the big data problems might cost the companies tons of money but not receive any advantages.

Infrastructure failures

Inevitably, Big Data contains huge amounts of data is vital for a company to operate requires a vast and complex hardware infrastructure. And the more complex of the data, the more advanced hardware systems will be needed. The challenges appear when storing those complex data that the company gathered. Onces the system fails, it affects the storing data process and makes it more difficult to work with.

Solutions to overcome Big Data challenges

A massive amount of data generated every day has motivated an emergent need for software development to organize, examine, and handle voluminous data efficiently. And to produce a beneficial conclusion from this huge amount of data, integrated Data Mining in Big Data is required.

Data Mining solution in Big Data

Data Mining in Big Data helps organizations better turn the raw data into useful data. Additionally, Data Mining provides businesses to resolving Big Data problems and issues in this challenging information age include:

Gather reliable data and information among vast amounts of data
Deliver selected and valuable data for data scientists to quickly initiate automated predictions of trends and behavior, also discover hidden patterns.
Detect fraud, build risk models and improve product safety

Cloud-based solution

As Big Data involves huge amounts of data which some times, system might fails due to the data complexity. In order to maintain the continuity and avoid sudden faults can happen, any Big Data storage should be backed in a cloud-based service to continue the fluency and the user can work even if failure happened.

Big data contains the most sensitive information of the business, providing a critical competitive differentiator. And, to enhance security, any piece of data information in the system must be encrypted and stored in two different tables.