DEV Community: Parth Bari

Voice Interfaces and ChatGPT: The Convergence of Text and Speech

Parth Bari — Fri, 01 Sep 2023 12:00:31 +0000

In an era characterized by rapid technological advancements, voice interfaces have emerged as a transformative force in digital interaction. From virtual assistants like Siri and Alexa to voice-activated smart home devices, spoken language interaction with technology is becoming increasingly common. Behind these voice interfaces lies a convergence of text and speech technologies, with ChatGPT developers playing a crucial role in this evolution.

The Rise of Voice Interfaces

Voice interfaces have evolved significantly since their inception. Early voice recognition systems were often limited by their clunky and inaccurate performance, hindering their practicality. However, recent developments in Natural Language Processing (NLP) and Machine Learning have empowered voice assistants to understand and respond to human language with remarkable accuracy.

The increasing adoption of voice technology is evident in our daily lives. Whether it involves requesting weather updates, controlling smart home devices, or dictating messages, voice interfaces have become an integral part of our interactions with technology.

ChatGPT: A Brief Overview

At the core of this transformation is ChatGPT, a powerful text-based conversational AI created by OpenAI. While ChatGPT is primarily known for text-based interactions, its influence extends beyond written communication. ChatGPT's in-depth understanding of language enables it to facilitate natural and dynamic conversations, making it a valuable tool for ChatGPT developers seeking to bridge the gap between text and speech interfaces.

Convergence of Text and Speech

Imagine how you talk to your voice-activated assistant like Siri or Alexa today. You ask questions or give commands, and it responds with answers or actions. However, these interactions often feel a bit robotic, like you're talking to a computer program.

Now, think about making these interactions feel more like talking to a real person. That's what we mean by the "convergence of text and speech technologies." It's about making voice-activated systems understand and respond to you in a way that feels natural, like having a real conversation.

Here's where ChatGPT comes in. ChatGPT is like a smart text-based assistant that can have conversations with you in writing, like in a chat. But what's exciting is that we can use ChatGPT's conversational abilities to make voice assistants smarter and more human-like.

As a ChatGPT developer, you can make voice assistants not just follow commands but also engage in real conversations. Imagine asking your voice assistant a complex question, and it responds by explaining things to you, just like a helpful friend would. This is what we mean by "sophistication" in voice interfaces. It's like having a more intelligent and friendly voice assistant that truly understands you. And this is possible by combining the power of ChatGPT with voice technology.

Use Cases and Applications for ChatGPT

Think of ChatGPT as a really smart assistant that can have conversations with you, just like chatting with a friend using text messages. Now, let's talk about how we can use ChatGPT in voice interfaces, like the voice assistant on your phone.

1. Customer Service Systems:

Imagine you're calling a company's customer service line, but instead of talking to a human, you're talking to a computer that uses ChatGPT. This computer can understand what you're saying and respond in a friendly and helpful way, like a real person.

This makes customer service interactions much better because you get personalized and quick answers to your questions. ChatGPT helps make these conversations feel natural and efficient, improving the overall customer experience.

2. Virtual Assistants:

You're probably familiar with Siri or Alexa, right? These are virtual assistants that can do things like tell you the weather or set alarms. Now, imagine if these virtual assistants were even smarter.

With ChatGPT, virtual assistants can give you more detailed and informative answers when you ask them questions. It's like having a super knowledgeable friend who can help you with anything. These voice interfaces become not only helpful but also really smart and adaptable.

So, in simple terms, ChatGPT makes voice interactions with technology feel more like talking to humans. It's especially useful for improving customer service and making virtual assistants even more intelligent and versatile.

Challenges and Considerations for ChatGPT

1. Privacy and Data Security:

Imagine having a conversation with your voice assistant. The things you say are personal, right? It could be about your plans, your preferences, or even sensitive information.
For ChatGPT developers, ensuring that these voice interactions are private and secure is a big concern. They need to make sure that your conversations aren't accessed by anyone else or used inappropriately.

2. Accuracy in Understanding:

When you talk to your voice assistant, you expect it to understand you correctly. But sometimes, it might misunderstand your words, leading to confusion.
ChatGPT developers work on making sure that the voice assistant accurately understands what you say. This is important because it prevents frustrating moments when your voice assistant doesn't get it right.

3. Addressing Biases:

Just like people, AI systems can sometimes have biases, which means they might treat different people or groups unfairly. This is a big concern for ChatGPT developers.
They work hard to make sure ChatGPT doesn't provide responses that are biased or discriminatory. They want it to treat everyone fairly and respectfully, regardless of who they are.

So, while making voice interactions with ChatGPT more natural and intelligent is exciting, ChatGPT developers have to deal with these challenges to ensure your privacy, accuracy, and fairness in your interactions with technology.

Building Voice-Enabled Chatbots with ChatGPT

If you're a ChatGPT developer interested in creating voice-based chatbots, it's a thrilling but also complex journey. This means making chatbots that you can talk to using your voice, like a virtual friend.

Here's what's involved:

1. Integrating ChatGPT:

Integrating ChatGPT into a voice-enabled chatbot means making ChatGPT understand spoken language. While ChatGPT is excellent with text, understanding speech is a different challenge.
It involves connecting ChatGPT to a speech recognition system, which is a technology that converts spoken words into text that ChatGPT can comprehend. This connection allows ChatGPT to "listen" to what users are saying and process it.
Think of it like teaching ChatGPT to understand spoken language just as it understands written text. This integration enables the chatbot to hear and respond to spoken commands or questions, making it more versatile and accessible.

2. Designing Conversational Flows:

Imagine having a conversation with a chatbot. You want it to feel natural, like talking to a friend who understands you.
To achieve this, you need to plan out how the conversation will unfold. You create a flowchart of possible responses based on what the user might say. For example, if the user asks about the weather, the chatbot should respond with weather information.
It's about making the chatbot react sensibly and coherently to different inputs from users. You design these flows so that the conversation makes sense and feels human-like.
The goal is to create a roadmap for the chatbot's responses, ensuring that users have a meaningful and engaging interaction.

3. Optimizing User Experiences:

When users talk to your chatbot, you want them to have a great experience. This means making the interaction smooth, comfortable, and enjoyable.
To optimize user experiences, you focus on several aspects:
- Response Time: Ensuring the chatbot responds quickly so users don't have to wait.
- Clarity: Make sure the chatbot's responses are clear and easy to understand.
- Personalization: Tailoring responses to individual users for a more personalized experience.
- Error Handling: Handling misunderstandings or incorrect inputs gracefully and providing helpful guidance.
The aim is to make talking to the chatbot feel natural, like having a conversation with a helpful friend who knows exactly what you need.
User experience optimization ensures that users find it easy to communicate with the chatbot, and their interactions are enjoyable and efficient.

The Future of Text and Speech Convergence

When we talk about the "future of text and speech convergence," we're looking ahead at how we interact with computers and devices.

Right now, we're making great progress in combining text-based AI like ChatGPT with voice interfaces, like talking to your phone or smart speaker. But this is just the beginning.

As technology continues to advance, it's likely that these voice interactions will become even better and more natural. Here's what we can expect:

1. More Seamless Interactions:

In the future, talking to your devices will feel even more natural, like having a real conversation. It won't be clunky or awkward; it will be smooth and effortless.
ChatGPT and other AI systems will understand you even better, and you won't have to adapt your speech to make them understand. They'll adapt to you.

2. Context-Aware Conversations:

Future voice interfaces will be smarter. They'll remember what you've said earlier in the conversation and use that information to give better responses.
For example, if you ask, "What's the weather like today?" and then follow up with, "How about tomorrow?" the voice interface will remember the context of the first question and provide a relevant answer.

3. Increased Intelligence:

AI systems like ChatGPT will become even more intelligent. They'll be able to answer complex questions, explain things in detail, and provide valuable insights.
These AI systems will feel like knowledgeable companions, helping you with a wide range of tasks and providing information and assistance.

4. Ongoing Innovation:

The world of AI and voice interfaces is constantly evolving. Researchers and developers are always working on new ideas and improvements.
This ongoing research and development holds the promise of exciting breakthroughs. We can expect to see innovations that we can't even imagine today.

Conclusion

The convergence of text and speech technologies, driven by the capabilities of ChatGPT, is transforming the way ChatGPT developers interact with devices. Voice interfaces are becoming intelligent companions capable of understanding and engaging with ChatGPT in meaningful conversations. As ChatGPT development continues to evolve, ChatGPT developers can look forward to a future where the boundaries between text and speech blur, creating a more natural and intuitive digital world.

How to leverage OAuth-Based Authorization Service Architecture for Secure Services in IoT?

Parth Bari — Mon, 21 Jun 2021 06:07:20 +0000

Internet of Things or IoT is the new age revolution in every business vertical these days. Especially during the pandemic, there has been a constant need for tracking symptoms related to COVID. Now that many countries have vaccinated a significant portion of their population, authorities are also looking to track people not vaccinated.

While it may seem to be a great idea to use IoT-based solutions for tracking and tracing COVID-related data, security can be a concern here. According to a research paper prepared based on a security survey of more than 40 android apps used for contact tracing of COVID patients, tracking symptoms shows that about 55% of them store sensitive data in cleartext. At the same time, 55% of these applications also have been producing insecure random values.

There is no denying that IoT-based services need reliable security to ensure that there are no data leaks and privacy of the users is maintained. One such solution is OAuth-based authorization architecture. It is an authorization protocol that allows standardized and straightforward access to data. Let’s first understand the basics of OAuth and then discuss how to leverage it for IoT-based services?

What is OAuth?

OAuth is an open-standard authorization protocol that defines authentication of access to data of unrelated servers and services without sharing initial, related, or single logon information. It is also referred to as the third-party, user agent, or delegated authorization.

OAuth is based on representational state transfer or REST web architecture. In terms of application development, you need to understand that REST APIs can help you create web apps with CRUD( (create, retrieve, update, delete) operations. So, using OAuth for web applications that connect with smart devices based on IoT makes more sense.

Now that we have a brief idea about the OAuth protocol for data authentications let’s see what type of OAuth-based architecture you can use for IoT products.

OAuth-Based Authorization Service Architecture

IoT devices interact with different systems heterogeneous in nature, and that is why security is quintessential for businesses to manage authentication of access to data. A user interacts with the IoT-based systems to access data through a user interface designed to ease navigation and offer better UX. However, what goes behind making these interactions is the deployment of well-structured OAuth-based service architecture.

Some of the critical elements of an OAuth-based service architecture that help enhance the security in IoT devices, like

Symmetric/asymmetric cryptographic algorithms
Hashing functions
Security protocols at network/transport/application layers

The fusion of these elements creates an architecture aimed at providing security features such as confidentiality, integrity, and authentication. OAuth-based authorization architecture can be invoked through a host object or IoT-based smart device. It acts as a remotely triggered switch that filters out all the incoming data access requests to decide which one of them is to be processed. The architecture enables the reduction of the overhead on the smart devices to process many requests and filter the malicious request for better security.

Securing data access in an IoT-based network is essential, primarily using a public cloud vendor to store data. When it comes to cloud security, securing your data access and authentication is vital. Such an architecture can help in securing the entire IoT system, including cloud storage or services.

Source: http://www.tlc.unipr.it/ferrari/Publications/Journals/CiPiGoVeFe_SENSORS15.pdf

Here, a user U creates the information or data which first reaches the service provider(SP) that hosts data generated by the users and makes it available through Application Programming Interface(APIs). The Service Consumer(SC) accesses the information stored by SP through APIs.

OAuth adds a layer of authorization through Authentication services to secure the API interaction between SP and SC. The entire process of data generation to the data getting authenticated is as follows,

User(U) is willing to use a medium(SC) to offer data as the web, mobile, and others.
SC needs to access the personal information of the users hosted on SP for verification purposes, and that is why it asks for an RT or Request Token.
A request token is a temporary ticket that SC uses to authenticate access to personal information.
SP verifies the identity of SC and returns an RT.
Next, the user is redirected to the SP’s authentication service(AS) with an RT.
The user then interacts with AS and grants permission to SC to access personal information after verification of RT.
RT is exchanged between U and SC for an access token or AT
SC receives AT through authentication callback
SC issues requests including AT to SP for services that require permission from the user, also known as the secured APIs.

The entire design of the OAuth-based architecture has one goal of offering SP capabilities to integrate the authentication layer without the implementation overhead. These capabilities are for HTTP requests or COAP(Constrained Application Protocol).

There are two crucial elements in OAuth architecture for IoT devices. It is essential to understand that remote configuration of the AS or Authentication Services is quintessential. It helps businesses who are using the MVP or Minimum Viable Product approach for their IoT apps. The remote configuration will reduce their development time and help reduce the time-to-market of the beta version.

Now, let’s understand more about access tokens and request tokens exchange that helps in securing the API interactions between service providers and end device or web.

AT Granting Process

Source: http://www.tlc.unipr.it/ferrari/Publications/Journals/CiPiGoVeFe_SENSORS15.pdf

With an OAuth-based architecture, the use of security protocols becomes more transparent for SC. It offers complete backward compatibility with standard OAuth client applications. RT works as a key for the SC to access information in the SP. At the same time, authentication services that are delegated with the SP help SC access the data by offering an AT. The exchange of RT and AT enables granting of access to information. Processing of AT is facilitated through the callback URL of authentication.

However, the authentication service doesn’t need to be delegated in the SP only as it can interface with third-party authentication services like OpenID. Such an interface can include QR codes generated on users’ mobile devices to access OpenID Connect data. So, you can configure these services remotely without having the restrictive delegations to SP. The data that SP holds before granting access to SC should be only related to the identity of users.

Request Authorization

Source: http://www.tlc.unipr.it/ferrari/Publications/Journals/CiPiGoVeFe_SENSORS15.pdf

Request authorization depends on the SP’s scrutiny of access requested by SC. What an IoT-OAuth secure architecture does is improve the transparency between SC and SP. With this architecture in place, the implementation of the OAuth logic for authentication of requests is not SP’s responsibility.

So, the overhead of implementation is reduced, making the IoT systems more lightweight and yet secure. It is an excellent option for businesses looking to make IoT-based smart devices sleeker in design as they don’t have to work around much processing power. Even service providers facilitating the interaction between the database and intelligent devices will have a reduced load of processing each request and authorizing them.

Conclusion

Security is an essential part of any application development due to data theft risks and compliance with data regulation protocols. At the same time, increased exposure to IoT-based intelligent devices and a surge in their demand have caused more data. Authentication of large amounts of data simultaneously can be tedious for service providers with the need for massive resources. An OAuth-based architecture can not only help keep your apps lightweight but reduce the cost of operations.

Software Development 101: Architectures, Approach, and more

Parth Bari — Sat, 15 May 2021 11:22:42 +0000

Software is often written in response to real-world problems that organizations discover through market analysis. It may seem an easy task for many people to read a set of requirements and interpret them in technology. But, on the contrary, it takes much planning and a synchronized development approach to deliver high-end software solutions.

Source: Worldwide IT enterprise software spending 2009-2022 | Statista

According to Statista, IT spending on software development is bound to grow at 10.8% to reach $517 billion by the end of 2021. Spending heavy on your software development will not make sense if you are not getting the right ROI.

Especially with the need for remote collaboration during a widespread pandemic, the reliance on software has increased. So, if you are trying to develop software for maximum ROI, here is everything you need to know.

#Types of Software

When it comes to software development, there are two primary considerations,

Off-the-shelf software
Custom software

Off-the-shelf software is a ready-made product that you can purchase from the market. It is ready to use, and there is no scope of personalization or any major tweak according to your business requirements. Take an example of software for data recovery, which is not specific for any business and can be used by anyone to recover data lost.

Other examples of such software are,

MS office
Adobe Photoshop
Windows 10 OS
Norton Antivirus
TurboTax
SAP
Oracle

Custom software is developed to cater to specific business requirements or even for individual usage. Take an example of HR that wants a reliable solution for the payroll system in an organization.

As HR policies for each organization differ, and that is the reason custom software will be the perfect solution. Customization needs insight into an organization’s needs, which is why custom software fits the bill, as it can have specific features.

Examples of custom software are,

CRM
CMS
ERP
Automated invoicing.
Web portals.
E-commerce software solutions
Bug tracking software

Once you know what type of software your organization wants to develop, the development approach takes center stage. So, Let's understand different approaches for software development.

#Approach

When it comes to the development approach, there are many different options for any organization, but the choice depends on the project’s scale and requirement. So, let’s look at some software development approach that you can use.

Agile Methodology

One of the most popular approaches recently among many enterprises has been agile methodology. It works quite the opposite of a waterfall approach and has several incremental iterations. Agile explores software development, outsourcing decisions in a phased manner where the first iterations are like a beta version or MVP(Minimum Viable Product).

Source: Most 5 Valuable Benefits of Agile Methodology | Blog – Denysys Corporation

Every iteration is created with the inclusion of feedback from the preceding version. So, the final version of the software has most of the problems already taken care of. Here, the iterative approach reduces the risk of catering a half-baked product to customers.

RAD(Rapid Application Development)

Rapid Application Development is designed to be fast. So, if you are looking for a quicker time-to-market, RAD is what you should choose. It leverages the rapid development process and makes sure that everything is swift. It involves four stages,

Planning
User design
Software Building
Switchover

Source: Most 5 Valuable Benefits of Agile Methodology | Blog – Denysys Corporation

For the RAD approach, you will need highly skilled developers, and that is why outsourcing can be a good option. However, the outsourcing decision depends on the project’s scale, so it is not ideal for small-scale development.

DevOps Deployment Methodology

DevOps is not a software development approach in the traditional sense but a series of activities that help sustain the organizational goal throughout the process. The DevOps architecture focuses on an operational point-of-view and enables reliable collaborations between different teams responsible for development lifecycle activities like development, QA, and even testing.

DevOps is also a great way to handle software development in the age of Big data. You can leverage big data analytics for improvement in the efficiency of DevOps and in turn software development.

FDD(Feature-Driven Development)

FDD is a feature-based approach where the first phase includes the creation of a wireframe model. Next is to create a list of features that your software will have, and then start building each feature. The software is like a suite of different features developed to offer an enhanced experience.

These are just a few examples of the many different approaches that you can use for software development. However, the right approach needs to be backed up by correct architecture to create high-quality software. So, let’s discuss some of the architecture patterns that you can use for software development.

#Architectures

N-tier Architecture

One of the most popular software architectures used by many organizations is n-tier or layered architecture. Here, the code is structured so that once the data enters the top layers, it moves through each layer until reaching the bottom or database. Each layer is programmed for specific tasks, like reformatting the data values or even checking the consistency of data flow.

MVC is an example of n-tier or layered architecture where there are three layers- Model, View, and Controller. The model part deals with the data side, while the view part deals with the representation layer or user interface. The controller acts as a trigger between model and view to make an effective connection. Such architecture is easily maintainable and reduces downtime.

Event-driven Architecture

Event-driven architecture makes the software experience seamless. Everyone is privy to loading pages, and you might be annoyed by waiting for a function to execute even after clicking several times. Here, the problem lies with the execution of the event or a user request. As organizations scale, there is a surge in traffic, and that is where event-driven architecture helps.

Here, you can create a central unit that delegates each request as an “event” and then is sent to execute different modules in software architecture. So, parallel event execution is solved, and there is a seamless experience for users. Such architecture needs high-end programming to reduce the overhead and offer lower downtime, which becomes a significant factor to outsource development.

Microservice Architecture

Microservice architecture is a suite of small and independent services that create a suite of responses for each user request. The best thing about this architecture is mutually independent services. So, while you are updating one service, it does not hinder the execution of other services in the architecture resulting in lower disruption.

Take an example of Netflix, where each service says smart download, recommended titles, favorite titles, or even top recommendations as per your region, acts as a suite. But, all of these services seem to be from different websites. The sync of the Netflix interface makes it look like a constellation of best streaming services.

Signing Off

Software development is getting more exciting with each innovation, and new development approaches adopted by organizations. For example, cloud-native software development has been gaining traction during the recent pandemic due to remote capabilities.

There is no end to the use cases for which software development can be used. However, the decision-maker for one’s software development approach or architecture will always remain aligned with business requirements.

API Security best Practices: What to keep in mind for apps?

Parth Bari — Mon, 19 Apr 2021 12:39:27 +0000

API(Application Programming Interface) is one of the essential parts of any application architecture. It is a protocol that regulates the data exchange between heterogeneous systems. Next time, if you are wondering how your smartwatch is showing relevant data on an application on the smartphone, it’s API’s magic. But what if this API is not secure? That is where you can lose essential data, even personal information.

According to Gartner, API attacks are bound to rise for enterprise data breaches year over year at an alarming rate. With the pandemic talking most IT assets online and making remote working capabilities necessary, the stress on API security is high.

There have been repeated phishing and data theft activities by hackers on APIs, making IT executives worldwide worried. A Salt Security survey on "The State of API Security – Q1 2021" indicates that in 2020, 91% of enterprises have seen at least one incident of API security breach.

So, worrying about your organization’s API security makes sense. Many scary server security vulnerabilities can happen if you don't follow best practices for API security. So, here are some of the best practices to follow for API security.

IT Asset Management

There are many different types of IT assets in a tech-based company. From the video APIs for creating video content to communication APIs offering functions like calling and messaging and even the kind of data on your servers, IT assets are diverse.

Creating updated documentation of the APIs and keeping records of all the data on your servers can help you identify the IT assets. Identification becomes vital to manage these assets.

For example, API documentation can help you manage different endpoints, deployed API versions, and others. Managing the API-based IT assets will also help you explore the vulnerabilities and debug risky endpoints.

Source: https://www.f5.com/labs/articles/education/securing-apis--10-best-practices-for-keeping-your-data-and-infra

If you compare a web API to an interface-based web app like Postmates, additional endpoints’ exploitation is at low risk due to decentralized deployments with a microservice architecture. The problems lie with irregularities in the updates of outdated API versions that lead to a lack of security. Running of retired APIs can put your systems at risk of cyberattacks.

One of the best practices you can follow is updating and recording each API version for your applications and software.

User Authentications

Authentication mechanisms need correct execution, which can block attackers from compromising the authentication tokens. Such attacks help them exploit the implementation flames of the system and procure vital user data. This process allows the attackers to hide behind the user authentication tokens to compromise the system’s ability to identify them.

Attackers can easily access user data, extort sensitive information, mimic users, and even overtake the entire account. Especially in Fintech apps where digital banking is enabled through API gateways, authentication takes center stage. It can expose your consumer’s credentials which leads to monetary loss.

Take an example of an online banking website. There are many different APIs that you can use, like REST API, and yet you can be prone to security issues if the authentication is not executed well. Using a proper user authentication protocol is one of the best practices that you should keep in mind.

TLS/SSL

SSL is a cryptographic protocol that helps to secure websites. An SSL or Secured Socket Layer ensures that your web server’s connection with the browser is secure. It uses API keys to aid the security of browsers and ensure safe web browsing.

There are two sets of keys that are used-public and private. The fact that Google’s algorithm considers an SSL certificate ideal for indexing the website in search result page rankings makes it more than just an accessory.

Though SSL is a necessity, there are other protocols too that are far more advanced. For example, using a TLS or Transport Layer Security can help you against cipher block chaining attacks. The main difference between SSL and TLS is the usage of HMAC(hash-based message authentication code) by the advanced TLS that makes it more secure.

API Firewalling

API gateways provide firewalling capabilities by embedding Apache ModSecurity. It is a tool kit that enables real-time HTTP traffic monitoring, recording, logging, and data access control data threats. Such gateways allow organizations to reduce the risks of application-level attacks and data threats due to API vulnerabilities. API firewalling can help you avoid attacks like SQL injection, cross-site request forgery, XSS, and many more.

The ModSecurity engine can be configured to protect the APIs and reduce threats due to issues in HTTP traffic. These API gateways ensure that each threat is identified and monitored for any exceptions.

Broker Authentications

Two types of authentications occur in any network. One is direct authentication which works best for intranet networks where the authenticator and user are using the same network. Simultaneously, when heterogeneous systems are interacting over different networks, you need a broker to authenticate the access to data.

Source: Direct vs. Brokered Authentication (brucejmack.net)

As the modernization of APIs increases, there has been a surge in data brokers or minors that dig users’ data to exploit the system. Through broker authentication on top of API encryption, your systems can identify such data minors and rectify the protocols to prevent any data theft.

Input Validation

One of the most significant API best practices to follow is validating each input that you pass on to the endpoint through an API. It is vital to the API security and reduces exposure of sensitive user data on the endpoint. Most of the endpoints are susceptible to data leaks, and that is where input validation can help reduce the risk on API security.

Conclusion

As more innovative technologies are integrated into the apps and web apps, the need for API security is bound to increase. Due to higher exposure of system endpoints and unique ways of attackers cloaking their presence behind user’s authentication data, the challenge of maintaining API security will be higher. But, organizations can help their systems stay alert by updating, maintaining, and inventorying API versions.

How to Automate Enterprise Software Testing With RPA?

Parth Bari — Mon, 22 Mar 2021 10:17:30 +0000

Technology advances and innovations in the IT industry have been pushing enterprises to innovate for their business operations. Enterprise software applications are the new age solutions that companies develop to match an ever-changing market’s business needs. Extending the enterprise software is not enough as you will need an extensive testing mechanism to validate secure access to vital data.

Image Source: https://www.statista.com/statistics/203428/total-enterprise-software-revenue-forecast/

According to Statista, the total IT spending on enterprise software will be $506 billion worldwide, with a year over the year growth rate of 8.8%. The cost of enterprise software development also includes expenditures on testing and fixing errors found during the tests.

So, how can we keep the testing automated and reduce errors?

The answer is simple, using the most advanced software testing approach called RPA or Robotic Process Automation.

What is RPA for software testing?

RPA or Robotic Process Automation tools are equipped with technologies like Artificial Intelligence and Machine Learning. It reduces the repetitive tasks in business operations to render automation.

If you consider the repetitive tasks that you need to reduce in software application testing, like logins, requesting data, assigning response, calculating the time of execution, and even memory consumptions are automated with RPA.

RPAs enable codeless testing to resolve queries, computations in testing, and maintenance of testing logs. It employs software bots or programmable bots that help mimic repetitive tasks otherwise being executed by humans.

When you are planning enterprise software development, there are many stages where automation can help improve efficiency. Any SDLC or Software development life cycle has the following steps,

Defining the problem within the enterprise
Designing the enterprise solution
Developing the software solution
Testing the software solution
Deploying the enterprise software application
Maintaining the ESA(Enterprise Software Application)

RPA can help enterprises to automate testing, deployment, and even maintenance of the ESAs by reducing the repetitive tasks, and simulations of testing environments. But, before you customize an RPA solution for your enterprise software solution, you should know the difference between ESAs, and others.

How is enterprise software different from others?

Enterprise software is different from your average software application used by businesses in many ways, such as basic structure and even deployments. Any company may be using two types of software- systems software and application software.

The systems software enables machines and computers of a business to execute general operations. Simultaneously, application software offers tools like documentation, spreadsheets, graphics, and others that companies can use.

Enterprise software is not for specific tools or functionalities that an application software offers and has more large scale attributes. ESAs can work across departments in an organizational structure rather than just a task-specific application.

Another aspect that makes ESAs different from other software is security. When you develop off-the-shelf software, there are from malicious sources, which can lead to data risks.

But, with enterprise software, you can ensure better security features, and as these are solo ownership tools, the chances of supply chain attacks are minimal. Apart from these attacks, ESAs can help make the data access within the organization secure than off-the-shelf software.

Now that we have some knowledge of ESAs let’s discover how RPA can help automate enterprise software testing.

Applying RPA for Enterprise Software Testing

RPA enables organizations to configure the software testing suites to emulate human actions and execute process testing seamlessly. Some of the essential applications RPA offers for automation across different domains are,

Insurance policy management
Patient analysis
Employee onboarding
Automated invoicing
Testing automation

Here, it is essential to understand that RPA automates processes and integrates the user interface accordingly. So, if you want to test the UI side, there are other automation tools that you can try, like Docker, which offers an OS virtualization.

Implementing RPA needs bot validations before application in testing automation because a low-quality software bot will not render accurate results. Verifications must be done based on functionality, data integrity, and other factors.

So, here is how you can use RPA bots to maximize the software testing accuracy.

Step1: Identifying QA Pain-points

The first step towards integrating RPA into your enterprise software testing mechanism is identifying the repetitive tasks by QA. Here, you will need to plan the entire QA process setup and use extensive documentations to target repetitive tasks. These repetitive tasks can be anything like creating simulations, testing environments, maintaining logs of user interactions, and others.

Step2: Testing Requirements

Enterprise software has massive features that scale over different departments and assets of an organization. So, the testing requirements may differ from off-the-shelf commercial software. Before the integration of RPA bots, it is essential to identify the prerequisites of the testing suite.

Step3: Prioritizing

ESAs have many components that you will deploy, but it is necessary to identify the critical elements that will have the highest impact due to testing automation. So, instead of testing the entire process, you can minimize testing resources through targeted tests on prioritized components.

Step4: End-to-End Scripting

With RPA, you can enable the end-to-end scripting of testing mechanisms by integrating different open-source tools like Selenium, Katalon, Appium, and others. RPA bots can automate integrations among these tools by programmable APIs or Application Programming Interface. Enterprises can leverage the programming capabilities of the in-house development team or technical partners to create APIs.

Step5: Single Automation Script

RPA bots for software testing will need data patterns for training modules. You will need a single automation script that bots can use to execute and validate the impacted systems.

Step6: Reusable Scripts

Enterprises can reuse single automation scripts for higher environments with RPA bots. You can use these scripts for System Integration Testing(SIT) and User Acceptance Testing(UAT).

Integrating RPA into your ESA will need accurate testing data, accurate executions, and bot script validation. For example, you are creating an ERP software for a conglomerate with different firms working in several business domains. Now, the nature of employment, remuneration levels, and employees’ data patterns will vary across firms. For the execution of RPA bots for testing such an ESA, you will need a strategic plan.

Planning of the RPA based testing begins with data creation. Whether you need to create end-to-end scripts or a single automation script, data becomes quintessential. Sourcing data from different layers of enterprise structure can help with vital data.

Image Source: Robotic Process Automation and Quality Assurance – A Perspective (infosys.com)

Next, you will need trigger scripts that will execute bots for testing automation. Here, API codes can work as triggers and execute bots according to the organization’s priority. The testing automation will follow a process flow defined by the enterprise.

According to the process flow, RPA bots execute the automation scripts and render a test report for validations. Here, the data created before the tests from several sources can be compared with the test report to identify errors.

Conclusion:

RPA scripting needs skilled developers and experts, which may be one factor you will have to consider while integrating it with your ESA testing. The accuracy of testing automation will be higher than any other approach, which is why choosing RPA makes sense. Going forward, ESAs will see RPA integrations not only in the testing process but also in the development approach.