DEV Community: Khiem Phan

From Estimates to Real Hardware Refresh Budget Forecasts

Khiem Phan — Thu, 16 Jul 2026 06:16:14 +0000

Hardware Refresh Budget Forecasting is usually challenging because the data behind the budget is scattered across teams and records.

Every year, someone asks “How much will we need to spend on new laptops next year?” And too often, the answer is still a guess. It is based on last year’s number, a feeling, or whichever spreadsheet still opens without errors.

This article explains what hardware refresh budget forecasting is, why it is often difficult, and how AssetLoom helps teams build a clearer refresh plan.

1. What Is Hardware Refresh Budget Forecasting?

Hardware refresh budget forecasting in ITAM is the process of using asset usage data to estimate which hardware assets should be replaced in the next budget cycle.

Instead of saying, “**We need to replace around 100 laptops next year,**” teams can build a forecast from real records. For example, they may find that 86 laptops will pass the company’s 4-year refresh policy. Among them, 60 are already fully depreciated, and 25 have warranties ending soon.

This gives team leaders a clearer way to plan future hardware spending. With the right asset data, teams can answer questions such as:

Which assets are due for refresh next year?
How much will replacement cost?
Which departments will need the largest budget?
Which devices can be extended, repaired, or delayed?
What is the expected value of assets reaching end-of-life?

2. Why Hardware Refresh Budgeting Is Often Guesswork

Refresh budgets are not usually wrong because teams do not plan. They are wrong because the data behind the plan is scattered, outdated, or incomplete.

Every Team Has Part of the Picture

In practice, bringing all of this data together is not always easy. Lifecycle status may live in one tool. Depreciation may be tracked in a finance spreadsheet. Warranty dates may sit in a vendor portal. Department ownership may come from HR records, or may not be clearly tracked at all.

When teams need to match this information by hand across hundreds of devices, refresh planning becomes slow and unreliable. This is often where the forecast starts to fall apart before the budget is even built.

The cost of this shows up later, when the budget is presented. IT may ask for funding to replace 100 laptops. But finance may see a different number in the depreciation records. At that point, nobody can say for sure which number is correct. Instead of approving the budget, leadership asks for more time to 'double-check the numbers.

Spreadsheets Only Tell Part Of The Story

Spreadsheets can help teams list assets, but they often miss the full context behind each device. A laptop may look old based on its purchase date, but it may still be under warranty, or not yet fully depreciated.

When teams only rely on spreadsheets, refresh planning becomes less accurate. The budget is often estimated from last year’s spending, device counts, or manual checks instead of real usage data.

This is how budgets end up too high or too low. A team may flag 100 laptops for replacement based on age alone. But 25 of those laptops may still be under warranty. That's budget spent on devices that didn't need it, meaning less left over for the ones that actually did.

3. Key Data Needed For Forecasting

So, what data should be used to forecast a hardware refresh budget more accurately?

To move beyond estimates, teams need data that shows the asset age, condition, financial value, and ownership on the same page.

Asset Lifecycle Stage

Lifecycle status shows where each asset currently stands. It helps teams understand which assets should be included in the refresh forecast and which should not.

For example, devices that are in use may need to be reviewed for replacement, while devices in stock may be reused before buying new ones. Assets in maintenance may require attention if repair costs are increasing, and retired assets should be excluded from future planning.

Purchase Date

Purchase date helps teams calculate how long each asset has been used. This is important for identifying devices that are approaching the company’s refresh policy. By reviewing asset age, teams can estimate which devices may need replacement in the next budget cycle and plan the refresh budget earlier.

Depreciation Value

Depreciation data shows how much financial value remains in each asset. A fully depreciated device is easier to justify replacing; one with significant remaining value may be worth repairing or delaying instead.

Warranty Coverage

Warranty information helps teams compare replacement costs with repair or support options. If a device is old and its warranty is ending soon, replacing it may be more cost-effective than extending support.

Department and Assignee Data

Department and assignee data shows who is using each asset and where the replacement cost will happen. This helps teams forecast the budget by department, location, or business unit instead of only seeing one total number.

4. How AssetLoom Helps With Hardware Refresh Budget Forecasting

When these data points are connected, teams can move from a rough replacement estimate to a refresh forecast that is easier to explain, review, and approve.

AssetLoom helps bring these details together so teams can monitor refresh signals and make final budget decisions from one place.

Step 1: Standardize Asset Models And Depreciation Rules

Before uploading the asset list, it is recommended to prepare the depreciation methods and asset models in AssetLoom first.

Depreciation methods define how asset value is calculated over time, such as a 5-year depreciation period. Asset models help standardize the details for each type of hardware, such as Dell XPS, Dell Vostro, etc.

In AssetLoom, depreciation is configured at the asset model level, not separately for each individual asset. This means teams need to create the depreciation rule first, then link that rule to the correct asset model.

By setting these up in advance, uploaded assets can be mapped to the correct model and depreciation rule from the beginning. This makes the imported asset data cleaner and helps teams calculate lifecycle value, remaining value, and refresh timing more accurately.

Step 2: Import Asset List

Other asset details, such as purchase date, warranty information, etc, can be filled in the CSV file and imported together with the asset list. During the import process, AssetLoom allows teams to map the columns in the CSV file to the correct asset fields.

All the necessary asset data is centralized in one place instead of being scattered across spreadsheets, records, and files. This makes it easier to track asset information and use it later for reports, dashboards, and refresh budget planning.

Step 3: Track Results With Reports And Dashboards

After the asset list is imported, you can use AssetLoom reports and dashboard widgets to monitor refresh needs across the hardware fleet.

AssetLoom provides report templates that support both financial tracking and asset usage review. You can make use of them to track assets reaching end of life, assets with expiring warranty, etc.

Teams can also use dashboard widgets to give leadership a quick view of important refresh indicators without opening separate reports. From one place, they can monitor asset age, warranty status, total asset value, assets under maintenance, and asset distribution.

By observing trends in the dashboard, you can quickly see where refresh costs may come from. For example, an asset age widget can show how many devices are moving into older age groups. A warranty overview can highlight devices that may soon lose coverage. A total asset value widget can also help compare purchase cost with current book value.

Step 4: Review and Finalize Decisions

After teams identify the assets that may need replacement, the next step is to review the forecast and decide what should happen next.

Not every old device needs to be replaced immediately. Some assets may still be in good condition, still under warranty, or suitable for lower-priority users. Others may need to be replaced sooner because they are fully depreciated, out of warranty, or frequently repaired.

By reviewing the data together, IT, finance, and leadership can decide which assets should be replaced, which can be repaired, and which can stay in use longer.

Final Thoughts

Hardware refresh planning should not rely on last year’s spending or rough device counts.

With AssetLoom, teams can centralize asset records, connect depreciation rules, import key t details, and track indicators through reports and dashboards.

FAQs

1. How often should a company refresh its hardware assets?

It depends on the asset type, usage level, and business needs. Many companies review laptops every 3 to 4 years, while monitors, desktops, and accessories may have longer lifecycles. The best approach is to set refresh rules by asset type instead of using one timeline for everything.

2. Should fully depreciated assets always be replaced?

Not always. A fully depreciated asset may be easier to justify for replacement, but it can still be usable. Teams should also consider condition, performance, warranty status, repair history, and whether the device still supports the user’s work.

3. What is the risk of delaying a hardware refresh?

Delaying refresh can save budget in the short term, but it may increase support costs later. Older devices may have more repairs, weaker performance, expired warranties, or security limitations. The goal is not to replace everything early, but to know which delays are safe and which create risk.

4. Who should be involved in hardware refresh planning?

IT should not plan the refresh budget alone. Finance should be involved to review depreciation and budget timing. Department leaders should help confirm business needs, hiring plans, and priority users. This makes the final budget easier to explain and approve.

5. What is the difference between hardware refresh planning and hardware refresh budgeting?

Hardware refresh planning focuses on which assets should be replaced and when. Hardware refresh budgeting turns that plan into estimated costs, budget owners, and approval decisions. A good process connects both, so replacement needs are tied to real financial planning.

Starting from July 2026, AssetLoom is open for early access. Try it for free and manage unlimited assets until the end of 2026.

QR Code Inventory Check-In/Check-Out For IT Assets

Khiem Phan — Mon, 13 Jul 2026 11:05:12 +0000

QR Code Inventory Check-In/Check-Out helps IT teams manage asset handovers faster and with fewer manual errors. Instead of searching spreadsheets, typing serial numbers, or updating records by hand, teams can simply scan a QR code to access the correct asset record instantly.

In this article, you can see how QR code inventory check-in/check-out works, the problems it solves, and how to implement it with AssetLoom.

1. How Teams Often Check In & Check Out Assets

For many teams, asset check-in and check-out still depend on a manual process. When assets are issued to an employee, the help desk first finds the asset record and confirms the serial number . Then, they need to update the assignee, change the asset status, and record the new location.

The same thing happens when an asset is returned. The team finds the record again, updates the asset back to inventory, notes its condition, and decides what happens next.

In smaller teams, this process may happen through spreadsheets, shared forms, emails, or help desk notes. It may work at first, but as more assets move between users, manual tracking becomes harder to keep accurate.

Discover: What is An Asset Check-In/Check-Out System?

2. Common Problems With The Manual Check-In/Check-Out Process

As this routine repeats across new hires, device returns, replacements, and repairs, small manual steps start to add up. What looks like a simple update can become a source of delay, confusion, and inaccurate records.

Slow Asset Handovers

When employees come to the help desk for assets, the team may need to find the device in storage. Then they need to match it with the spreadsheet record and assign it to the employee.

This process requires teams to manually compare details in spreadsheets, verify asset location, and update records one by one. Repeating these steps for every asset can create queues during busy periods such as new hire onboarding or device refreshes.

Higher Risk Of Data Entry Errors

Manual typing can lead to wrong serial numbers, incorrect assignees, duplicate records, or outdated asset statuses. For example, teams may assign the wrong laptop record or mark a returned device as available too early.

These small errors make asset records less reliable. Teams may need to double-check devices manually before every checkout, return, or inventory audit.

Limited Visibility Into Asset Ownership

Over time, it becomes harder to answer a simple question: which employee has which asset? Devices may be loaned out, reassigned, or returned without the update being recorded properly. Due to outdated records, teams may struggle to track missing devices, follow up on damaged assets, or recall for offboarding purposes.

Without clear ownership records, it becomes difficult to confirm responsibility when assets are missing, damaged, or not returned during offboarding. Teams may know the devices exist in the inventory, but not be sure who is currently responsible for them.

3. How QR Code Scanning Helps With Asset Check-in & Check-out

Instead of treating every asset movement as a manual data-entry task, QR code scanning gives teams a faster way to open the right record and update it at the point of action.

Reduce Manual Effort

With QR code scanning, teams can scan the asset label to open the correct record instantly. Instead of searching spreadsheets, typing serial numbers, or checking multiple fields by hand, the help desk can move directly to the check-in or check-out action.

In that way, it turns asset handovers from a record-searching task into a quick scan-and-update workflow. As a result, teams can move through checkouts and returns with less manual effort.

Improve Record Accuracy

QR code scanning helps teams work from the correct asset record from the start. By scanning the attached QR label, teams open the asset record linked to that specific physical item. That means the record teams see during a reassignment or repair reflects the asset's real status, not last quarter's spreadsheet entry.

When devices need to be reassigned or sent for repair, the record is more likely to show its real status. When they need to be recalled during offboarding or verified during an audit, teams can also see who owns them and where they should be.

Make Asset Information Easy to Access

QR code scanning makes it easier for teams to view asset details. Instead of searching or filtering by serial number, they can scan the QR label and open the asset information directly.

This is useful when teams need to check who owns the asset or where it is located. They can also confirm its current status, such as available, in use, returned, or waiting for maintenance.

4. How AssetLoom Supports QR Code Inventory Workflows

AssetLoom helps teams bring QR code scanning into their daily asset workflows. From generating QR codes to scanning assets for check-in, check-out, or record updates, teams can manage the process directly within a single ITAM platform.

Step 1: Prepare Your Asset List

First, you need a clean asset list in AssetLoom. This includes basic details such as asset name, category, serial number, asset tag, and other related information.

AssetLoom allows you to add assets manually or import them in bulk with a CSV file. This creates the foundation for a centralized platform where teams can manage asset details before moving into QR code tracking.

Asset tags are important because they act as the unique identifier for each asset. In AssetLoom, you can enable auto-generated asset tags when creating assets manually or importing them in bulk. This helps keep asset records consistent, avoids duplicate identifiers, and removes the risk of assigning the wrong device.

Step 2: Generate QR Codes

Once your asset list is ready, the next step is to generate QR codes for them. You can select one or multiple assets and generate QR codes in bulk, making it easy to prepare labels for an entire inventory.

AssetLoom also lets you customize the QR codes before exporting them. You can choose the QR code size, error correction level, and displayed information. Now, your team can create labels that fit both their physical assets and internal identification needs.

Step 3: Label QR Codes To Your Assets

Once the QR codes are printed, you can attach them to the corresponding physical assets. After every asset has its QR code attached, your inventory is ready for quick scanning and real-time updates in AssetLoom.

Step 4: Scan Assets To Check In, Check Out, Or Update Records

All the setup is complete. You can now scan the QR label to check assets in, check them out, or update asset records. The scan opens the asset information tab directly, so the help desk can quickly view and update details without the manual searching that slows down handovers.

Every change is automatically recorded in the asset's history, giving teams a complete audit trail of who made the update, what changed, and when it happened. Questions like 'who currently has this laptop' or 'was this device returned' are always answerable. You can maintain accurate records and provide full visibility into every asset movement throughout its lifecycle.

Final Thoughts

Manual asset check-in and check-out may work for small inventories, but it becomes increasingly difficult to manage as more devices move between employees, locations, and departments. QR code scanning simplifies the process by reducing manual effort, improving record accuracy, and making asset information instantly accessible.

Ready to simplify asset check-in and check-out? Start managing QR code inventory workflows with AssetLoom.

New Hire Onboarding Workflow: From Request to Ready-To-Use Packages

Khiem Phan — Thu, 09 Jul 2026 07:03:27 +0000

A new hire onboarding workflow helps teams prepare the resources a new employee needs before their first day. But when requests, approvals, records, and assignments are managed through scattered messages and spreadsheets, the process can quickly break down.

In this article, we’ll explore how AssetLoom onboarding kits help teams standardize and automate new hire preparation.

1. New Hire Onboarding Workflow

What Is A New Hire Onboarding Workflow?

A new hire onboarding workflow is the process of preparing resources for new employees before their first day, so they can start work smoothly. In ITAM, this refers to the journey from the initial request to the handover of IT assets, accessories, licenses, and other resources.

For example, when a new employee joins the company, the operations team may need to prepare and assign a laptop, headset, and software access before their first day.

A Typical Workflow In Most Workplaces

In many workplaces, the process begins like:

A manager/ leader sends a request through email, Teams, Slack, or chat. The message may include the employee’s name, start date, role, and the equipment they need.
The IT operations team checks to see which ones are available and whether the requested device can be assigned.
If approval is required, the request may be confirmed in a separate email thread, chat message, or meeting discussion.
Once approved, the team selects a suitable laptop or device and prepares it for the new hire.
After the handover, the asset assignment is manually updated in a spreadsheet or asset record.

2. Why The New Hire Onboarding Process Is Broken

Onboarding should be simple, but the current preparation flow depends too much on scattered communication and manual tracking. As a result, the workflow can easily break down when the requests scale.

Scattered Requests

New-hire requests may come from email, Slack, or direct messages, making them easy to miss. One request can be sent through chat, another may be via email, while approval may happen in a separate conversation.

As more requests come in, the onboarding workflow becomes difficult to prioritize and track. Teams may know that a new hire needs equipment, but they may not have a clear view of who requested it, whether it has been approved, or what still needs to be prepared.

Outdated Records

Many teams still manage asset stock in spreadsheets, where updates depend on manual input from different people. Over time, these records can become outdated and fail to reflect the latest asset status.

This creates a gap between what the record shows and what the operations team can actually use. A laptop may still be marked as available even though it has already been reserved for another new hire. Then, teams often spend extra time double-checking device availability before every onboarding request.

The process becomes even more complicated when onboarding packages include more than just core assets. A new hire may also need accessories and software licenses to start working properly. However, these items are often tracked separately from the main asset record. As a result, the workload for operations teams increases as the company scales.

Limited Automation

In a manual workflow, every step depends on someone remembering to take action, follow up, and update the right record. The operations team may need to check inventory, confirm approval, reserve resources, update spreadsheets, and record the final handover manually.

Over time, these repeated tasks can quickly create delays and inconsistencies. The impact becomes even clearer when teams may struggle to answer basic questions such as:

Who approved the request?
Which laptop was assigned?
When was it handed over?
What accessories were included?
Which software licenses were allocated?

3. What Can Be Enhanced For A Better Employee Onboarding Flow

So, what should a better employee onboarding flow look like?

A better onboarding flow starts with giving operations one clear place to manage the full process. Instead of separate sheets, teams should be able to see requests, available stock, and resource status in one centralized platform.

The onboarding package should also be managed as a complete setup, not just individual resource assignment. When the onboarding bundle is prepared together, teams can reduce repeated assignments and delays on the new hire’s first day.

Automation can further improve the process by removing repetitive manual tracking. For example, teams can reserve available resources, update status after assignment, and keep a clear record of the handover automatically.

4. AssetLoom Onboarding Kits

This is where AssetLoom onboarding kits can help. Teams can create predefined kits that group assets, accessories, consumables, and software licenses together, then assign the full setup to a new employee in a structured workflow.

Let’s explore how to set up the onboarding kits in AssetLoom:

Step 1: Add Resources to AssetLoom

First, you need to add all resources that may be included in a new hire onboarding package. This includes core assets such as laptops, tablets, or mobile phones, as well as accessories, consumables, and software licenses.

For teams that already manage inventory in spreadsheets, AssetLoom allows bulk import from CSV files. This makes it easier to migrate existing asset records into AssetLoom without recreating everything manually.

Once imported, teams can view each resource with details such as available quantity, current status, location, and ownership information. This creates a clear inventory foundation before onboarding kits are created.

Instead of checking spreadsheets to guess what might be available, you can see which resources are ready to be assigned.

Step 2: Create Predefined Kits

Next, you can create predefined onboarding kits for different roles, departments, or work setups. Each kit is created by adding existing assets, accessories, software licenses, and consumables into one reusable package.

For example, a New Hire Onboarding Kit may include a laptop, keyboard, mouse, Jira licenses, and some office stationery. You can also define the quantity of each item in the kit, so every new employee receives a consistent setup.

This helps you avoid preparing each item one by one for every new hire. Instead, you can simply select the right onboarding kit to check the availability of items and assign it to the new employees on their first day.

Step 3: Set Up Auto Checkout For Onboarding Kit

By default, you can check out a predefined kit to any user whenever you want. This is useful when you want to manually assign a specific kit to an employee on demand.

AssetLoom can also automate this process. By selecting a predefined kit as an onboarding kit, the resources inside the kit can be automatically assigned to a new member when they join the site.

Because the resources for new hires are already standardized in the onboarding kit, you no longer need to go back and forth with requesters to confirm every item. You only need to double-check available stock, and AssetLoom can handle the checkout process automatically.

Learn more: Automation in ITAM

Step 4: Invite New Onboarding Member

The final step is to invite the new employee to AssetLoom. Once the member joins the site, AssetLoom can automatically assign the onboarding kit that was selected in the previous step.

All resources included in the kit, such as assets, accessories, licenses, and consumables, are connected to the new member’s profile. This gives you a clear view of what has been assigned and when they are assigned to the employees.

Final Thoughts

New hire onboarding is more than assigning a single laptop. It requires clear requests, accurate stock records, complete resource packages, and reliable handover tracking.

With AssetLoom onboarding kits, teams can standardize what each new hire receives, automate checkout, and keep every resource under control.

Data Granularity: The Hidden Factor Behind AI Testing Quality

Khiem Phan — Mon, 15 Dec 2025 10:23:23 +0000

Data granularity plays a crucial role in how we understand, evaluate, and improve AI systems. In AI testing, granularity isn’t just a data feature but directly impacts how accurately we measure model performance.

In this article, we’ll explore why data granularity matters in AI testing, the different levels of granularity you can use, and how to choose the right level for each stage of development. We’ll also highlight the common mistakes and how to avoid them, helping you create more accurate AI test strategies.

1. Data Granularity In AI Testing

Data granularity refers to the level of detail contained within a dataset. It can range from detailed data (individual user actions, historical logs, etc) to general data (overall trends, summarized metrics, etc).

In traditional analytics, granularity determines how deeply you can analyze trends. But in AI testing, granularity plays an even more critical role. It defines how precisely you can evaluate AI behaviors, uncover edge cases, and trace failures back to their root cause.

2. How Important Data Granularity Is In AI Testing

When you're testing an AI system, the level of detail in your data doesn’t just influence the results. It also shapes how well you understand the AI’s behavior in real situations.

Model Comparisons

Two AI systems can only be compared fairly if they’re tested with data at the same level of detail. Otherwise, the comparison becomes biased. For example, you give two AI systems different sets of data and request them to generate new test cases. One is detailed with requirement descriptions, while another is only with a few bullet points and notes. As a result, two models generate different output quality. It is not because one model is smarter, but because it was given more information to work with. This is especially important in some AI testing techniques, such as Pairwise, to ensure the comparisons are objective and meaningful.

Catching Edge Cases

Many AI failures happen in the tiny details. When you only provide the AI with general, simplified information, many edge-case issues never appear during testing. They only surface later, when real users interact with the system in unpredictable ways. The absence of these issues in early tests doesn’t mean they don’t exist. It simply means your data was too oversimplified to reveal them. For example, imagine testing an AI that validates shipping addresses. If you only provide clean, well-formatted examples, the model may look perfectly accurate. But once you include real-world variations (missing apartment numbers, slightly reordered fields, etc), you will see where the model struggles. Detailed data helps expose these weaknesses early, before they become customer-facing issues.

Discover how Agiletest AI-Generator can help you create test cases with detailed test steps now

Efficient Testing

More granularity isn’t always better. Sometimes, highly detailed data creates unnecessary complexity and slows down your testing efforts. For instance, if you’re checking whether an AI can identify the defect trends in a testing report, you don’t need the entire article with every paragraph included. A short summary may be enough to verify the model’s understanding. This can help you save more time and resources in the AI testing process.

3. The Three Levels of Granularity

Now that we understand why granularity matters, the next step is choosing the right level for your testing goals. Not all granularity is the same; too much or too little detail can distort your results or slow down your process.

To make this easier, data granularity can be grouped into three levels: high, intermediate, and low. Each serves a different purpose in AI testing. In the next section, we’ll look at what each level means and when to use it.

High (Fine) Granularity

High (Fine) granularity means your data is extremely detailed. Every action, field, or element is captured and treated individually. For example, they can be the detailed requirement documents, user flows, acceptance criteria, etc. This level is useful when you need to understand exactly how an AI system behaves or where it fails. You can use these data for debugging model errors, testing edge cases, etc.

Intermediate Granularity

Intermediate granularity provides a balanced level of detail. The data isn’t overly complex, but it includes enough information for meaningful analysis. They can be requirement summaries, brief explanations, key notes, etc. This is the most commonly used level in AI testing because it offers clarity without overwhelming the system. Those data can be applied in comparing models, evaluating model performance, etc.

Low (Course) Granularity

Low (Course) granularity uses broad or summarized data. It removes fine details and focuses on the big picture. It comes in handy for quick checks or high-level evaluations where detail isn’t necessary. Some examples of these data are pass/fail results, testing outcomes documents, etc.

4. How To Choose The Proper Granularity

Choosing the right granularity is not about picking “more detail” or “less detail”. The key is selecting the level that aligns with your testing purpose. The ideal granularity depends on what you want to learn and how deeply you need to evaluate AI output.

Choose high granularity when you're in the early development stage, where understanding the AI’s behavior in detail is essential. At this stage, small mistakes have a big impact, and every step of the AI’s reasoning needs to be visible.

Intermediate granularity becomes most valuable during the pre-production stage, where you need reliable, consistent inputs to test how well the AI performs under typical real-world conditions. It gives the AI enough context to perform meaningful tasks without overwhelming it or slowing down the testing process.

You should select low granularity once the AI reaches the production stage, where you primarily want quick insights rather than deep analysis. At this point, you’re looking for broad patterns and indicators that tell you whether the system is generally healthy. This level is best for broad assessments rather than deep evaluation.

5. Common Mistakes In Practice

Even with a solid understanding of granularity, many teams struggle to apply it effectively in real AI testing workflows. There are some common mistakes teams encounter when testing AI.

Using One Granularity Level for Everything

A frequent mistake is using only a single level of granularity throughout the entire testing process. Teams often rely on whatever data they used at the start and apply it to every stage, from development to post-production.

Different stages require different levels of detail. Early development needs detailed data to uncover reasoning errors, while production monitoring benefits more from general summaries that highlight trends. Using one granularity everywhere results in either over-testing simple tasks or under-testing critical scenarios.

Ignoring Granularity When Investigating Failures

When an AI system produces an unexpected or incorrect output, teams often focus solely on the result. They overlook a key question: Was the input at the right level of detail?

Sometimes, it is not about the models you are using. Instead, there could be problems with the data you input for AI. In other words, investigating output without reviewing input is like troubleshooting a device without checking whether it was plugged in.

Assuming More Data Automatically Means Better Testing

Another common misconception is believing that providing more details will always improve testing outcomes. While detail is valuable in the right scenarios, it’s not a universal solution. Aforementioned, too much data could slow down the AI performance and testing process. In many cases, a concise and focused input produces a more reliable test result than a large, detailed one.

Final thoughts

Choosing the right level of data granularity is essential for meaningful and reliable AI testing. The level of detail you provide shapes how accurately you can evaluate model behavior and uncover potential issues. In the end, effective AI testing isn’t about using more data; it’s about using the right data at the right stage. By adjusting granularity thoughtfully, you can achieve clearer insights, faster testing cycles, and more dependable AI performance.

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AI Testing Evaluators for Scalable, Reliable QA

Khiem Phan — Tue, 02 Dec 2025 08:26:14 +0000

AI Testing Evaluators are becoming an essential part of modern software AI Testing processes. While AI can produce output at impressive speed, ensuring that this output is accurate, complete, and aligned with real product behavior is a new challenge for QA teams. This is exactly where AI Testing Evaluators step in.

In this article, we’ll explore what AI Testing Evaluators are, the key characteristics that make them effective, the four main evaluation methods, and how to decide which approach fits your testing workflow

1. What Are AI Testing Evaluators?

AI Testing Evaluators are frameworks, tools, or methods designed to measure the quality of AI-generated testing artifacts. Instead of relying solely on manual review, evaluators help teams assess the AI’s output with clear criteria and benchmarks. In simple terms, they act as a quality gate, helping teams decide to accept, improve, or discard the output.

2. Key Characteristics of AI Testing Evaluators

Some main characteristics that those evaluators should have include:

Consistency That Reduces Human Bias

AI Testing Evaluators apply standardized criteria every time, ensuring that the quality no longer depends on who reviewed it. This consistency is critical as AI-generated artifacts scale, teams get uniform quality control without the variability of human judgment.

For example, with the same work, human reviewers give widely different scores depending on experience level. Meanwhile, the AI evaluator follows a defined set of scoring criteria and applies them consistently. This reduces subjective bias and ensures uniform quality across all evaluations.

Deep Understanding of Requirements and Intent

Good evaluators don’t just check whether the output “seems fine.” They understand what the feature is supposed to do and what the user expects. Because of this, they can catch small but important mistakes that basic checklists would overlook.

For instance, imagine the requirement says: “The system must block the user after 5 failed login attempts”. If the AI generates a test case that blocks the user after only 3 attempts, a strong evaluator will immediately detect the mismatch. It understands the original requirement and can point out that the test does not reflect the correct behavior.

Holistic Quality Measurement Across Multiple Dimensions

Instead of a binary pass/fail, evaluators assess tests from multiple angles: clarity, logical flow, completeness, risk coverage, feasibility, and alignment with system behavior. This multidimensional scoring mirrors how an experienced QA engineer thinks, but at a far greater speed and scale.

For illustration, AI can generate a test case “User uploads a profile picture”. The evaluator will check Completeness (any negative test for unsupported test types), logical flow (test step order), and so on. The evaluator not only ensures the AI outputs are right, but it also reviews them from different angles to comprehend the output.

Ability to Scale Without Compromising Quality

AI tools can generate hundreds of tests at once. Evaluators can review all of them in just seconds. This speed allows evaluation to happen automatically, keeping the testing process fast and smooth even as projects grow.

In particular, AI Agents can generate hundreds of test cases within minutes. However, QA/QC teams will need from one to two hours to review all of them. The AI evaluator can process all test cases in minutes, scoring each and highlighting those that need revision.

3. The Four Core AI Evaluation Methods

Now that we understand what makes a strong evaluator, the next step is to look at how these evaluations actually perform. Let’s explore the four main methods used to evaluate AI-generated test outputs.

Heuristic-Based Evaluators

Heuristic Evaluators use predefined rules that come from real testing experience. These rules reflect what testers have learned over time, so they’re practical and human-oriented. While heuristics don’t “think” like humans, they inherit patterns from past human judgment, allowing them to quickly catch common issues such as missing steps, unclear instructions, duplicated content, or incomplete scenarios.

As an illustration, when AI summarizes test execution logs, heuristic evaluators can quickly check for recurring problem patterns. They can include frequently failing tests, repeated error messages, or mismatched timestamps. These are issues that often appear across multiple runs, and heuristics are well-suited to catch them immediately.

Heuristic Evaluators can only check what they’ve been programmed to look for. If the issue falls outside the predefined rules, the evaluator may completely miss it. This makes heuristics fast but not very adaptable when the testing scenario becomes complex or unusual.

Human Evaluators

Human Evaluators involves QA experts reviewing the output directly. Humans bring domain knowledge, intuition, and practical experience that no automated method fully replaces. Humans can interpret business rules, identify edge cases, and spot contextual nuances that AI may miss, especially in complex or high-risk scenarios.

Imagine when a specific feature suddenly shows an unusually high number of failed tests, human evaluators may notice this point. They can investigate, connect it to recent product changes, and uncover the real root cause, which automated systems might ignore.

However, Human review is slow and inconsistent. Two testers might judge the same output differently, and large volumes of AI-generated work can quickly overwhelm a team. This makes human evaluation accurate but not scalable.

LLM-as-Judge Evaluators

LLM-as-Judge Evaluators uses another AI model (like ChatGPT, Gemini, etc) to evaluate the original AI’s output. The “judge” AI reads the test case, understands the requirement, and provides a reasoned assessment or score. Its strength lies in offering human-like judgment at high speed, making it ideal for evaluating large batches of AI-generated tests.

For example, if the AI claims a test failed due to a timeout, the judge model may detect that the real cause was a backend 500 error. This type of context-aware reasoning allows LLM judges to validate not just what the AI produced, but whether the reasoning behind it is sound.

On the other hand, LLM judges can occasionally misinterpret the context or produce confident-sounding but incorrect conclusions. Their results may also vary depending on how the question is phrased (the “prompt”). Therefore, they need careful guidance and human oversight to ensure their judgment is reliable, especially for domain-specific testing tasks.

Pairwise Evaluators

Pairwise Evaluators compares two AI-generated outputs and selects which one is better. Instead of scoring each test separately, it simply chooses the better option. This makes the process simpler, more reliable, and effective when multiple AI agents produce different versions of the requested outcomes.

With the same task to generate a summary of a test cycle, one AI might list failures but miss the root cause patterns, while the other identifies that most issues occurred after a recent API update. Pairwise evaluation helps surface the more useful version without requiring full scoring of each.

One note is that Pairwise evaluators always choose the “better” option, even if both are poor. Since they only pick a winner and don’t explain what’s wrong, they are less useful for improving quality. They help choose between options, but they don’t guide how to fix them.

4. When to Use Each Evaluation Method

Heuristic-Based Evaluators: Use for Quick, High-Volume Checks

Heuristic Evaluators are ideal when you need fast, automated validation on large batches of AI output. Use them when you want to:

Catch common or repeated issues quickly
Validate formatting, completeness, or correctness at a basic level
Filter out low-quality outputs before deeper review
Review test logs or execution summaries for repeating failure patterns

Best for: early screening, daily test runs, bulk AI generation, CI/CD pipelines.

Human Evaluators: Use for Critical, Complex, or Business-Heavy Scenarios

Human Evaluators are essential when accuracy and product context matter most. Use them when:

The feature involves important business logic or compliance
There’s an unusual spike in failures in one area
You need to confirm whether the AI’s reasoning matches real product behavior
The test output influences a decision with high risk (release/no release)

Best for: root-cause investigation, high-risk modules, business-rule validation, exploratory testing.

LLM-as-Judge Evaluators: Use When You Need Scale and Context Awareness

LLM-as-Judge Evaluators shine when you want something more intelligent than heuristics but faster and more scalable than human review. Use them when:

You need to evaluate large numbers of AI-generated outputs with deeper reasoning
You want a human-like assessment without human time investment
The AI output includes explanations, summaries, or logic that needs validation
You need consistency across dozens or hundreds of reviews

Best for: reasoning checks, log interpretation, test plan evaluations, and verifying the correctness of AI explanations.

Pairwise Evaluators: Use When Comparing Multiple AI Outputs

Pairwise Evaluators are perfect when different AI agents, prompts, or models produce multiple versions of an output. Use them when:

You want the best option out of several AI-generated results
You want to sort for the best “quality selection” across many outputs without detailed scoring
You want a fast comparison without full evaluation overhead

Best for: model comparison, multi-agent outputs, prompt tuning, batch selection.

Final thoughts

AI Testing Evaluators play a crucial role in ensuring that AI-generated outputs are not only fast but also accurate and reliable. Each method offers unique strengths: heuristics for quick checks, humans for deep insight, LLM-as-Judge for scalable reasoning, and pairwise evaluation for selecting the best among multiple options.

By combining these approaches, teams can maintain high quality while embracing AI-driven testing at scale. Evaluators help turn AI from a productivity booster into a trustworthy part of the QA process, ensuring confidence in every release.

AgileTest is a Jira Test Management tool that utilizes AI to help you generate test cases effectively. Try it now!

Xray Test Management for Jira: To What Extent Can AgileTest Be An Affordable Alternative?

Khiem Phan — Fri, 28 Nov 2025 18:04:54 +0000

Xray Test Management for Jira is a widely used tool for test planning, execution, and reporting, offering a comprehensive set of features to manage testing workflows directly within Jira. It’s designed for teams that need full capabilities and extensive integration with Jira, making it a popular choice for larger organizations.

AgileTest by DevSamurai is also an effective test management solution that integrates with Jira. In this article, we will go through some key features in which AgileTest can be an affordable substitute for Xray Test Management for Jira.

1. Test Management

Both Xray and AgileTest support you and your team to create, manage, execute test cases, and track test results. Let’s discover how each app works.

Create and organize test cases

AgileTest

In AgileTest, there is a separate section named “Test Case”. Here’s a breakdown of the key features you can access:

Navigation path: you can easily notice and navigate directly to see your existing test cases. With a straightforward path, you can access your entire test case library without unnecessary clicks.
Create test cases: you could click on the button “+ Test Case” to create a test case manually. If you already have a list of test cases, you can hit the import button nearby to upload your prepared sheets.
Organize test cases: you can drag and drop test cases into folders to categorize them into features, requirements, etc that match your preferences.

⇒ Every action you need to perform with test cases in your projects is centralized in one place, which then makes AgileTest intuitive and UI-friendly in your daily workflow.

Source: AgileTest App

XRay

In XRay, test cases are stored in the Test Repository area.

Source: XRay App

Navigation path: you need to choose “Test Repository” → Test Folders → Test cases to see details.
Create new test cases: the test cases in Xray are named with Test. You can add a new test case manually by clicking “Create Test”. However, if you need to import test cases, you have to move back to the menu sidebar and choose the “Test Case Importer” to upload your tests.
Organize test cases: you can drag and drop test cases into folders for tracking and management purposes. Since one test case can only be stored in one folder, Xray allows you to categorize a test case in different Test Sets, one of its features that functions as a collection of tests.

⇒ Xray Test Management for Jira might require first-time users to spend some time getting familiar with the interface and technical naming. Despite this learning curve, Xray’s features make it an effective tool for large teams that need advanced test case management within Jira.

Source: Xray App

Execute test case

AgileTest

Before test executions, you can create a Test Plan to manage test results. The Plan helps you:

Define test timeline: in each plan, there is a specific due date and end date. This help you track your test progress and ensure that your test execution is on the right track.
Manage test execution: you can create and add as many test cases to each test execution as possible. Here, you will retrieve the test cases you have grouped into folders to ensure all necessary test cases are fully covered.

Source: AgileTest App

During each execution, you and your team can:

Mark test status: you can assign a status to every test step. Then, AgileTest will calculate the test status of the overall test case. This helps you locate exactly which steps should be focused on in the next rerun.
Add findings and comments: you can add attachments, notes, and comments during the execution sessions
Link defects: if a bug is discovered, you can easily create a new Jira defect link or associate an existing one, enabling your tracking of both test cases and bugs in Jira.

Source: AgileTest App

⇒ AgileTest’s Test Plan feature allows you to define timelines, manage test cases, and track progress easily. During execution, you can assign statuses to individual steps, add findings, and link defects directly to Jira, making it a comprehensive yet simple solution for smaller teams or those seeking intuitive test management.

XRay

XRay’s Test Plan functions as a collection of test cases you plan to conduct for each execution session. However, there is no defined timeline to track for these plans.

Define test timeline: there is no timeline feature to track for these plans.
Manage test execution: you can add different test cases to a test plan. On the main page, you can see the progress for each of them.

Source: Xray App

You can conduct some actions during execution, including:

Mark test status: you can assign a status to every test case. Xray will display the number of test cases for percentage tracking progress.
Add findings and comments: in case you want to leave any findings, you have to add attachments and, description on the Jira ticket of the test cases.
Link defects: if you want to link a test case with a defect ticket, you need to click on the specific test cases → Link issues → Create Defects.

Source: Xray App

⇒ Xray provides a more structured approach to test management with its Test Plan feature, allowing you to organize test cases for execution. While it lacks a built-in timeline feature and linking defects or adding findings requires navigating to Jira tickets, XRay can still be a good option for teams who are not looking for a more integrated workflow.

Report test result

AgileTest

In the Report features, AgileTest offers three main types of reports:

Test Coverage: display how your test cases covered the predefined requirements
Test Traceability: show the relationships among your test cases, test plans,and test executions with requirements & defects.
Defect Summary: collect all created defects linked with your test cases during executions.

Source: AgileTest App

⇒ AgileTest reports currently are designed to give you a clear overview of your test coverage and traceability despite a limited report types.

Xray

After accessing the Reporting Center, you will see 9 types of reports for 3 main purposes:

Organization & Planning:
- Tests List: gather all test cases you have created
- Tests Sets List: display grouped collections of your test cases
- Tests Plans List: show all your test plans in the testing projects
Execution
- Tests Executions List: list your test executions with test status and owners' info
- Tests Runs List: summarize your individual executed test cases with details
- Test Plan Metric: overview of the test status, number of defects found, types of test cases in each test plan
Coverage & Analytics:
- Test Coverage: indicate which requirements are covered with test cases
- Requirement Traceability: show how requirements are linked with test cases, executions, and defects
- Defect Traceability: display defects and the link among them with test cases and requirements

Source: Xray App

⇒ Xray’s extensive reporting features provide deep insights into every aspect of your test management process, making it a great choice for larger teams that need detailed data on your testing projects.

2. Plan and Pricing

AgileTest currently has one pricing plan:

Has a 30-day free trial
Free for teams under 10 members
USD 1.50 each user per month when teams are from 11 to 100 members.

Xray has two pricing plans: Standard and Advanced. The Advanced package inherits all features in the Standard package, plus a storage update from 100 to 250 MB & an increase in API calls from 60 to 100 per minute.

Has a 30-day free trial
Standard and Advanced Plans have a flat fee of USD 10.00 and USD 12.00 for teams of under 10 members each month
USD 6.33 and USD 7.60 each user per month when team sizes from 11 to 100 members for the Standard and Advanced package respectively.

Final thoughts

Xray Test Management for Jira is better suited for larger teams with more complex testing workflows. It provides detailed reporting, advanced test management, and deeper Jira integration, but comes with a steeper learning curve. Meanwhile, AgileTest is a great choice for smaller teams or those looking for a simple, intuitive test management solution. It offers core features like test case creation, execution tracking, and reporting at an affordable price, making it ideal for teams that don’t need advanced customization or complex analytics. Ultimately, your choice should be based on your team's size, workflow complexity, and the level of reporting and customization you need.

AgileTest is a Jira Test Management tool that utilizes AI to help you generate test cases effectively. Try it now!

Self-corrective Code Generation: A Basic Understanding and Real-life Application

Khiem Phan — Fri, 28 Nov 2025 14:58:01 +0000

Self-corrective Code Generation is an advanced AI approach where code is not only generated but also continuously refined based on feedback and predefined rules. Unlike traditional methods, this process ensures that code meets essential standards for readability, efficiency, maintainability, and compliance with coding guidelines.

This article will explore how AI-generated code can be incomplete without proper validation, the consequences of using untested code, and how self-corrective code generation solves these challenges. We will also discuss advanced techniques, like multi-step agents, to further enhance code quality, followed by practical examples of how this process works.

1. Sources of Incomplete Code

There are two main sources of incomplete code originating from AI’s output.

Limited Contextual Information

AI models can only work with the information they are given. When a prompt lacks clarity, specific constraints, or detailed requirements, the model fills the gaps with assumptions. Even when the requests are vague, AI doesn’t ask clarifying questions. It simply generates what seems “most probable”.

For example, when you ask AI to “prepare test cases for a login function”, AI can generate a list of test cases that may seem technically correct. However, without contextual information, these test cases are incomplete. AI does not include the authentication method your system uses (password-based, OAuth, SSO); what security rules apply (password complexity, rate limiting), etc. At this level, the output only reflects a generic understanding, not the real needs of your system.

One-Pass Generation Constraints

Most AI code generation happens in one direction: the model reads your prompt and immediately returns code. Once the output is generated, the AI doesn’t automatically review it unless you explicitly ask it to. Large language models (LLMs) either do not run or test the code they generate. They rely on patterns from training data to predict what the correct code should look like. As a result, they can produce code that appears valid on the surface but breaks immediately when executed.

For instance, imagine you ask an AI model to “write a function that calculates the total price after applying a discount and tax”.

def calculate_total(price, discount, tax):
    discounted = price - (price * discount)
    total = discounted + (discounted * tax)
    return total

The code seems fine, but the AI never actually tests it. That means it won’t notice issues like incorrect order of operations (tax might be applied before discount in your business logic).

2. What Are the Consequences

So, what could be the effects of these sources of incomplete code?

An Increase in Debugging Time

Incomplete or incorrect code forces developers to spend additional time debugging and rewriting the code to meet the required standards. In fact, 67% of developers have to spend more time revising AI’s output (Harness, 2025). This extra time spent on debugging, coupled with the need for a deeper understanding of context, can quickly add up. Gradually, this issue could outweigh the time-saving benefits AI was originally supposed to offer.

A Likelihood of Hidden Defects Escape from Development

When AI-generated code is not thoroughly checked, there’s a higher likelihood of hidden defects coming to the production environment. According to a study by the IEEE Computer Society, 35% of found bugs are related to incomplete code. As AI tools become more widely adopted in development workflows, this percentage is likely to rise.

A Reduction in Trust and Adoption of AI Tools

If AI-generated code continues to produce errors, teams may lose trust in AI and move back to the traditional approach. The persistent need for manual corrections and validation could undermine the perceived value of AI. Teams would rely more on their own expertise and established processes rather than embracing AI’s potential for automation and efficiency.

3. Self-corrective Code Generation

To minimize these impacts, testing teams need an approach that gets AI to auto-review its output to polish it again and again before finalizing for testers. That’s when Self-corrective Code Generation comes in.

What is Self-corrective Code Generation, in Simple

Self-corrective Code Generation refers to the AI testing process in which AI models generate code and then iteratively refine their output based on predefined rules, feedback, or tests. This is the key difference between this approach and the traditional one. This turns AI from code generation tools into smart assistants, which can improve the output before finalizing it with developers.

How does Self-corrective Code Generation Work

Question: The process begins with a question. This could be a prompt asking the AI to generate code based on a given task or problem description.
Generation (Node): Once the question is inputted, the AI enters the generation phase.
Draft answer: The model generates a response, which is structured as a Pydantic Object. There are three main components:

Preamble: a short note to indicate what the code does and why the code is there.
Imports: a list of tools or libraries needed for the code to function properly is available.
Code: the actual solution to the problem or task you asked AI for.

Import Check (Node): After generating the code, the next step involves checking whether the required imports have been correctly included. This step ensures that the code references all necessary external libraries or modules. If any required imports are missing or incorrectly referenced, the code is flagged as incomplete and fails at this stage. It will go back to the Generation (Node) to edit the Imports section of the Draft answer.
Code Execution Check (Node): Once the imports are verified, the AI moves on to the most critical step: the code execution check. This step involves executing the generated code in a controlled environment to ensure it runs as expected. The logic here is similar to the Import Check (Node):

If the code runs without errors, it passes the check.
If the code fails or encounters an issue, the AI detects the problem and sends the code back to the Generation (Node) to make corrections.

Final Answer: This auto review-and-refine process creates a feedback loop, where the AI continuously checks and improves the code. Only the output that successfully passes all the checks will be finalized and presented as the final solution.

4. Self-corrective Code Generation Using Multistep Agent

An advanced approach to self-corrective code generation uses multi-step agents, which significantly improve the quality of AI-generated code. Unlike traditional methods that review code in a single pass, multi-step agents iterate through multiple stages of feedback and refinement. Here’s how it works:

Iterative Refinement: The code generation process involves multiple steps. After generating the initial code, the agent evaluates it based on predefined rules (like readability, efficiency, and robustness) and unit tests. For example:

import yaml
from jinja2 import Template

class IterativeCodeAgent(ToolCallingAgent):
    def __init__(self, prompt_template: str = None, *args, **kwargs):
        """
        Initialize the IterativeCodeAgent. If no custom prompt template is provided, a default one is used.
        """
        # Use a default prompt template or a provided one
        self.run_prompt = prompt_template or self._load_default_prompt()

        super().__init__(*args, **kwargs)

    def _load_default_prompt(self):
        """
        Loads a default prompt template from a file or predefined string.
        Modify this method if you want to use a hardcoded prompt template instead.
        """
        # For this example, the default prompt template is hardcoded.
        # You can replace this with YAML or another dynamic template if needed.
        return """
        You are a helpful code generation assistant. Based on the given instructions, generate the required code.
        Instructions: {{ instructions }}
        """

    def run_agent(self, task_instructions: str, *args) -> None:
        """
        Run the agent with the provided task instructions. It will generate code based on the template and input instructions.
        """
        # Use Jinja2 to render the template with the task instructions
        prompt = Template(self.run_prompt)
        task = prompt.render(instructions=task_instructions)

        # Pass the generated task to the parent class for execution
        super().run(task, *args)

Code Quality Review: A tool within the agent reviews the code like a "human-like" quality checker, ensuring the code meets key principles such as maintainability, efficiency, and adherence to coding standards. Here is an illustration of how your prompts can be:

prompt_template = """
You are an AI code reviewer. Your task is to analyze the provided code and ensure it meets the following principles:

1. **Readability**:
  - Check if the code is easy to understand, even by someone who didn't write it. Ensure the use of meaningful variable names, consistent formatting, and proper variable and argument typing.

2. **Maintainability**:
  - Evaluate if the code is easy to modify, update, and debug. It should follow coding standards, avoid overly complex logic, and be modular when required.

3. **Efficiency**:
  - Check if the code uses resources effectively. It should minimize execution time and memory usage.

4. **Robustness**:
  - Ensure that the code handles errors appropriately. Look for the use of try-except blocks for risky code blocks and proper error handling.

5. **PEP-8 Compliance**:
  - Check if the code follows the PEP-8 style guide. This includes proper indentation, line length, naming conventions, and other style guidelines.

### **Tasks**:
1. Ensure the output follows the expected output.
2. For each of the principles listed above, analyze whether the code meets its respective requirements.
3. Request any changes in the provided code as part of your feedback in the comments.
4. Do not assume any external documentation when reviewing the code.
5. Provide a summary at the end of your feedback to gather all suggestions.
6. At the very end, return a boolean value:
  - **True** if all principles returned True.
  - **False** if any of them returned False.

### **Expected output example**:

1. **Readability**:
  - The code uses clear and descriptive names for functions and variables.
  - A type hint is missing for the input parameter of the function `run(input_string):`

2. **Maintainability**:
  - The solution is modularized into several functions.
  - Error checking and consistent structure make it easy to modify or extend functionalities.

3. **Efficiency**:
  - Code has been written with optimal structures.
  - The solution is using efficient Python built-in functions.

4. **Robustness**:
  - The code includes appropriate error handling through type checks and try-except blocks.

5. **PEP-8**:
  - The code follows PEP-8 guidelines: proper indentation, spacing, meaningful names, and line lengths.

### **Summary**:
- **Readability**: Add type hinting in the declaration of function `run(input_string)`. Proposed solution: `run(input_string: str) -> None:`
- **Maintainability**: No changes required.
- **Efficiency**: No changes required.
- **Robustness**: No changes required.
- **PEP-8**: No changes required.

### Final Decision:
**False**
"""

Automated Feedback Loop: Based on this review, the agent refines the code, improving it iteratively until it meets the required quality criteria. This feedback loop enhances the AI's ability to produce high-quality, reliable code

Final thoughts

Self-corrective code generation empowers developers by ensuring AI-generated code is continuously reviewed and refined. This results in higher quality, more reliable code with minimal manual intervention. As AI tools evolve, these self-corrective systems will become even more integral to development workflows, helping teams produce faster, better results with confidence.

Jira Test Management Tool

Khiem Phan — Fri, 07 Nov 2025 08:21:13 +0000

Jira Test Management tool plays a vital role in helping teams plan, track, and optimize their testing activities within the Atlassian ecosystem. Jira is a project and issue tracking tool by Atlassian, widely used by software development, IT, and project management teams to plan, track, and deliver work. In a typical testing workflow, teams use Jira to create user stories and tasks, track bugs, and document test results. QA and testers use testing tools to link and manage test cases with related user stories, while developers update issue statuses as fixes are made.

This blog looks at Jira from a Test Management perspective, explores what test management may look like, and introduces AgileTest as a Test Management Tool to optimize the testing lifecycle within Jira.

1. Test Management in Jira

Test Management Tools

While Jira provides a solid foundation for managing issues and user stories, it lacks built-in capabilities to handle the full test management lifecycle. Tasks like organizing test cases, tracking executions, and analyzing results often require manual effort or scattered add-ons.

This is where test management tools come in. They are often add-on or integrated solutions designed to help QA teams plan, execute, and track testing activities directly within the Jira environment.

Test Management Activities

These Jira test management tools help you and your team conduct key testing activities:

Creating and Organizing Test Cases: QA teams define test cases to verify specific requirements or features. Each test case includes clear steps, expected results, and input data. Testers need to organize them into features or categories to maintain structure and reusability across projects.
Planning Test Execution: Once test cases are ready, teams group them into test executions aligned with sprints or releases. This ensures testing is scheduled and tracked systematically, allowing testers to know what needs to be executed and when.
Tracking Test Results and Coverage: As executions progress, testers record results (pass, fail, or blocked) and link them to specific builds or requirements. This helps measure coverage and ensures every user story or acceptance criterion has been tested.
Logging and Linking Defects: When issues are found, defects are logged and linked back to the failing test cases and related requirements. This traceability allows developers and testers to collaborate efficiently and quickly resolve problems.
Reporting and Analysis: Finally, test managers review execution reports, defect trends, and coverage metrics to assess product quality and readiness. They will apply filters and create charts to visualize these analyses.

2. AgileTest as a Test Management Tool in Jira

AgileTest, which is designed for test management in Jira, helps teams perform all these key activities with a wide range of features. It enables QA teams to create, execute, and track tests seamlessly inside Jira—maintaining clear traceability between requirements, test cases, and defects throughout the testing lifecycle.

Test Case Management:

AgileTest helps you create test cases quickly with its AI Test Case Generator. Based on your requirement descriptions, the AI automatically suggests multiple relevant test cases aligned with your defined objectives. You can then use the folder structure to group and manage these test cases, keeping them organized and easy to maintain across different execution sessions. When it’s time to run tests, simply select an entire folder to include all related test cases, thus reducing the risk of missing any and ensuring complete coverage.

Test Session Planning:

The Test Plan section in AgileTest allows you to create and manage multiple test executions within a single project. You can define an overall testing timeline to schedule and track each execution session, specifying when and what needs to be tested. This provides visibility and control over your testing progress, helping you understand how far the testing has advanced and how much time remains to complete the planned activities.

Test Execution:

Within each Test Execution, testers can record the actual status of every test step, such as Pass, Fail, or Skip. The overall test case status is automatically updated based on these step results. When reviewing failed tests, the team can easily identify the exact steps that caused the issue, making defect analysis more precise. Testers can also attach findings, screenshots, or comments to provide additional context, helping developers reproduce and fix bugs more efficiently.

Defect Linking:

For any tests that fail due to bugs, you can either create a new defect or link it to an existing Jira issue. These linked defects appear directly in the Jira issue view, allowing you to track testing progress alongside development work. Each defect includes references to the related test cases and test executions, giving full visibility into where the issue was found and how it impacts the overall testing effort.

Summary Reports:

AgileTest offers three key types of reports: Test Coverage, Traceability, and Defect Summary. These reports provide a clear view of the relationships between test cases, test plans, test executions, requirements, and defects. You can easily verify if all requirements are covered by related test cases, see which test plans and execution sessions they belong to, and identify any defects linked to the test cases. This maps the relationship of your test items together, helps you track testing progress, and ensures full coverage.

3. Test Management Tools in Atlassian Marketplace

On Atlassian Marketplace, there is a wide range of Test Management Tools you can choose from to match your preferences. Here is the list of 5 Test Management Tools for Jira that you can consider:

AgileTest

AgileTest offers an AI-driven test case generation and full Jira integration, providing QA teams with enhanced visibility, real-time test coverage, and defect management. Its folder-based organization and end-to-end traceability help you manage tests from creation to execution, all within Jira, thus saving time and improving collaboration between developers and testers.

Rating: 3.7/4
Pricing:
- Free 30-day trial
- Free for teams under 10 members
- USD 1.50 per user each month

AIO Tests: QA Testing and Test Management for Jira

AIO Test Management allows teams to generate test cases using Gen AI and maintain end-to-end traceability. It simplifies execution management, tracking statuses, defects, evidence, and more on a single screen. Teams can generate 20+ customizable reports, export them in PDF/Excel, and schedule reports for stakeholders.

Rating: 3.9/4
Pricing:
- Free 30-day trial
- Free for teams under 10 members
- USD 1.98 per user each month

Requirements and Test Management for Jira (RMT)

Requirements and Test Management (RTM) is helps organize and track testing efforts within a project. It offers full traceability with built-in requirements management and a tree-structured view for easy navigation. RTM enables reusable test plans, real-time customizable reports. With features like Traceability Matrix and AI-generated test cases, it ensures comprehensive test coverage and efficient project tracking, all in a streamlined Jira environment.

Rating: 3.5/4
Pricing:
- Free 30-day trial
- Free for teams under 10 members
- USD 1.82 and 2.3 per user each month for Standard and Advanced versions, respectively

QMetry Test Management for Jira (QTM4J)

QMetry is an AI-powered test management solution that simplifies the testing process with the help of AI to generate test cases, design test cycles, and detect flaky tests. It offers end-to-end test management, including organizing test cases in folders, tracing bugs and stories, and launching CI/CD pipelines. Insightful reports with traceability, planning, and coverage data are available, alongside custom 2D reports and Confluence integration.

Rating: 3.7/4
Pricing:
- Free 30-day trial
- Free for teams under 10 members
- USD 3.8 per user each month

QAlity Plus - Test Management for Jira

QAlity Plus allows teams to define test steps directly in Jira issues and easily track results. It offers features like test case creation, folder organization, and import/export capabilities. Additional exclusive features include assigning users to executions, adding tests via JQL, and generating Test Execution and Traceability Reports

Rating: 3.6/4
Pricing:
- Free 30-day trial
- Free Standard version for teams under 10 members
- USD 1.5 and 2.0 per user each month for Standard and Advanced versions, respectively

Final thoughts

An effective Jira Test Management tool is essential for QA processes and improving collaboration within Agile teams. Tools like AgileTest, AIO Test Management, and QMetry integrate seamlessly into Jira, offering features such as test case organization, execution tracking, and reporting. By using these tools, teams can maintain end-to-end traceability, reduce manual effort, and gain valuable insights into test progress. Choosing the right solution for your team can significantly enhance testing efficiency and product quality, all within the Jira ecosystem.

Best Practices for Executing Tests with AgileTest

Khiem Phan — Fri, 07 Nov 2025 08:17:58 +0000

Having good test execution practices is essential for you and your team to follow and conduct your testing activities effectively. Test execution is the stage where all your preparation and planning turn into actionable results. It’s where quality truly meets delivery. But simply running test cases isn’t enough; the value lies in how you execute them.

In Jira, tools like AgileTest can help this process in managing test cases, executions, and defects. This article explores what makes a good test execution and the best practices you can apply to improve your execution process.

1. What makes a good test execution?

A good test execution is more than just running test cases; it's about managing the time, coverage, and effectiveness. A good test execution should have:

A Detailed Timeline: You should plan in advance how many test executions you want to conduct in a sprint or milestone. This helps you ensure that you can run all of your test cases and executions without being rush due to the time limits
A Link between Test Cases/ Steps and Requirements/Defects: You need to establish and maintain traceability between your test cases and steps and their corresponding requirements and defects. After completing all test runs, you can trace each test case back to its related requirement and linked defect to verify coverage and investigate any issues.
A Prioritization of Important Test Cases: You ought to focus on some important test cases that need to be executed several times, rather than running all test cases every execution. This approach allows you to verify and stabilize essential functionalities first, increasing the likelihood of catching and resolving critical defects early for better product quality.

2. Best Practices to Ensure A Good Test Execution

Let’s see how you can enhance your test execution process with AgileTest in the following best practices.

Define A Specific Timeline

After generating your test cases with detailed steps, the next step is to add them to a Test Plan and prepare all relevant Test Executions. You don’t need to define every exact test case or execution at the very beginning. Instead, you can start by estimating how many sessions you’ll need and assign a few test cases to each. You can always refine and adjust the plan as testing progresses.

To ensure that you execute all the necessary test cases, you should categorize them under requirements, features, or purposes, then have at least one execution for these test cases within the same group. For example, to add all test cases of a requirement into one test execution in AgileTest, you can click the Test Plan section → Choose Test Execution → Hit the Add Test Executions button → Apply Covering filter to select those test cases that you have categorized under requirements. This ensures that you have at least one execution filled up with relevant test cases for each requirement.

Within the Test Plan section, you’ll see the project timeline (with defined start and end dates) alongside the Execution Status. This overview provides a clear snapshot of your testing progress—showing how many executions have been completed, which are in progress, and how much time remains. Having this visibility helps you stay in control of your testing activities, allowing you to plan proactively and manage your test sessions more effectively over time.

Organize Test Cases with Traceability

Whenever you execute your tests, it’s important to document your findings and results thoroughly. Within each test case, you can record the status of every individual test step. This level of detail allows you and your team to pinpoint exactly which step failed, rather than only seeing that the overall test case encountered an issue. In AgileTest, there is a mechanism to calculate the test case status based on the test steps' status. For example, if all your test steps pass, you will receive a pass result for the whole test . Meanwhile, if any steps fail, the whole test case is considered failed. You just have to add status to the test step, and the system will update the test case status for you.

Also, you should make sure that you have at least one requirement linked to a test case. This would reflect the coverage of your execution, which indicates whether the test cases have covered enough of your defined requirements.

In case any of your test steps fail due to a bug, you need to create or add an existing defect to the specific steps. With this action, you and your team can trace the failures with their root causes to reproduce, analyze, and fix the issues. The defects can be visible in the Jira issues view, so that your non-technical members can also be aware of ongoing bugs. They can easily see which bugs have been found, the context in which they occurred, and the progress toward resolution.

Rerun Failed Test Cases

After you have executed every test case at least once, you can create a follow-up execution to focus on some important test cases. They can be those with the highest impact on the overall system or those with high failure frequency. In case you want to create a new test execution to reverify the recent failed test cases, you can go to the Test Plan section → Create Test Execution → Switch to Status tab → Choose Fail and add all listed test cases to your execution.

You can keep rerunning these important test cases again and again until it can fully match your completion criteria. Each rerun provides valuable feedback, confirming whether the fixes are effective and ensuring no regressions are introduced in related areas

Each test execution is a chance to learn and get better. Review your results, identify what worked and what didn’t, and adjust your approach for the next cycle. Staying consistent in how you plan, execute, and follow up helps your team build confidence, reduce issues, and deliver higher-quality releases every time.

Final thoughts

Effective test execution isn’t just about completing test runs—it’s about ensuring traceability, structure, and continuous improvement. With AgileTest, you can manage every aspect of your testing process directly in Jira, from planning and executing to tracking results and defects. The given test execution practices have indicated that by defining clear timelines, maintaining traceability, and rerunning failed test cases, you create a reliable testing process that leads to more stable releases and higher product quality.

How to Enhance Your Jira Test Project Management

Khiem Phan — Sat, 01 Nov 2025 11:19:01 +0000

Teams often start with an all-in-one Jira project, where you manage requirements, development, and testing together. This setup works well for small teams because it’s simple, collaborative, and keeps everything in one place.

As projects grow, however, the all-in-one model can become hard to manage. This use case explores what issues arise when large teams apply an all-in-one Jira project and suggests how teams can apply a more effective project organization to overcome the issues.

1. Problems with All-In-One Project Approach

Cluttering tasks

When developers and testers share the same Jira project, everything ends up in one place. You may find a chaotic mix of stories, bugs, and test cases across places, including the backlog and the project board. Developers scroll through long lists of testing items they don’t need, while testers have to filter through development tasks just to find their own work e.g., test cases, test executions, and even test plans. To stay organized, teams often tag every issue manually or rely on naming conventions (Dev/Test) and Epic labels to separate responsibilities. Over time, this manual sorting not only slows everyone down but also increases the risk of mistakes, like misplaced issues, duplicated work, or missed updates. What starts as a simple shared space gradually turns into an extra coordination effort that distracts the team’s attention from actual testing and development.

Conflicting workflows

When developers and testers work in the same Jira project, their workflows often overlap in confusing ways. The product owner and developers use backlogs to plan upcoming features, adding user stories, bugs, and epics that define what to build. Testers, meanwhile, may need to create test cases or test executions in that same backlog to plan their testing cycles. However, because all these issue types share one view, testers can easily lose track of which items belong to development and which belong to testing. A tester might open the backlog to create a new test execution, only to find it buried between dozens of stories and bugs from the dev team.

2. Two-Project Organization Approach is the Key

When team sizes grow, developers and testers need different workflows. At this point, it makes sense to separate development and testing into two projects — one focused on building features, and the other on validating them.

Project for developers

The development project is where developers plan, implement, and track their work. It typically includes user stories, epics, and bugs. Epics outline major features that need to be completed in multiple sprints, giving a high-level view of what’s being built. User stories break those epics into smaller, actionable tasks that describe specific user needs, guiding developers on what to build and giving testers clear references for validation. Bugs capture defects or unexpected behaviors found during testing or in production, ensuring issues are tracked, prioritized, and resolved before release.

Project for testers

The testing project, on the other hand, is for the QA team. It includes test cases, test plans, and test executions, allowing testers to organize, run, and report on testing activities without cluttering the development workflow. Test cases define the steps and conditions needed to verify that a feature works as expected, usually linked to a user story or requirement (in another project) to ensure full coverage. Then, Test plans organize these cases under a shared goal, outlining what will be tested, when, and by whom. Test executions capture the actual test runs and results, showing which tests passed, failed, or need re-testing.

3. How to Achieve Two-Project Management with AgileTest

Separating development and testing into two projects helps teams stay organized, but it can also make tracking progress across both sides more difficult. To keep everything connected, AgileTest helps teams trace back to the work items, giving a broad view of what your team has built, tested, and is ready to release.

With AgileTest, you can configure the settings for requirements and defects mapping. By default, Stories and Tasks are mapped to Requirements, while Bug Issue Type is mapped to Defects.

Requirements Mapping

Once configured, testers can open the related Jira requirement from within AgileTest. Instead of switching tabs, testers can click directly on the issue ID to redirect to the Jira tickets.

Then, you and your team can also link the requirements from one project to another to achieve a two-project approach. You can go to your Test cases → Requirements → Add your existing requirements or create a new one. You can choose different types of work items for requirements, including tasks, subtasks, stories, and epics, depending on your previous Requirement mapping setup.

This means testers are directly linked to the requirements (Stories/Tasks) in Jira, which is where developers are already working. Therefore, testers and developers can have different workflows and tasks for one app on the two projects, but testers will receive updates during changes in the developers' tasks or stories.

Defects Mapping

Similarly, once you have configured Defect Mapping in AgileTest, your development team can easily track and manage bugs directly within their Jira environment. When a test case fails in AgileTest Test Execution, testers can log a new defect or add an existing one. If you create a new defect, it will also appear in the Jira issue view. In addition, the defects will also be linked with the corresponding test case and requirement.

This feature helps developers quickly prioritize and address defects based on testing feedback. Developers can efficiently review the specific test cases and requirements related to the defect, ensuring your team can resolve the issues within the correct context.

By separating development and testing into distinct Jira projects, teams can reduce irrelevant work items, improve workflow clarity, and stay focused on their core responsibilities. AgileTest ensures traceability between the two projects, so both developers and testers can work in parallel without losing sight of dependencies. This approach minimizes confusion, prevents task overlap, and keeps teams aligned, even as the project scales.

Final thoughts

As teams scale, managing testing isn’t just about tracking cases; it’s about keeping development and QA aligned. An all-in-one Jira project works early on for small teams, but larger teams need clearer workflows to stay efficient. Splitting projects helps organize work, yet it can also create gaps in visibility. By focusing on clear traceability and shared visibility, teams can bridge that gap, ensuring that you can build and validate every feature with confidence.

Top 5 Test Management Tools in Jira 2025

Khiem Phan — Sat, 01 Nov 2025 11:16:53 +0000

Selecting among different Test Management Tools in Atlassian Marketplace is a crucial step to ensure that the software is thoroughly tested and ready for deployment. Whether you're managing a small team or an enterprise-level project, the right test management platform can enhance your testing efforts and improve collaboration between teams.

This blog compares five popular test management tools: AgileTest, TestRail, QMetry, Xray, and Zephyr, to help you choose the best one for your needs. Each tool offers a unique set of features, ranging from manual and automated testing support to reporting and AI-driven capabilities. We’ll explore how these tools stand out in terms of test types, reports, AI functionalities, and pricing, helping you make an informed decision.

1. AgileTest

AgileTest, developed by DevSamurai, is a Jira-native plugin test management tool. It is available in two versions: Jira Cloud and Jira Data Center. All the features are nearly the same for both versions, excluding the AI-powered test generation, which is only available in the Cloud version.

Testing Strategies

AgileTest has three separate features that help testers and teams conduct three main types of manual testing, including:

Script Test: a feature that allows testers to list out test cases with no detailed test steps, can be used for a quick and daily check-up.
Exploratory Test: a feature that testers can use to record their findings during exploratory test sessions with no preparation needed in advance.
Classic Test: a feature that testers can define requirements, generate test cases and test steps, then create test executions to track and run these test cases following the plan.

Currently, AgileTest supports teams to integrate with CI/CD tools (Jenkins, Bitbucket, GitLab, Github, CircleCI,...) and seven testing frameworks (JUnit, NUnit, TestNG, xUnit, Robot Framework, Cucumber, Behave) for automated testing.

Report

AgileTest supports you and your team to generate three types of reports that are linked with Requirements and Defects:

Test Coverage report: identifies if all the requirements are fully covered with test cases
Test Traceability report: shows the relationship between test cases, test plan, defects, test runs, and requirements.
Defect Summary report: summarizes all defects found during execution sessions, gaining more centralized management in one place.

AI Capabilities

AgileTest’s AI Generators can help you generate test cases and test steps from the description of your Requirements. The generating process takes place within seconds, and you can have more time to edit and refine these work items.

Pricing (Cloud version)

Free 30-day trials
Free for teams under 10 members
$1.5 per member each month for teams from 11 to 100 members; discounts will be applied when the team size increases

2. TestRail

TestRail, developed by IDERA, Inc., is a standalone test management tool that can integrate with Jira. It is available in Cloud and Server versions. Both versions provide core test case management and reporting, but the Cloud version is hosted by IDERA, while the Server version requires on-premises setup (own-host).

Source: TestRail

Testing Strategies

For manual testing, TestRail focuses only on the formal testing, which usually needs detailed test cases and test steps setup. In case you need to conduct some simple testing, such as ad-hoc or exploratory, you can also use this feature and bypass the complex setup. However, it would take you time to manage results from different testing strategy in one place.

For automated testing, TestRail supports integration with nine CI/CD tools (Jenkins, GitLab, Bitbucket, GitHub, Azure DevOps, CircleCI, Travis CI, TeamCity, Bamboo) and seven testing frameworks (JUnit, NUnit, TestNG, xUnit, Selenium, Cucumber, Robot Framework, Appium).

Report

TestRail shifts the focus of reports to the test execution and the overall test project with four main types of reports:

Test Case Report: shows test case details, execution status, and metrics.
Test Run Report: displays the status of test cases (e.g., pass, fail, blocked), which helps teams track overall execution progress.
Test Plan Report: tracks the execution of multiple test executions grouped in a Test Plan, offering insight into overall project progress.
Defect Summary Report: TestRail integrates with Jira to generate defect reports, summarizing defects linked to failed test cases.

AI Capabilities

TestRail’s AI Test Cases Generator can help you generate test cases based on Requirements and saved templates. You can refine the draft that AI has generated to better match your preferences.

Pricing

No trials
$40 per member each month for teams from 1 to 20 members
$33 per member each month for teams from 21 to 60 members; discounts will be applied when the team size increases

3. QMetry

QMetry, developed by QMetry, Inc., is a standalone test management tool with Jira integration. It is available in Cloud and On-Premises versions. Both versions offer test management, automation support, and reporting, but the Cloud version includes AI-assisted test analytics and faster setup.

Source: QMetry

Testing Strategies

Regarding manual testing, QMetry has two main features:

Exploratory testing: an extension that helps testers record and store their finding during exploratory sessions
Test Case Management: a feature to generate test cases & test steps, then execute them for test results.

For automated testing, QMetry integrates with five CI/CD tools (Jenkins, GitLab, Bitbucket, GitHub, CircleCI) and seven testing frameworks (JUnit, Selenium, Cucumber, Appium, TestNG, Robot Framework, Postman).

Report

QMetry offers testers three main types of reports:

Test Coverage Report: provides coverage analysis by linking test cases to requirements or user stories, ensuring all aspects are tested.
Test Execution Report: shows the status of test cases after execution, tracking results (pass/fail/blocked).
Defect Summary Report: displays defects identified during test runs and links them back to failed test cases, helping teams manage defects effectively.

AI Capabilities:

Auto-Test Case Generator: can automatically create test cases based on application requirements and user stories
AI-powered smart search helps scan through vast amounts of test data to locate relevant test cases, defects, and other important artifacts to find specific test artifacts quickly and efficiently

Pricing (Cloud Version)

Free 30-day trials
Free for teams under 10 members
$3.8 per member each month for teams from 11 to 100 members; discounts will be applied when the team size increases

4. Xray

Xray, developed by Adaptavist Group, is a Jira-native plugin for test management. It is available in three versions: Cloud, and Data Center. The Cloud version offers automatic updates and cloud hosting, while Data Center provide self-hosting, greater customization, and scalability options.

Source: XRay

Testing Strategies

For manual testing, XRay emphasizes the formal testing only. It has separate areas for you to organize test cases, test plans, and test execution. Similar to TestRail, you can also conduct other simple test types and bypass the complex setup.

For automated testing, Xray supports integration with five CI/CD tools (Jenkins, GitLab, Bitbucket, Bamboo, Azure DevOps) and seven testing frameworks (JUnit, TestNG, Selenium, Cucumber, Robot Framework, Appium, JBehave).

Report

Xray also supports you with three types of reports

Test Coverage Report: tracks whether requirements are fully covered by test cases, showing the relationship between tests and requirements.
Test Execution Report: shows the execution results of tests, including status, failure details, and links to Jira issues.
Defect Summary Report: provides a summary of defects discovered during test execution, integrating with Jira issues for tracking and resolution.

AI Capabilities

AI for Smart Test Execution: analyzes historical data and usage patterns to recommend the best order for test execution, prioritizing the most impactful tests first.
Automated Test Result Analysis: analyzes automated test results and can suggest potential reasons for failures, making it easier to identify issues early in the test cycle.

Pricing (Cloud version)

Free 30-day trials
$1.2 per member each month for teams under 10 members
$7.6 per member each month for teams from 11 to 100 members; discounts will be applied when the team size increases

5. Zephyr

Zephyr, developed by SmartBear Software, is a Jira plugin test management tool. It is available in Zephyr Squad, Zephyr Scale, and Zephyr Enterprise versions. Zephyr Squad is designed for small to medium teams with basic test management features. Meanwhile, Zephyr Scale adds advanced planning, reporting, and stronger automation integrations. Zephyr Enterprise offers full enterprise-level scalability, centralized test repositories, and extensive customization for large organizations.

Source: Zephyr

Testing Strategies

For manual testing, Zephyr also focuses on structured test management. It allows teams to create test cases and test executions with separate sections. Same as TestRail and Xray, you can also make use of this feature for the less complex test types.

For automated testing, Zephyr supports integration with five CI/CD tools (Jenkins, Bitbucket, GitHub, CircleCI, Bamboo) and seven testing frameworks (JUnit, TestNG, Selenium, Cucumber, Robot Framework, Appium, NUnit).

Report

Zephyr offers three types of reports for your team to choose from

Test Execution Report: Tracks the execution status of test cases across test cycles, providing insights into pass/fail results.
Test Cycle Report: Displays the progress of tests within a specific test cycle, showing execution status and defects.
Defect Summary Report: Summarizes all defects linked to failed tests, offering centralized defect tracking in Jira.

AI Capabilities

AI Test Step Suggestion: suggest test steps based on your existing test cases. This feature aims to enhance test coverage and efficiency by automating part of the test creation process

Pricing (Cloud Version)

Free 30-day trials
$1.0 per member each month for teams under 10 members
$5.21 per member each month for teams from 11 to 100 members; discounts will be applied when the team size increases

Final thoughts

Choosing the right test management tool depends on your team’s specific requirements, scale, and workflows. Whether you’re looking for a Jira-native plugin with AI-powered test generation like AgileTest, a reporting and test case management system like TestRail, or a tool with strong CI/CD integration like QMetry, each platform offers unique features that cater to various testing needs.

By understanding the key strengths and unique features of each tool, you can select the one that best fits your testing workflow and project requirements. Whichever tool you choose, integrating a test management system will ultimately enhance your testing processes, improve collaboration, and help you deliver higher-quality software.

How the Defect Summary Report Brings Order to Scattered Jira Defects

Khiem Phan — Fri, 24 Oct 2025 08:46:15 +0000

Defects are a natural part of every testing process. They tell you what’s not working, where the product needs attention, and how stable a build really is. It is not a problem that defects exist. Instead, it often comes as an issue when these defects are stored and tracked in fragmented ways.

Although Jira provides powerful tools for tracking defects, many teams face difficulties when generating an actionable defect report. A typical report requires a clear timeline to visualize trends, and it needs to include appropriate filters for refining the selection based on specific criteria. Additionally, it should allow for easy traceability back to the defects themselves. This article will explore the challenges teams face when using Jira reports for defect tracking and how a centralized defect summary report can solve these issues.

1. Defect Report from Jira’s statistics

In most QA teams, Jira serves as the main platform for managing defects throughout the testing process. When a tester identifies an issue during execution, they log it as a Jira ticket that includes key details such as the summary, steps to reproduce, expected and actual results, severity, and priority.

Create a Defect Report on Jira

Whenever your team wants to create a Defect Report from Jira’s statistics, you have to create a Jira Defect Filter. Without the filter, you cannot sort the necessary data for your Defect reports. This means you might end up mixing all work items together, even those unrelated to your current testing cycle. Creating a Defect Filter is a necessary step to define that only Bugs appear in your report within a specific time frame. Here, the two compulsory selections for your Defect Filter that you need to configure are Bug (for Work Item) and your desired timeframe (for Time Created).

Then, you move to the Jira Dashboard, pick up a widget, and search for your created Filter and choose Statistic Type (Priority, Assignee, Status, etc). When you choose the Priority for the Statistic Type, your Jira Defect Report will show how many defects exist under each priority level (High, Medium, Low).

Issues when Using Jira Defect Report

Repetitive Setup

In Jira, you need to setup filters multiple times. For instance, if you want to create a chart displaying bug priority for the current sprint, you first need to create a filter with the exact timeline. Then, you must go to the Filter View and apply this filter to see your bug list. Later, you want a new chart that shows bugs created by assignees from the previous sprint, you would need to create a new filter tailored to that time frame and data type. You will also need to apply this filter view to see the new bug list.

This repetitive process can become a problem when you want yourl reports with different combinations of time frames and data sets. Each time you change your focus, you must manually adjust the filters, making it difficult to quickly switch between views or purposes. This repetitive setup process can be time-consuming, especially when working across multiple test cycles or projects.

Lack of Traceability and Actionable Information

You can combine multiple charts in a single dashboard to visualize different aspects of your defect data. However, these charts are primarily for statistical purposes, helping you stay updated on the progress of your defects. The charts display numbers, but they don't provide specific work item IDs or the details needed for further action. For example, while the chart may show that your project has 2 high-priority defects, it won’t tell you which defects they are or which test steps or test cases they are linked to. To investigate and view the full details of the defects, you would need to switch back to the Filter view, where the detailed list of defects is available.

2. AgileTest’s Defect Summary Report

To address these challenges, defect management needs to be consistent and accessible throughout the entire testing process. Rather than relying on scattered defect lists and disconnected tools, there should be a centralized view where you can easily review every issue, track its status, and understand its context in real time.

AgileTest’s Defect Summary Report can help achieve this by organizing all defect information in one place. With the ability to filter by date range, milestone, and test execution, along with a summarized view of defects linked to their test steps, teams can more efficiently review and track defects across multiple testing cycles.

Quick Setup and Generating Process

In AgileTest, you do not have to create a Filter view of separate defect information with other work items, like in the Jira report. You can directly access the Report section → Defect Summary to generate your report of bugs.

By default, the report will display defects from all existing projects. You can filter the information to be displayed by selecting the specific day ranges, milestones, or test executions for which you need to collect test results. As an illustration, you can choose a sprint period to check if the percentage of bugs in this sprint has been fixed, and those that need to be moved to the next cycle.

In case you want to display other distribution charts, you can click the “Display” button and select your desired ones. This frees you from the process of repeatedly picking up a widget, then selecting a suitable filter view, and choosing a statistic type. You can now quickly generate and switch between charts in only one tab, thanks to the built-in filters.

Actionable Information

As you scroll down, you will see a detailed table that lists all related defects under the pie charts. This table provides detailed defect data that complements the charts, allowing you to quickly identify unresolved critical issues and decide on next steps. By looking at both the status and priority columns together, you and your team can spot which issues are the most critical and still unresolved, helping you decide what to focus on.

Additionally, you can sort defects in ascending or descending order by priority or status. This brings the most critical issues to the top of your view and reduces the chance of missing high-impact bugs that might be buried among lower-risk ones.

Defect Traceability

During test execution, when a test case or step fails, you can quickly create a new defect and link it directly to the corresponding test case or step. This link will be summarized in the Defect Summary Report, where it also acts as a shortcut, allowing you to trace back directly to the failed test case or test step from the report.

With the table view in the Defect Summary Report, you can see additional information about defects, such as the projects they belong to. And when you need to trace back the details of defects later, simply click the Ticket Key (or ID) to open the Jira defect ticket right away. This way, you can trace defects back to their source and fix them faster, without having to search for them on the Jira board again.

Final thoughts

Fragmented defect data isn’t just inconvenient; it can delay your entire release. When your team has to constantly switch between different tools, like Jira reports, to check statuses, priorities, or defect details, they waste valuable time that should be spent on testing and resolving issues.

The Defect Summary Report solves this by centralizing all your defect information in one place. Unlike Jira reports, which require manual filter creation and separate dashboards, the Defect Summary Report provides a single view. You can quickly access your recent defect status, identify critical issues, and trace defects back to their source without switching between tabs. This approach saves time on managing data, allowing your team to focus on delivering a high-quality product.