Nowadays, developing a piece of software or an app could require companies to meet tight deadlines. You can’t very well spend a year or two developing a product when the competition does it in just a few months. Traditional methods struggle to keep up with these developments, causing companies to adopt automation.
That said, there is one ally that surpasses even automation when it comes to efficiency. Artificial intelligence (AI) can improve QA test management and the overall workflow. Testing tools such as Kualitee can use AI-based analytics to boost the testing process, leading to faster time-to-market (TTM).
Challenges Brought by Manual Test Planning
Manual testing alone has been a useful tool in test management. Human intuition can always be relied on, especially when someone has been studying their entire life for this. That said, using only manual testing can bring on a series of challenges, including:
- Time-Consuming: Smaller projects are easily handled, but larger ones can take precious time you don’t have. Automation can help, but test scripts can’t be updated without intelligent involvement.
- Human Error: While man-made issues are often kept under 1%, they can still happen. Even the most experienced individuals can make a mistake. High stress levels or overload could cause incorrect use of data, which leads to a faulty code.
- Growing Test Volume: When a piece of software or app becomes more complex, it requires a higher test case volume. This makes it potentially difficult to cover every scenario, leading to bugs later on.
- Prioritization Challenges: Human testers rely on intuition or availability to fix bugs. This could cause them to address an unimportant issue just because it is there and leave the high-priority one alone. If an important defect is missed, it can lead to unstable software.
Manual testing has its benefits and cannot yet be discarded altogether. However, to improve project management, it is best to use artificial intelligence alongside it.
Benefits of AI in Smart Test Planning
AI offers a series of benefits when running queries, including the following:
- Accuracy: Defects are caught much faster and with higher precision.
- Efficiency: Testing effort and time significantly decrease.
- Speed: AI can perform dozens of commands per second, speeding the testing process.
- Coverage: AI uses generative technology to explore more edge cases and scenarios.
- Cost Reduction. Costs are reduced as less human involvement is required.
Once a testing team takes advantage of a defect tracking tool that uses AI, it becomes much easier to maintain a scalable, structured plan for testing.
How AI Test Management Tools Can Help
Bug tracking software such as Kualitee relies on AI and can help the development process in the following ways:
- Risk Prediction
AI-based tools like Kualitee can identify high-risk areas with the greatest probability of developing a bug. The system will go over application modifications and cross-reference past trends, predicting potential defects.
This way, testers can put their focus on managing them beforehand and maintaining critical functions. This can keep post-release defects at a minimum, provided they use a good test management tool.
- Adaptive Coverage Optimization
Each update can bring on a testing gap that affects the functionality of the application. An old test script might not offer the appropriate coverage, leaving blind spots in the code that could become a great risk.
Kualitee evaluates all the new features and updates, suggesting the right notifications for the test script. In the end, the test doesn’t overlook anything while avoiding unnecessary executions.
- Automated Test Generation
Generating test cases is perhaps one of the most time-consuming parts of planning. Some tests require you to use the same script on and on, but others could require modified or new scripts altogether.
An AI-based defect management tool such as Kualitee can generate test cases automatically, based on the new requirements. It will also take historical data and implement the app’s code to generate a suitable script automatically.
- Intelligent Bug Classification
Software bugs can take numerous forms, with some being more difficult to handle than others. Some are low-priority and will likely not cause any damage to the app if they stay there longer. Others can affect the functionality of the app if you don’t remove them promptly.
AI-based defect management software can detect and classify these bugs, flagging the ones with the highest priority. This saves time in the long term, as testers won’t have to fix expensive damage to the app later.
- UX and Cross-Device Testing
A piece of software or application could work very well on the intended device (e.g., a laptop), but not on ones with different screens (e.g., a smartphone). Testing tools that rely on AI, such as Kualitee, can catch onto these inconsistencies.
During a test run, they gauge both the visuals and the user experience. Since the analysis is generally done at a pixel level, the tool saves time because it keeps false positives to a minimum.
- Intelligent Test Execution
AI tools optimize the execution of test cases by adjusting the strategy based on the results. This is important because sometimes, threats appear mid-cycle, affecting the overall integrity of the script. Artificial intelligence detects these issues immediately and changes priorities automatically, going after the high-risk issues.
Bug tracking software also offers continuous test case optimization since it uses data from past cycles. For example, if the AI-based tool notices that a test often fails in a specific environment, it could offer suggestions on how to change the scenario.
- Resource Efficiency
Smart testing requires a balanced mix of manual and automated testing, keeping efficiency at its highest. As humans, we may not be able to manage this correctly, because we like to get things done ourselves. Machinery can be error-prone, which is why humans tend to prefer manual administration sometimes.
In these cases, it is very easy to forget what should be left to a machine and what we should handle ourselves. AI tools are there to ensure optimal allocation in our stead. A good bug-tracking tool can suggest what tests should be executed automatically and which ones could benefit more from manual testing. This way, no time or workforce is lost.
- Real-Time Reports and Insights
AI defect management software can provide real-time insights into risk areas and other parts of the test. This way, QA teams can adjust a strategy midway, improving the quality of the results. This spares them from having to wait precious time on a flawed scenario or script.
For example, if the AI tool notices that it takes longer to generate a result, it can notify the testers. In turn, these can look through potential bottlenecks and fix the issue. Taking this action beforehand can prevent a delay in the release, helping you stay on track.
Verdict
Test planning can be made smart by adding AI insight into the mix. Artificial intelligence can offer actionable, data-based insight that makes testing more precise and time-efficient. Technology is consistently growing as well, which means that an AI-based test management tool could save time in the long run. Consider using tools such as Kualitee to take advantage of AI and stay ahead of your competition!
