Test management has always been a necessary part of making sure a software release is qualitative. It covers the organizing, planning, executing, and monitoring of software testing tasks. Reliable defect management software must track bugs, analyze test results, and manage test resources. This is often done through human involvement and some automation, but different technology is making its way into the world of testing.
Artificial intelligence is a tool that helps make data-driven decisions using advanced analytics and automation. Testing tools such as Kualitee use AI to go through historical data and analyze the current progress. This way, these instruments can assign roles and maximize resources, improving the efficiency of the testing process. This article will dive more into how that’s done and how testing teams should implement it.
How AI Technology Benefits Test Management
AI technology benefits test management by acting as a “third man” with a very good memory. Here is how an AI-driven defect management tool such as Kualitee can help.
- Improved Productivity
AI-driven project management improves productivity by automating test execution for routine tasks. These tools can plan test cases without requiring any human intervention, keeping manual tasks to a minimum. Since the team no longer has to focus on tedious tasks, AI-driven testing can improve productivity and performance by up to 40%. Artificial intelligence becomes like a “productivity calculator,” optimizing the workflow to prevent overload.
- Better Accuracy
Even the smartest minds are made to forget at some point. Our brains forget about 90% of new information within a week. Plus, as we stuff it with new facts, we might struggle to remember some of the details. AI-trained models are different. They don’t forget, and testers ensure better accuracy by using an AI-driven defect management tool. AI uses past test results and research to spot bugs or inconsistencies, identifying inefficiencies with more accuracy than humans.
- Faster Prioritization
Human testers know their priorities in the short term. This way, they may leverage their resources accordingly. Still, some risks may look small now but can have a great impact in the future. AI tools such as Kualitee can perform defect analysis checks and refine test cases in real time, letting testers place their focus on the highest-priority bugs.
- Increases Test Coverage
The broader the test coverage, the easier it is to catch a potential bug in the code. However, with manual or regular automated test management, coverage is still limited. Humans have to write the scripts for each scenario, and few teams have the resources to cover everything. AI-driven bug tracking software can allocate some of these tasks to generative AI, which can create different test cases within minutes. This reduces the chances of incomplete test coverage while freeing up time for the testing team.
- Catches Hidden Defects
Defects can appear at any moment in the code. However, when you have thousands of issues there, even experienced testers can miss things. A defect management tool such as Kualitee uses automation to catch potential errors. This includes those who have yet to deal any damage and are in their “brewing” stage. This can prevent costly issues from appearing in the future.
Tips to Maximize Test Management Efficiency with AI
AI tools such as Kualitee can be an asset for allocating resources and maximizing productivity. For that to happen, it has to be done right. Here are some tips to implement this piece of technology.
- Use AI-Driven Test Management Tools Early
Tools such as Kualitee need all the historical data they can get to allocate resources effectively. When using AI-driven QA test management, make sure to implement the technology from the early stages. This lets it collect data that can be later used in test cycles. Using that information, these tools can refine a test scope and identify the resources needed before the execution process even begins.
- Automate Issue-Tracking and Escalation
Not every bug is meant to be solved at that very moment. Especially in the incipient stages, code can have hundreds of bugs, and addressing the high-risk ones is a priority. That said, low-risk bugs that are left alone for too long can escalate. This is something that humans can easily miss. Thus, implementing an AI-based test management tool can help allocate resources better. This can flag defects that haven’t been looked at within a specific timeframe or suggest alternative resourcing if the original developer isn’t available.
- Implement a Continuous Learning Model
AI models learn as they go, and they can get even “smarter” as you advance through the project. You may do the original training, but as time goes by, it learns to improve even if you don’t program it for that. When it comes to software testing, AI tools can identify patterns that lead to defects. They can also see who did the best job to solve issues. Even if a successful test case needs human involvement, the information should be “taught” to the AI tool so it knows how to address the issue later on.
- Use AI for Risk-Based Testing
AI can prioritize tests based on risk factors, both in the short and the long term. Humans can do that too, but it’s not in our nature and possibilities to see that far ahead. Intuition helps us get through some risky times, but the riskiest ones are those that we underestimate. This is where test management with AI comes in. You can use it to prioritize a test case based on the risk level. This way, testers make sure to streamline the low-risk areas. At the same time, they put more resources into the easily missed high-risk points.
- Set Up Resource Load Balancing
Where teams are concerned, everyone should have a similar workload to maintain productivity. However, the truth is that resources are not always balanced correctly. Many end up doing more work accidentally. This way, they overload themselves, whereas some 43% get bored with nothing to do. A good AI-driven defect tracking tool can monitor the workload and even out the distribution, delegating tasks if necessary. This task balancing helps prevent burnouts, which affect productivity and efficiency.
- Set Up Collaborative AI Systems
AI can do a great job at going through data and generating test cases faster than any human would. Humans may be limited in that aspect, but their intuition and ability to recognize bias make them a sensible choice. Rather than choosing one or the other when allocating resources, it’s better to implement collaborative AI. Once these strengths are combined, the time needed to formulate and execute test cases shortens. This allows the QA teams to cover more ground and focus on refining details, improving overall efficiency.
The Bottom Line
AI can be a true asset when managing resources and improving productivity. A defect tracking tool such as Kualitee that uses this technology can improve the accuracy of tests and get faster results. When every resource is used to its highest potential (even the AI itself), it improves overall software quality and efficiency. This places you in front of your competition. You just need a good plan and the appropriate tools from the start!
