Do you have a process in place to analyze defects, identify the defect categories and common pitfalls, and correlate the results to recommended corrective actions? Forced to get more done with less, organizations are increasingly finding themselves in need of an effective defect analysis process. We may often believe that the problem is resolved but in reality, we have just addressed a symptom of the problem and not the actual root cause.

With the increasing importance, size, and complexity of automated test suites, automation teams roles and responsibility are growing increasingly complex over time. With automation suite growing to hundreds of test case, analyzing the automation failure became a full time job of couple of members in QA team and causing productivity loss. This makes automation analysis a time and resource consuming process.

Can you imagine a tool can automatically analyze and identify the patterns of defects to find the root cause and enable the assignment to the correct team, reducing defect turnaround time and improving productivity.? Well, this is possible with the help of AI and Machine learning algorithm.

There is an increasing attention of reporting bugs resulting from software applications, which are considered as an important and precious source for application’s memory. Past errors play an important role in future work in building new software applications. This happens through avoidance of falling in the same errors or estimate the appropriate time and selecting good developers needed to solve new coming issues. Also, studies show It has been estimated that over 70% of the total costs of software development process is expended on maintenance after the software has been delivered.
Software applications errors are managed and maintained by bug repository or issue tracking system. Issue tracking system often contains a knowledge base containing information on each defect such as: description of the problem, resolution to fix it which is called impact analysis, project name, founder role, phase detected and phase Injected. Issue track system is an open a communication channel between different people such as end users, programmers and testers to find the suitable response about detected issues in software applications

Even a highly experienced QA Engineer can only remember few things in analysis, and it is hard to remember all the tracking through Issue tracking system. In contrast, machine learning can reproduce these dependencies. Moreover, it can calculate the output for new data because the algorithm is capable of learning. For humans, the dependencies between input and output are a ‘black box’. For UReport, input is a automation failure, and automatic analysis output is a defect classification, bug number, priority and the area of testing that the defect belongs to, and so on. Brickred System’s UReport is a single solution for all the challenges in analyzing the automation results, providing a AI and ML solution to automatically analyze the automation failures and saving valuable time of testers. The most repetitive tasks appearing in a test automation process are automated with the help of AI and human intervention is minimized. For more information visit www.brickredsys.com/ureport 

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