UNIFIED REPORT PLATFORM (UReport)

Improving existing mechanism for optimization/efficiency

UTAP (Unified test automation platform)

Improving existing mechanism for optimization/efficiency

How AI Auto analysis of test results works?


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 

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 

How to measure effectiveness of QA Automation ?

While many companies today understand the importance of implementing effective test automation into their process , not all manage to do it properly . When you’re part of a software Development team you hear it time and time again : “We don’t know if QA Automation is effective.” This Automation results and data holds the  keys to valuable insights. Organisations  that unlock these insights can create better experiences for customers and gain a huge competitive advantage. An increasing number of managers yearn for QA Automation report to help them make major decisions in the organisation , so a Manager dashboard that utilizes valuable metrics has become critical for any successful company . When designed correctly a good QA dashboard can help a company track strengths and weaknesses , show performance and quality of a product. Unfortunately many out of box dashboards from existing framework use ineffective reporting visualization and matrix that ultimately confuse the viewers rather than accurately direct future success. Selecting the QA metrics that matter and building dashboards based on these metrics deliver actionable insight and drive product quality and smooth release. A QA dashboard should be considered your company ‘s  single source of truth . Any stakeholder can look at it and immediately get a sense of how the product is doing and where it stands in relation to its own goals , its competitors and overall market. All executive strive to optimize business processes, become more efficient and increase productivity. The challenge is understanding where improvements can be made that will impact a product quality, performance and release. To keep executives calm and teams focused, you need a QA dashboard based on key metrics that is used as the foundation for all decision making. QA Dashboards that are accessible to all team members, as well as to management, become the lifeblood of the stakeholders. But how often have you presented and agreed upon QA dashboard only to be asked for  additional data points or for a dissection of the data in ways you hadn’t anticipated? All executive strive to optimize business processes, become more efficient and increase productivity. The challenge is understanding where improvements can be made that will impact a product quality, performance and release. To keep executives calm and teams focused, you need a QA dashboard based on key metrics that is used as the foundation for all decision making. QA Dashboards that are accessible to all team members, as well as to management, become the lifeblood of the stakeholders. But how often have you presented and agreed upon QA dashboard only to be asked for  additional data points or for a dissection of the data in ways you hadn’t anticipated? Brickred System’s UReport is a single solution for all the challenges in measuring QA business goals, providing a holistic framework to organize and measure the increasing number of complex QA activities while providing management with the QA metrics and KPIs they need to measure performance. For more information visit www.brickredsys.com/ureport 

UReport Test Automation

Our client have need to run 4000 test case daily and due to frequent changes they had failure of 10 to 15% of automation test cases and to investigate same required 4 test engineers for 4 days of time to complete the work.

UTAP Test Automation

Our client is having multiple project and daily release for which test automation was getting executed in silos with different tools and technologies and each team wanted to continue with same eco system resulting in inefficiency.

Automation Testing

Improving existing mechanism for optimization/efficiency

Functional Testing

Improving existing mechanism for optimization/efficiency

Automation Testing-2

Improving existing mechanism for optimization/efficiency

Performance Testing

Improving existing mechanism for optimization/efficiency