For instance, Cognitive QA enables us to do in-depth analyses to ascertain both the exact causes of problems and their impact on the system. In a typical manual approach, root cause analysis (RCA) can take days to weeks to complete. The ‘root cause analysis report’ illustrates an automated RCA report. It allows to take deep dive by project, application, module with automated pareto distribution. This really aids in determining where further resources/time should be invested to resolve challenges. This report employs a quasi-Monte Carlo algorithm with ranking, inclusive of a few more NLP techniques. One can employ a time machine to rerun the analysis. This enables us to draw lessons from previous releases and fine-tune our strategy.
In the “what to test” figure, we examine how to increase quality with risk-based testing for every development build. Cognitive QA assists us in making decisions about which test cases to execute for a specific application or end-to-end flow. This can be accomplished through the use of a natural language processing (NLP) ranking approach.
Often, the time available for the test cycle is so restricted that we are unable to finish the full regression cycle. However, we are under pressure to create high-quality work. Additionally, we may add a timing dimension to the data and prescribe a fixed set of test cases to ensure the highest possible test coverage in the allotted time. The Cognitive QA platform can help us identify high-priority test cases within a project by assigning a rank to each test case. Test selection ranking is determined using the test selection score, which is calculated by combining the execution score, the failure score, the defect score, the recency score, and the risk score for each test.
A storage corporation in the United States was examining how to prioritize tests and configurations depending on the likelihood of risk and failure. The idea was to save time and make better use of available hardware.
- The platform recommended 325 high-priority test suites and 571 low-priority test suites out of a total of 896.
- Only 44 configurations were assigned a high priority rating out of 192.
A vendor of medical imaging solutions was considering modifying their technique for selecting regression tests based on different inputs. The recommended test list should be included into the cycles of automated test executions. The platform correctly suggested focusing on 1,664 regression test cases from the 13, 721 existent ones.
We must also consider the frequency with which test cases were conducted previously, the velocity of code changes, the interdependence of applications, test cases, and test data, as well as compliance, security, performance, and return on investment requirements. The figure "what to automate" assists us in prioritizing automated test cases. This can be performed by utilizing a natural language processing (NLP) ranking solution.
Having little to no visibility of risks and faults leakage in production is a recurring challenge, with direct impact on the business. For years, numerous methods have been employed to forecast problems with the high level of accuracy. The challenge is that no forecasting model can be simply estimated statistically from defect trends. It must also take into account code check-ins, requirement volatility, and a variety of other factors. The ‘defect prediction’ figure illustrates the range of the prediction model's output. Algorithms such as linear regression allow to reach 98% of accuracy.
Now we have arrived at the critical choice point of "to be or not to be." The one decision that can propel a business to the next level or can erode customer trust and have a negative impact on revenue and brand reputation. There is always debate over whether we test enough or we test too much, and eventually, how we can deliver products on time and with the highest quality. As the DevOps pipeline process matures, it's also critical to consider how quickly we can respond to errors that occur in production. Occasionally, we can take a gold build, stabilize it in a test environment, and ensure that all problems discovered are addressed through hotfixes. We may also immediately release products and hotfixes. When to cease testing gives you all the information you need to make the best choice. It gives you with all the information necessary to make an informed decision. It generates recommendations based on machine learning, natural language processing, and key KPI inputs.