September 23, 2022
QA plays a vital role in the software development and testing process. The main idea of software testing and quality assurance is to find ways to prevent bugs in the software development process. In this blog, we’ll let you know how AI is enabling automation in QA, making it more efficient and effective!
In today’s dynamic and fast-evolving digital marketplace, a tech company has significant competitive advantages when it can deliver quality products to the market faster than the competitors. On the other hand, the complexity leading to errors - of technical products has increased heavily. Thus, Software testing and Quality Assurance need to evolve for meeting the constantly changing demands of speed to the market and should also ensure great customer experiences.
The interesting fact is that in times of increased complexities, software testing and quality assurance services can always create a bottleneck to success. This is because a majority of quick testing is still adjusted towards manual testing and automated test scripts.
Software testing and quality assurance - efficiency and effectiveness are the two metrics that determine the overall advancement of a test strategy. Artificial intelligence and machine learning in testing primarily focus on these two measures.
It is estimated that over 60% of the test cases in any industry test case portfolio are repetitive. AI identifies such redundant cases that are logically as well as physically similar and eliminates the duplicates that aren’t adding any value to the business and the removal doesn’t decrease the business risk coverage. AI is also capable of maximizing the detection of defects and risk coverage followed by minimizing the execution timings, costs, and the number of test cases by identifying the optimal sets. In addition to these, AI has self-healing automation properties i.e. it can heal the broken automated test cases and make test automation flexible to changes.
In short, AI makes software testing and quality assurance smarter and promotes increased efficiency and effectiveness.
Listed below are all the important functions of AI in software testing and quality assurance:
Automation has existed in software testing and quality assurance for years. But, the benefits of automation were not very impactful for the business to notice.
In the first generation automation, the focus was UI-based and on regression. The idea was to build a framework that accelerated automation using commercial tools. The evolution of automation was to include data and keyword-driven and lately, business process-driven frameworks that would bring vital savings to clients. But these savings were limited to regression and didn’t bring many changes to the business.
The next wave of automation included the operational side of the business in the form of test data automation, middleware automation / API, etc. This generation of automation truly brought the value of automation into testing executions and other testing activities. The focus was shifted from UI-based automation to multi-tier automation, making a remarkable impact on time-to-market and efficiency. This wave of automation is now evolving more with an increased focus on continuous testing.
AI-led intelligent automation combines the best approaches of automation with AI and helps in getting superior results. The focus is three-dimensional i.e. eliminating test coverage overlaps, optimizing efforts with improved predictable testing, and lastly, shifting from defect detection to defect prevention. Today, organizations have improved machine learning algorithms for processing huge volumes of data that results in better run-time decisions and pattern analysis.
For case, during a software up-gradation, machine learning algorithms can traverse the code to detect fundamental shifts in operation and link them to the obligations to identify test cases. This facilitates optimizing tests and blocks the making of judgments on hot spots that could lead to failures.
Software testing and quality assurance is a cost-intensive and time-consuming activity. A challenge with consecutive test automation in QA is that by the time test codes are completed, the requirements start to change and applications start to evolve with regards to business functionality and User-interface. This means that all the efforts invested in developing the test codes are wasted and you need to adapt the test automation needs respectively.
Often, the software quality assurance service teams find themselves in this test-automation trap.
AI can be leveraged to solve this situation and speed up manual testing. For instance, test cases may be prioritized based on the already existing test cases and logs so that the QA team can focus on investigating malicious functions. Further, AI agents can also learn and develop themselves throughout the testing process they will develop after modifications in the code base, discovering different application functions without human interference.
Artificial intelligence is an indisputable reality of this era. The use of AI inputs many positive results, including a decrease in cost, risk-detection, increased profitability, and customer satisfaction. With AI and Intelligent automation solutions, businesses can re-energize their core and make every process more efficient and productive.
If you are looking for software quality assurance service providers, you are at the right place. Consagous technologies provide you with the absolute best software testing and quality assurance services. We have been delivering solutions to companies all over the world, for more than a decade.
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