The technology of Artificial Intelligence (AI) can be effectively utilized in identifying problems in software. It can help predict and predict the occurrence of such problems, so it greatly supports the Quality Assurance (QA) process.
Today, businesses are all embarking on transformation, digitalization to achieve a range of results: improving productivity, efficiency, cost savings, and improving ROI. In an effort to modernize IT infrastructure, businesses often ignore or pay little attention to quality assurance regulations – QA. However, with the methods of Agile and DevOps, which will focus on providing quality products or services with a shorter release time, QA always slips into any business development strategy. The pressure of having to release in a shorter period of time required the adoption of automated testing, for traditional models it was no longer suitable.
Does that make it easy to automate testing? No. Challenges arise from the diversity of device platforms, operating systems, networks, as well as the introduction of technologies such as IoT, Big data, cloud computing and other technologies. So what is the exit to the above problem? How to ensure the quality assurance process, provide products or services with no errors or gaps? Let’s embark on AI-based testing. This sounds confusing right? Currently, AI has infiltrated our daily lives as intelligent assistants like Siri (on IOS) and Amazon’s Alexa. In addition, the World Quality Report – The World Quality Report emphasizes the need to use machine-based intelligent solutions to overcome QA and test challenges and implement strategies based on risks. ro. Therefore, AI-based testing can help automated and regression testing become smarter, better and faster.
Are AI and Machine Learning (ML) the death knell for Software Testing?
The answer is NO, because AI and ML can improve software testing quality by making the process of identifying errors faster. However, despite the advantage of AI-based testing: automated testing and delivering accurate results, people still hold the key to such aspects as scalability, performance, and another aspect.
How can AI and ML shake things up in Quality Assurance?
The centrality of Continuous Integration (CI) and Continuous Delivery (CD) in the DevOps diagrams represent Quality Assurance, including test automation and processes. manual, supported. This can only happen by performing test automation with AI. Let’s discuss the benefits AI can accrue to existing QA processes.
Speed up manual testing (traditional manual testing) and other processes:
Manual testing for a software may take days. Besides, this test can be expensive in both money and time. However, with AI it is possible to eliminate such tests by writing scripts – scripts and analyzing large amounts of data faster. Easily, the AI can sort through log files to improve the quality of the code. Despite the great benefits, AI cannot replace manual testing because traditional methods are required to design test strategies. In the future, manual testing as well as testing with AI will coexist.
Test automation – Test automation – ready:
As software development becomes complex with APIs that interfere with a multitude of contacts, the chance of errors during construction and testing is enormous. This is an opportunity for testers to overcome these problems by testing multiple test cases for different scenarios and including countless variables. Test automation can facilitate regression testing and be more effective by identifying a large number of problems in less time. Therefore, in the development and testing environment based on DevOps which is an integrated demand and continuous testing, test automation can be considered as the best choice for quality assurance team. However, with automated testing, the requirement to create test scenarios with reporting mechanisms can be a challenge. At this point, AI testing can help us write complex test cases quickly.
Prevention of deficiencies:
AI application testing services allow us to look up historical data for problems encountered in log reports. Through this, guide Dev on areas where errors are more likely to occur. They can combine best practices to deliver superior results within the following three aspects. One is to eliminate the overlap of the test scope. The second is to provide more predictable test results and ultimately move from fault detection to prevention. By analyzing patterns and processing huge amounts of data, developers and QA experts can make better decisions in real time. For example, the ML algorithm can analyze a set of code during a software upgrade to identify major changes in functionality. Algorithms can easily relate changes to test cases and optimize the QA process.
Even if you want to build the best product, follow the rules, the protocol … the error is inevitable. Even with automated testing, the bug identification is still a nightmare, because it can only help detect errors if the script has the same conditions. On the other hand, using the framework of AI testing can get answers such as where the error is, how the error occurs and when it occurs within seconds. The answers can help developers understand whether they need to make code changes to prevent these errors or apply other methods. In fact, AI can perform real-time analysis of problems while a piece of code is being executed.
AI testing services can analyze the impact of software problems without the participation of QA experts. They can screen for the relationship between different elements in a software if something goes wrong. AI can help QA experts and development teams prioritize problems so they can be addressed, including irregularities in their processes that have led to problems appearing in the first place.
Conclusion: QA experts and developers can effectively use Artificial Intelligence (AI) to predict the occurrence of bugs / vulnerabilities or form an effective trio in solving them. . Technology can be more handy in ensuring the software of the future is still superior in quality.