By Visakh ST, CTO, Simplify3X
Software testing continues to evolve owing to the constant pursuit of quality. Earlier, testing was a manual process, with testers being required to methodically develop and carry out test cases line-by-line. This technique was sluggish, prone to human errors and unsustainable at scale, particularly as software complexity advanced.
Today, the development of test automation has reached an intriguing chapter with the introduction of artificial intelligence (AI). It has transformed testing automation by intelligently creating and sustaining test cases, identifying errors, automating repetitive processes, collecting test data, analysing test performance and delivering comprehensive results. In many respects, artificial intelligence has elevated software testing to new levels of efficiency and effectiveness.
AI and ML in software testing
As technology continues to expand itself into various facets of computer science, artificial intelligence and machine learning can be implemented into various stages and features of software testing. Nowadays, software testers may employ AI/ML to accelerate testing processes by maximising these technologies’ human-level decision-making skills. Consequently, this may provide significant advantages to QA teams who know how to implement it effectively. Although AI/ML is not an entirely novel technology, it has advanced considerably in recent years, and there exist several instances and applications for its utilisation.
AI/ML test use cases
There are several ways to employ AI/ML to enhance software testing, and the key to unlock those capabilities is to understand what these technologies may potentially accomplish and then discover innovative methods to incorporate them into day-to-day testing operations. Here’s a look at some of the ways in which these technologies are transforming the testing process efficiently:
Test case recommendation: Machine learning’s value lies in its capability to learn from data and generate predictions without being explicitly programmed for every possible scenario. This implies that as time passes, AI develops a better understanding of how people engage with the app on which the tester is working. It continues to modify its test creation process to comply with the specifications set by the company. Furthermore, it determines the standard ways by which a tester examines things by analysing the rules, existing tests, and historical data before recommending the best test cases for the given situation.
Predictive analysis: Keeping track of test script updates after modifications to the application can turn into a hassle. AI excels at identifying patterns in massive datasets. Using AI and ML technologies, testing teams can analyse code changes and automatically update test cases to react. These technologies also assist in proactively analysing product usage and faulty data to identify where the product is most likely to encounter issues. AI then recommends regular testing of certain locations to detect flaws before customers discover them. This predictive strategy of testing enables organisations to stay ahead of maintenance requirements and provide higher-quality software.
Faster development cycles: AI is propelling the growth of low-code test automation technologies, which make test creation accessible to non-technical users. With a low-code testing product, anyone can create automated tests by demonstrating the steps required on a real mobile device. AI then develops a reusable test script that can be executed on several devices. Moreover, low-code tools enhance the capacity of automated testing across the organisation.
Debugging: AI can effectively identify errors based on patterns and defect records, conduct root cause analysis, pinpoint the source of the issue and provide alternative solutions. Implementing an AI-driven autonomous testing system can accelerate the entire software testing process, resulting in increased productivity, accuracy, and cost savings.
Future of software testing with AI
With the ample advantages that AI/ML offers, the future of software testing looks promising. However, it is important to highlight that AI-powered testing solutions are not intended to replace human testers, but rather they will support and enhance their existing knowledge.
In the coming years, users can expect to see increasingly powerful AI algorithms and machine learning models that can analyse complicated code and data to detect even minor faults and vulnerabilities. According to a study by Fortune Business Insights, it is anticipated that the global AI-driven market size will grow to 2746 billion by the year 2030 at a CAGR of 20.7 per cent during the forecast period. This projection is entirely focused on the capability of AI-powered testing tools that will become increasingly advanced as they continue to learn from perpetual input, strengthening their accuracy over time.
As technology evolves, the implementation of AI in software testing will become increasingly crucial. Organisations that embrace this technology and invest in resources and training will be well-positioned to succeed in the forthcoming digital landscape.
