Enterprise leaders acknowledge that software testing will be a significant part of the budget set aside for the investment
Software testing is one of the most expensive segments of the software development lifecycle. The step is critical to ensure that the product works accurately and as planned. Larger corporations have complete teams that are dedicated to testing alone. However, for smaller development teams, a larger testing team is far beyond the available budget. Simultaneously, the current market requires rapid development phases that leave minimal time for proper testing.
Organizations of all sizes are looking for methods to decrease costs and boost the reliability and scope of testing, and at the same time, deliver software that satisfied client deadlines and expectations. Under such pressure, testers have turned to AI and ML to augment the testing processes used at their enterprises.
CIOs believe that quality assurance and AI are a perfect match.
Reducing the software development lifecycles
Organizations are continuously releasing software that are regularly updated by engineering teams. In the current times, the lifecycle of software has constantly shortened and become more complicated. The sudden rise in the popularity and deployment of third-party APIs, microservices, and other software solutions leaves most developers building software with numerous dependencies, all of which require testing.
Finding balance with ML and AI
CIOs agree that there exists a struggle for software development teams, between the pressures of meeting deadlines and developing software. Developers must achieve the milestones and deadlines agreed upon by executives and clients. So, it’s not a surprise that innovations in AI and ML technologies are now being implemented in software testing to improve the cost of testing, accuracy, and pace of the complex releases.
Avoiding delays with regression testing
Enterprise leaders point out that regression testing is one of the most time-consuming areas of the testing procedure, but this testing is critical to ensure that new capabilities, patches, updates and other changes/modification do not introduce new bugs into the existing code.
Such regressions are common when the code is poorly documented, or the developer turnover for the particular project is high. End-users and clients can demand changes to existing capabilities that seem easy on the superficial level, but need significant restructuring within the code, increasing the risk.
CIOs say developers are pressured to ensure that they don’t introduce sudden breaking changes anywhere in the codebase. However, when the testing is blended with the need to provide small updates at a rapid space, regression testing soon becomes a bottleneck for the overall testing process.
Future of software testing
Innovations in the ML and AI technologies are focused on increasing the number of tasks that can be automated and decrease the time needed to complete the tasks, by prioritizing them, dynamic resourcing and running the tasks in parallel.
Of course, various programming languages are capable of integrating processing support into the language core itself. It could potentially revolutionize the concept of software testing that was traditionally a highly time-consuming and costly process.