#testing ai systems

Testing AI application is very different than traditional software testing. Here are some of the key points on How to Test an AI system?

The spectrum of software testing has broadened considerably. This is due to the fact that the software of today has interfaces with other software, systems, networks, and platforms through APIs. The systems mentioned herein are the various devices (mobiles, tablets, laptops etc) or legacy machines of various make, configuration and resolution. The complexity arising out of such an arrangement renders the testing of software a tricky affair. Importantly, the use of test automation under the Agile and DevOps framework has helped matters to some extent with an increase in the test coverage area, better identification of glitches, accelerating the time to market, and realizing the ROI.

Quality assurance can pre-empt the failure of software, be it in terms of performance, functionality, security, or usability. It has become the pre-requisite for digital transformation for it helps the software application to not only meet the business objectives but deliver a rich customer experience as well. The latter has become an important outcome should businesses want to stay in the competition. However, to ensure greater quality compliance, enterprises are increasingly looking at Artificial Intelligence and machine learning as enablers.

The objective of leveraging AI is to write and execute automated test scripts through QA and move away from the process of manual testing. The latter needs to be done away with given its glaring inadequacies. In fact, AI can help execute complex and iterative testing processes with ease and relieve the testers of their workload. The testers, in turn, can work on other pressing issues concerning software development. The various ways in which AI can add value to the quality assurance efforts are as follows:

Helps in test automation: The competitive market dynamics requires a software product to be delivered to the users before others. To achieve this objective, businesses are releasing software without subjecting it to a proper software quality assurance process. This approach, unfortunately, can break the trust of users should the software contain glitches.

Besides, many IT companies do not want to stretch their budget to accommodate QA software testing given their misplaced business priorities. Since most of the software quality assurance processes involve repetitive testing of inputs and their dependencies, they often leave a lot to be desired especially when used manually. This is where artificial intelligence can help by automating the testing processes and thereby enhancing the accuracy of software.

Facilitates testing: The powerful analytical tools of AI can help in the writing of automated test scripts and derive suitable outcomes. The AI test algorithm can examine a large number of files and sort the relevant areas for testing. This way it can save time, cost and improve the accuracy of the codes. Moreover, AI can predict glitches in the codes based on their thorough analysis. It can help the development and testing teams to focus on areas where the chances of glitches from getting into the system are more. This approach can prevent cost escalation, which otherwise would have arisen due to rework.  

Evolve a bug proof system: Software testers are often at their wits’ end to identify bugs and the point at which they entered the codes. This is mainly due to the lack of due diligence and painstaking QA software testing processes they ought to follow. The AI algorithm can accurately predict and identify the source of bugs. Its incisive and comprehensive analysis can help developers to carry out modifications in the software development process to prevent the entry of glitches. Moreover, since the AI algorithm can run virtually non-stop, it can identify and eliminate bugs, which manual testing is simply not capable of pulling through.

Helps to improve continuous testing: AI can easily create various sets of test data and perform software quality assurance testing at various stages of software development. Thus, each and every code gets into the testing rigmarole (not always the case in manual testing,) which in turn helps to identify hidden glitches. Since AI can perform end to end QA and integration quickly and consistently, it results in enhancing the quality of the application in terms of functionality, reliability, security, and performance.

Conclusion

AI can augment the efforts of QA testers on a scale that is simply not possible to achieve otherwise. AI bots should be utilized as test enablers by the testing teams to reduce their workload and enhance the quality of the software development process.