How Will AI Change Software Testing?
The advent of artificial intelligence (AI) marked the beginning of an unprecedented techno-based world. Through its seamless automation processes, AI transformed almost every sector, including healthcare, finance, education, and so on. New advancements like Siri and Alexa have become an integral part of the lives of many.
But this is not all. Apart from all the day-to-day benefits that AI offers, it is soon going to revolutionize the software testing industry as well. Wondering how?
Read through this article to find out your answer.
A Bit About Traditional Software Testing
Until the past two decades, software testing was conducted manually. These consumed hours of effort still could not deliver comprehensive solutions. In this scenario, test automation emerged as a savior for testers by enabling them to test the software by putting minimum manual effort and yielding optimal results.
But if you are looking for the best results, even this might not be enough. A prime reason for this is that even test automation demands some time for test scripts and maintenance. Additionally, today’s applications and devices leverage APIs (Application Programming Interfaces), iframes, dynamic components, etc., which make the entire process complex and in some cases impossible to automate.
Artificial Intelligence emerges as a robust solution in this scenario. Through its exemplary features like applying reasoning, machine learning, and problem-solving, AI can aptly automate tasks and mitigate the consumption time in testing and development of software. In a World Quality Report, 90 % of organizations revealed that AI will be the next big thing in their testing processes.
Ways by Which AI Changes Software Testing
Although still in its infant stages, AI optimizes the testing process in close collaboration with automation. It is redefining the self-generating, self-adapting, and self-executing testing framework. Here are a few points explaining how it will do the same in coming years:
1. Tools will either advance or die: AI holds the potential to either upgrade or replace existing tools in an attempt to ramp up the testing procedure. This is because AI can not only test but also decode the areas of concern and offer a solution faster. As a result, the tools that are available now can either get modified via the machine learning software or get completely replaced.
2. Human and AI will collaborate in software testing: Companies will gradually deploy AI-based solutions to improve human tester’s efficiency to get maximum results in minimum time. But then there will be some who will be hesitant to leave everything entirely to artificial intelligence. They can find a pertinent solution through AI-led quality assurance that will allow them to use their present data to identify the weak areas. So, it does not mean that humans will have completely no role left with regard to testing. But yes, the participation will reduce. The below point will make their exact role clearer.
3. Humans will occupy the supervising role: It is well known that AI is highly capable of excelling at every task. But one cannot ignore the fact that its system needs to be fed with a set of guidelines so as to make it function in the desired manner. Who can do this better than humans? Human supervisors would thus be required to establish criteria, fix the testing patterns, and supervise the tests for an error-free testing process. One thing to note is that those looking to embark on this role should have a deep understanding of all codes of the software.
4. Reliability will improve: There are times when testers are unable to deliver the required results as they struggle to comprehend the changes in the application, such as resizing or renaming a field. Through self-healing test scripts, AI can read the changes, assess their pattern, and make the code more reliable by correcting errors. Thus, there will no more be the need to change the test every time developers make even the slightest change. The Continuous Testing Report reveals that 9% of respondents who participated in the survey deploy self-healing test scripts as one of the advanced use cases of their AI-based software testing.
5. Minimum resources will yield maximum results: Manual software testers often face hindrances due to inadequate resources, limited financial aid, and strict time limitations. Additionally, manual testing means recurrent human errors, extensive time consumption, and tough scalability. With AI-based software testing coming into existence, time would no longer be a hurdle. Additionally, reliance on AI would guarantee minimal errors and issues can be identified and addressed instantly, without bearing the burden of having plenty of resources.
Slowly but steadily, AI is entering and transforming the software testing landscape. Isn’t reduced costs, time, human errors, and scripting what we all search for? This buzzword is set to mark a new trend in the industry soon and put an end to the hassles of software testing in a seamless way.