The word AI is making quite a stir in recent days and is being perceived in many ways, both as a threat and a treat. The threat is, AI would go on to make some of the careers vanish by helping businesses rely more on automating day-to-day things and thereby eliminating human involvement in those jobs that AI may seem to predate (assumed by most) and possibly take control over humans(?). On the flip side, technological advancement seems to be breaking its previous heights.
Artificial Intelligence(AI) in Software Testing :
The need for testing software to validate whether it confirms to the functional and non-functional requirements of the customers, has reached another level of significance as a minor glitch could do serious damage to the goodwill of the people involved and there could be financial implications too. So as long as software is going to be developed, software testing will exist in any of these forms viz. Functional, Performance, Security and so on.
So, what is being spoken about the advent of AI and how it would impact software testing. The short answer is, it’s mostly being perceived as a threat to software testing as a whole with the wrong notion that, AI would make testing extinct, by it becoming an integral part of the software development life cycle and contributing for the betterment of the respective streams. But the industry experts think otherwise, at a higher level they stress on the point that the use of AI tools can enhance the software developers and testers alike, there’s some relief.
How AI can help testing teams:
- Accelerating Timelines :
Testers need not exercise the application consisting of thousands of lines of code, AI can be used to quickly scan code, and detect errors much faster.
Additionally, AI can be made to perform repetitive tests, and let testers focus on new features, also AI does not get exhausted nor will make human errors, hence more accurate results. - Stable releases :
AI can be used to identify risky and complex of business functions and help teams make Go/No-go decision-making based on the stability of the potential release candidate. This increases customer satisfaction as every release that customers get is stable and doesn’t have glaring business functionality issues. - Highlight risks :
AI can be used to identify potential failure locations and gives teams insights into functions requiring more testing. Further, AI can use information from past events for the application under test to validate the initial findings. - Reduced test design effort with better coverage :
Test design is the key task every testing team performs, and lion’s share of effort goes into this activity, so AI can be utilized to help identify test scenarios and help testers with faster test design and better test coverage. AI can also help teams save execution time by eliminating duplicate test cases. - Mastering Test Automation with AI tools
Ever since its launch, ChatGPT has taken the world by storm and still has umpteen number of professionals in the waitlist to get their hands on it. Amidst all the hoo-hahs and criticism, the vast majority of technology professionals seem to be lauding the benefits it brings to the table.
Automation in combination with DevOps lends a helping hand to testing teams, by reducing the manual effort in deployment and test execution, ChatGPT can be used for generating code snippets/samples for test scripts, debug and review test scripts.
Not just ChatGPT, there are other tools like Applitools Eyes and Browserstack Percy which are AI based visual testing tools which are already gaining popularity, Functionize self-healing and mabl auto-healing are tools which use AI and ML to fix test scripts when the application under test’s code changes. The examples mentioned are just a few, there are other tools being used and are yet to attain wide popularity.
