How is the Future of Software Testing Improved with AI & ML?

Artificial Intelligence (AI) and Machine Learning (ML) have become the new buzzwords in the software development industry. Many AI and ML-based applications and devices impact our daily lives through drones, smart cars, smartphones, etc. One of the core reasons for integrating AI and ML in the software industry is to transform business operations and efficiently perform complex tasks. Today, AI and ML have matured to a level where they play a vital role in software testing.

The last decade in the software industry was about rapid delivery, and for that purpose, phenomena like CI/CD and DevOps came to the fore. However, while you’re delivering software at a fast pace, you also need to ensure high-quality and make sure processes are bug-free. That’s where the role of automation and regression testing comes into the picture. With the introduction of AI and ML in software testing, you can achieve automation more effectively and relieve testers from repeating and refining test cases.

Nowadays, many software testing methods leverage the power of AI and ML. There are many possibilities where AI and ML can play a definitive role in transforming software testing methods. Taking this into account, this article will discuss how AI and ML prove advantageous for software testing.

How AI and ML are useful for software testing?

Ensure consistent testing

Sometimes you’re confident of your test results but still would like to double-check the procedure. In this type of scenario, if you follow a traditional approach where there’s human intervention, then ensuring consistency in testing methods is difficult or repetitive. Instead, if you can use AI and ML, you can repeat the same procedure repeatedly without compromising the consistency of the process. Furthermore, AI and ML will also relieve software testers from conducting redundant tasks through smart automation.

Allows continuous testing

Every company has a dedicated testing department that ensures there are no bugs in the final product. The department can even work eight-ten hours a day, and during those hours, they have multiple tasks to handle. So, continuously being involved in repetitive tasks could be tedious for the testing team. Instead, using an ML-based testing system could help you establish a continuous testing pipeline and test the product across various environments.

Detect UI anomalies

The new age validation tools based on AI and ML can detect various UI anomalies that manual testing can easily miss. Also, software testers have so much on their plate that it will ease their work if AI and ML-based tools can handle such detection tasks. It will allow testers to focus on other core operations.

Ensure effortless testing

A software tester or a dedicated testing team in a traditional environment defines and executes the testing procedure. However, manually detecting all the bugs would be time-consuming, and one could waste a lot of time and effort. Instead of that, if you can involve AI and ML, you can easily automate testing procedures and save time that manual testing may not offer.

So far, we have seen how AI and ML can benefit software testing. But, the question is, what could be the impact of using AI and ML on the future of software testing? Let’s find the answer to this question in our upcoming section.

Impact of AI and ML on the future of software testing

With so much research being carried out to refine the algorithms related to AI and ML, it is evident to mention that AI and ML will get better with time. However, nowadays, you must conduct regression testing to check certain test cases repeatedly.

For processing those large datasets and automating the whole testing process, there is nothing better than AI and ML services. But remember, these services are not in place to replace software testers. Instead, they would assist the software testing team to make better decisions.

Here are a few impacts of AI and ML that would be visible in software testing:

• Faster turnaround time
• Refinement of software testing procedures
• Expansion of software testing scope
• Improvement in software quality
• Increase the accuracy of bug detection

These are some of the impacts of AI and ML in software testing. Now, let us explore the various applications and use cases it offers as well.

Applications of AI and ML in software testing

Monitoring and forecasting

One of the most common applications of AI and ML in software testing is for monitoring and forecasting results. Spunk is one such tool through which you can observe existing systems for necessities. In addition to that, it provides you with a machine learning toolkit to write and implement standard algorithms.

Rollback of test implementation

In a continuous deployment scenario, it becomes difficult to identify whether a test implementation is correct or not. Harness.io is one tool based on AI and ML through which you can analyze observational data and decide whether there’s a need to roll back the implemented tests.

Detecting mobile app crashes

There’s an application called NimbleApp through which one can detect mobile app crashes and the reason behind them. Then, based on the information provided by the tool, test engineers come up with valuable test cases that enable them to deliver a robust product to the people. Such tools and practices use AI and ML to analyze bottlenecks.

Automating test cases

Consider a situation where you have to run a regression test for your product, and for that purpose, you need to create multiple test cases on the go. The features also keep updating with time, and you need to add more test cases. This task becomes tedious for software testers. But, with ML-based tools like ProdPerfect, automated test cases generation becomes easier.

These were some of the practical applications of AI and ML in the testing horizons. However, if you want to integrate such practices into your testing ecosystem, then you should know the latest trends. Our next section focuses on AI and ML-based testing automation trends for 2022.

AI and ML-based testing automation trends for 2022

Following are some 2022 testing automation trends:

• Blockchain testing
• Cloud-based cross-browser testing
• Accessibility testing
• Performance engineering
• DevOps and agile-based testing
• Automated testing of cloud services
• Risk compliance and security testing

The question that remains is, what challenges would AI and ML address in software testing? Let’s try to decode that aspect in our next section.

Challenges that AI and ML would address

• AI and ML would decrease the need for large-scale manual testing. Finding high-quality manual testers is not that easy. AI and ML would reduce the need for all that.
• AI and Ml helps to optimize the test cases as one can learn from the data present in the QA ecosystem. In addition, it will refine the testing procedures.
• AI and ML can also help you predict the probability of failure when changes are made to the code. But, again, this is because AI and ML are self-learning technologies.
• These trending technologies would help you to increase test and code coverage. The reason is their ability to traverse many paths within a few seconds and self-learn.
• With the help of AI and ML, the generation of test cases will be faster and efficient compared to manual testing. So, scaling up test cases with AI and ML won't be hard.

These were some of the burning questions that AI and ML would be able to solve. However, there is a discussion going around the industry around the future of manual testing.

Will this be an end for manual testing?

The rise of AI and ML is a milestone moment for the software development industry. People have started adopting advanced practices with time. However, manual testers are slightly concerned about their role in this scenario. They feel that this will put an end to their bright careers. As far as the future is concerned, there will be a paradigm shift in software testing with the introduction of AI and ML. But, there will be human intervention required to strategize the testing procedures and define a roadmap. So, there won’t be an end to manual testing. It will coexist with AI and ML-based testing. It’s just that the role of a manual tester would change for the better slightly with time.

Conclusion

Software testing has a new dimension with trending technologies like AI and ML. It’s becoming more accurate and efficient than ever before. The software testers no longer have to worry about dealing with repetitive tasks. Instead, they have a role in defining procedures and setting up a road map for the testing processes. The primary aim behind integrating AI and ML in the testing ecosystem is to automate repetitive tasks that lead to wastage of manual time and efforts in large.

Like every other technology, you need to deal with a few concerns and challenges to use AI and ML in software testing. But, the benefits are far greater than challenges, and it should motivate you to pick up this trend. We hope you have a holistic view of AI and ML in software testing and its future now. What are your thoughts on this article? Do let us know in the comments.