How AI is Transforming Software Testing

AI is transforming software testing

The knowledge domain of software industry is ever evolving without any dull moment of inactivity. As per one of the growing trends, the professionals in the field of software testing now believe more and more that in future the domain will see increasing automation of the current manual testing processes. The time now devoted to actual software development is expected to come down and instead, more time and resources will be required to test the mobile and hybrid software applications.

But, of all the developments the one that is expected to change the field of software testing is the growing use of machine learning.

With more and more mobile apps now controlling home appliances to virtual reality, digital applications are slowly covering all areas of human activities. The software development teams are now using rapid delivery methods to make it possible to launch new products every week. The increasing number of apps and devices are increasing the need to test software before launch, something that is becoming almost impossible using the traditional manual testing methods.

In the software development, tests are required at every stage. Through machine learning techniques, pre-defined set of instructions can be created to generate the required test data thereby eliminating the need of manually filtering out the information. Also, by feeding the data into the AI machine, many tests can be performed in a complex test environment to ensure better reliability and security of the software in real life situations.

Software testing Principles

How AI is Transforming Software Testing

Let us understand how artificial intelligence is affecting software testing. Is it making it better?

1 Use AI else Employ Superman!

In the past, software testing used to be relatively less complicated and simple. It mainly involved giving a particular input and measure the output and compare it with the expected results that were defined by the user requirements. If it matched, the test was taken as ok, but if a difference was observed one used to start all over again to fix the problem.

In traditional testing methods, the Software tester used to follow a checklist to make sure that the potential user activities are all covered and if any issue arises the same is resolved. But, today the ecosystem is complex, and customers are becoming more demanding, thereby making the traditional testing methods less effective than before.

Today, with extensive use of software as mobile apps and IoT, the amount of data required to be handled by software testers has become many folds. The computing power today is not limited to some big corporates or large institutions. It is now available in the common domain. Instead, the smartphones that we are currently using are more potent that some of the best computers of the past. All this is creating a demand for a better approach to the problem of decreasing effectiveness of manual software testing methods. The solution to this problem is coming from the upcoming field of machine learning, artificial intelligence, and analytics.

2 More Machine Learning, Fewer Errors, Is the Mantra!

The traditional software testing methods heavily rely on manual methods to collect and analyze information, and human actions are prone to error of judgment. The problem is further compounded with less and less time to analyze data, thereby increasing the chances of wrong results and failure to identify and remove all the bugs. Such below-par performance finally leads to poor customer satisfaction and negatively impacting the brand reputation.

But the methods using machine learning are now enabling the software testers to get more accurate and faster results compared to what they could achieve using the traditional manual methods. AI not only eliminates the chances of error but it also reduces the total delivery time required to remove bugs and not to forget that all is being done handling multi fold data compared to past.

3 AI is making it possible to use Predictive Analysis techniques:

In the current cut-throat competition, the businesses are always under pressure to be ahead of the competition and understand the customer evolving needs better than others. To better understand and forecast the features of new products and services, predictive analysis is playing a vital role as part of software testing techniques by improving the quality of data analysis.

The Artificial Intelligence and Machine Learning go together with predictive analysis as part of software testing and quality assurance methods. They are making it possible to improve the efficacy of testing techniques to complete testing faster and get enhanced user experience.

So, it can be said that without a doubt that artificial intelligence is fast becoming an integral part of the quality assurance and software testing methods of today. The professionals in the field are quite excited about all the new possibilities that such developments are bringing to the trade.

Conclusion

Machine Learning and AI techniques are enabling software testers to understand better and adapt to ever-changing customer needs. They are now able to handle more data in less time and giving more accurate results.

The AI is still evolving, and with time the maturity level of the machine learning techniques in software testing will undoubtedly grow. The software developers and final users will be able to see the enormous benefits of deploying the AI techniques in improving software quality thereby raising the expectation of a bright future for such methods.

Amidst all the brouhaha about AI utility for software testing, one should not forget that it is finally a tool in the hands of testers who are till now accustomed to using the manual methods. So, the need is to take them on board and not to create a challenge by projecting as if the AI methods are going to replace them, which eventually is not the case. Instead, the organizations need to invest more to expose and train testers to better use AI bots as a tool to assist them by reducing their workload and improve the work efficiency.

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