In the past, software testing was a straightforward and simple process. It involved feeding the system with a particular input to measure the output and compare that with the expected results as defined by the user’s requirements. If the output matched the requirements, the test was deemed as okay, but if there was a difference then the testers would start the process all over again to fix the bugs.

Traditional testing methods require software tested to follow a checklist to ensure that all potential user activities are taken into consideration and if there is any issue, the same is resolved. Nowadays, the software testing ecosystem is quite complex and customer demand is increasing as well, leading to traditional methods becoming less effective than before.

Today, due to the extensive prevalence of software in mobile apps, IoT devices etc, the amount of data that is handled by software testers has increased substantially. It is important to note that this level of computing power is not only required for big corporations or large institutions, it is also applicable in the public domain. Our smartphones have the capabilities and potential for accomplishing more than some of the best computers a couple of decades ago. This has to do with the demand for a better approach towards the previous manual software testing methods. The ideal solution for these problems lies in the application of machine learning, artificial intelligence and analytics.

More Machine Learning, Fewer Errors, Is the Mantra!
Manual methods of traditional software testing to collect and analyze information and human actions are subject to error of judgment. The problem gets further compounded when there is less time to analyze the data. This further increases the likelihood of wrong and incorrect results as well as the failure to identify and remove all the bugs. As a result of this below satisfactory testing, it leads to poor customer satisfaction and creates a negative impact on brand reputation.

The adaption of machine learning applications in software testing has enabled software testers to derive rapid and accurate results compared to traditional methods. The use of AI drastically reduces the chances of errors as well as cutting down the total delivery time needed to detect and remove bugs. It should also be noted the accuracy is remarkable especially if you consider the amount of data that is processed as compared to the past.

AI is making it possible to use Predictive Analysis techniques
The current global market is very competitive. Businesses are always on the lookout for ways of staying ahead of the competition and anticipating the customer’s changing needs better than others. Predictive analysis in software testing plays a vital role in understanding and predicting certain features regarding new products and services as it improves the quality of data analysis.

Artificial intelligence and machine learning work together in the predictive analysis as a part of the software testing and quality assurance methods. These applications make it possible to improve the efficacy of testing techniques to finish testing quicker and provide a better customer experience.

Artificial intelligence is, without a doubt, becoming an integral aspect of quality assurance and software testing methods today. Software testers and data scientists alike are quite impressed and excited due to all the possibilities and technological advancements are offering to their trade.

Conclusion
Machine learning and AI tools enable software testers to better adapt and understand their customer’s changing demands. They make it possible for software testers to handle vast amounts of data while simultaneously cutting down the time required to generate results and provide more accurate results as well.

The adoption of AI in software testing is still evolving. Given enough time, the development of machine learning techniques will only grow stronger. Software developers and end-users will be able to enjoy the benefits of using AI techniques in improving software quality and this will likely generate more innovation and expectation for such methods.

It is important to become too optimistic too fast though. Till now, software testers were accustomed to using manual methods for testing and quality assurance. As a result, the challenge lies in bringing software testers on board along with AI-based applications while convincing them that these applications will not replace them eventually. A suitable solution towards dissolving such misconceptions is investing and training testers towards the application and usability of AI bots as a tool to assist them in increasing their efficiency while simultaneously reducing their workload.