5 reasons to integrate AI into the process of Software Testing

Artificial Intelligence

 

To meet the digital transformation goals where DevOps and Continuous Delivery are the key paradigms, businesses have their task cut out. The growing competition has led companies to innovate fast and deliver products and services that are free of glitches. This means assessing risks on a real time basis across the SDLC by leveraging advanced technologies such as Artificial Intelligence and Machine Learning. Also, the availability of big data and computing power has led the IT thinktanks in business to consider AI as a strategic investment.

To be a digital disruptor in your domain and hold a competitive edge, there should be adroit management of the customer experience. This can, however, be a difficult proposition given the broad spectrum of software testing. Today, a software application or system interacts with myriad digital touchpoints through APIs in a nonlinear fashion. Also, the digital environment is witnessing rapid changes driven by technologies such as IoT, quantum computing, and robotics among others. These developments have created a challenge of sorts for digital testing necessitating the use of machine-based intelligence.

How can Artificial Intelligence help QA?

With the complexity of applications, interfaces, and digital environment increasing, AI application testing can be used to conduct a plethora of tasks such as:

• Building and optimizing test cases
• Selecting the right test cases for automation
• Carrying out predictive analysis
• Speeding up processes conducting routine analysis
• Determining outcomes beforehand for complex test cases

Is AI in software testing going to be the new normal in QA?

In traditional QA, the modus operandi involves looking for an expected output after putting in the data manually. Even when test automation is introduced, some of the tasks like input of data are driven manually. However, with AI application testing, the software is made to understand the input data and predict the outcome accurately. The best part of involving AI testing services is the felicity with which the machine or tool carries out testing in various ways. Moreover, should there be any code or UI change, AI can direct the testing tools to handle it in a seamless manner. So, be it creating test codes, anticipating outcomes, handling code changes, or predicting glitches in thousand lines of codes, AI can deliver the goods with significant time and cost savings.

5 reasons how AI can significantly enhance QA

#1. Automating the QA architecture: The complex scenario of QA testing makes the identification of test processes a difficult exercise. Since not all processes can be automated, it is a challenge to choose the ones that need to undergo automation. Also, every test case comes with numerous dependencies, especially between attributes and instances. Handling such dependencies with information being added, deleted or modified due to a change in business logic can be a nightmare. A robust AI testing framework, on the other hand, can build appropriate test cases as per the defined rules serving twin purposes – pre-empting the creation of meaningless test cases and creating test cases to plug gaps in the risk coverage area.

#2. Optimization risk coverage: QA experts are often clueless about the risks associated with various processes and their touchpoints. To achieve the best results in testing, the QA exercise should make use of AI testing services. The best results can cover outcomes such as identifying the maximum number of defects, minimizing the duration of testing, reducing the number of test cases and resources, and increasing the risk coverage area.

#3. Identification and resolving test failure cases: Test automation can often lead to an increase in the number of failed test cases, especially for regression tests. This can happen when the rate of test automation, the frequency of testing, and system complexity show an increase. Other reasons showing a test case failure can be due to the unavailability or slowdown of a third-party API or any change in the test application breaking a business-critical functionality etc. Diagnosing the root cause of such test case failures can be time consuming unless AI is taken into consideration. Using AI in software testing can quickly identify and eliminate the reasons for such failures.

#4. Reducing the burden on QA experts: The Agile and DevOps framework entails creating a sprint wherein QA experts identify glitches post the development of a code. However, artificial intelligence app testing can be leveraged even by the development team to do a preliminary test screening. This can reduce the overall turnaround time as developers are able to identify and eliminate glitches in the codes beforehand. Moreover, they can even modify the codes to pre-empt bugs from sneaking in.

#5. Test simulation: AI testing can simulate any number of test users to validate various functionalities associated with an application. This can help in testing each and every test case and its interface with various digital touchpoints leading to better identification of glitches.

Conclusion

AI can influence new areas of testing such as predictive analysis, intelligent automation, and design thinking leading to quality engineering. It can help enhance business agility and competitiveness.

Diya works for Cigniti Technologies, which is the world’s first Independent Software Testing Company to be appraised at CMMI-SVC v1.3, Maturity Level 5, and is also ISO 9001:2015 & ISO 27001:2013 certified.