Artificial Intelligence (AI) is one of the most recent buzzwords in the software development industry. The use of AI in software development is still in infancy and the level of autonomy is less as compared to other areas such as voice-assisted control or self-driving systems. However, AI is accelerating in the direction of autonomous testing. Application of AI in software testing is focused on in order to simplify the software development lifecycle. By applying problem-solving, reasoning, and machine learning, AI can help to reduce the amount of tedious and mundane tasks in software development and testing.
AI as a New Age Testing Tool
With shorter delivery time, software development lifecycle is getting complicated. This is also resulting in the need for instant feedback by software testers to the developers to release error-free product on time. This calls for adopting changes and becoming smarter. Introduction of AI in software testing is likely to make the testing process faster and better, as AI with the help of machine learning can write its own line of code by stealing it from existing software. Use of AI can make test automation faster, smarter and better, this can also help software testers to focus on planning and implementing better strategies.
What About Machine Learning in Testing?
Machine learning can add to the AI with the application of algorithms that can help testing tool to improve by collecting an extensive amount of data produced by the testing. Data collection is key to the decision-making process and machine learning can be extremely valuable as it is a subset of the overall AI research that focuses on decision making based on the data collected. Machine Learning can help testers to analyze more data in less time, thereby, decreasing their margin of errors constantly. Three types of extensively used algorithms are supervised learning, unsupervised learning, and reinforced learning.
Impact of Artificial Intelligence and Machine Learning in Software Testing
AI and Machine Learning will likely broaden our horizon and opportunities. Some of the changes expected to be seen in software testing with introduction of AI are boosting accuracy, expanding the overall length and scope of testing, ensuring faster turnaround, and enhancing the quality of the software. The AI-powered testing platform will be able to recognize changed controls faster and efficiently along with the constant updates to its algorithms, even slightest change can be observed.
AI techniques in software testing have started becoming feasible and soon will become part of the quality engineering and software development process. AI in the quality assurance scenarios can conduct ongoing analysis of the error and can find answers to the questions like how, when, and where to eliminate the chances of bug. Testers can use this information to decide if any changes in codes are required to eliminate program errors or there is a need to apply some other approaches. AI will make software testing more efficient and play an important role in the future of software testing.
Is AI the End of Manual Testers?
With AI in software testing, there will be no need for testers to update test cases manually and identify controls, spot link between components and defects in an effective manner. AI is creating uncertainty among manual testers with respect to their jobs. However, manual testing is expensive in terms of both time and money, while, AI can tackle such time-wasting problem for developers by writing all the scripts and analyzing a large number of data sets faster. It is likely that in future both manual and AI software testing will co-exist. However, software testers will need different skill sets to survive, they will need to understand how AI and machine learning works.
AI and machine learning have become an important part of software testing that gives the testers an opportunity to better understand customer needs and compare them to the features in the software and makes changes faster with the changing customer expectations.
Businesses and enterprises no longer ignore the potential of artificial intelligence and machine learning for the software development ecosystem. Some of the big companies are using these technologies to enhance their decision-making process, carry various complex task and reduce errors, improve response time. Implementing AI and machine learning in software development and testing will need to revamp the testing cycle and training or reskilling the resources to work with AI.
AI can significantly improve quality function with benefits ranging from better quality to speed to market, optimization, traceability, and remarkable saving on the total cost of the development. Businesses need to understand how better they can utilize the power of artificial intelligence and machine learning in order to optimize their software testing efforts. Whether manual or automated, software testing can immensely benefit from AI and intelligence derived analytics and data.
Ritesh Patil is the co-founder of Mobisoft Infotech that helps startups and enterprises in mobile technology. He loves technology, especially mobile technology. He’s an avid blogger and writes on mobile applications. He works in a leading android development company with skilled android app developers that has developed innovative mobile applications across various fields such as Finance, Insurance, Health, Entertainment, Productivity, Social Causes, Education and many more and has bagged numerous awards for the same.