Friday October 27, 2017
Software testing has always been an integral part to the success of any product in the market. Be it a newly launched product or the latest version of the existing product, it is important for companies to make sure that each of its product passes through a stringent quality test to make sure that it meets the set standards.
Gone are the days when companies used to rely on manual testing. With the advancement in technology, the companies have almost switched completely to the automated mode of testing.
And, now the day is here when more and more companies will be seen using artificial intelligence (AI) in its software testing process.
AI has become the new buzzword as more and more companies are getting increasingly dependent on machines and computers to perform tasks that would have otherwise been performed only by humans.
This form of intelligence has simplified a lot of tasks and one of those is testing. All one needs to do now is feeding the computer with the required data, setting the required logic and simply running the testing process.
Shortcomings of Manual Testing
Although considered to be a reliable form of testing, manual testing has its own cons. These, in turn, reduce the efficiency and performance of the entire process. Some of the common issues of using manual testing are:
Benefits of AI in Software Testing
Incorporating AI in software testing is an apt decision as doing so will not only save a lot of time and labour but also will benefit in a number of other ways like:
1. Better Quality:
Using AI in software testing after the process of development not only saves time but also helps in ensuring better quality. It predicts, prevents as well as automates the entire process of testing using self-learning algorithms.
AI will not only support and improve the requirements models and test cases but also provide more sophisticated and refined form of text recognition and better code generators.
2. Quick and Reliable:
Using AI in the software testing process will save a lot of time of the development team. This is because using AI helps in delivering quick and faster results that are also reliable for the team.
Using AI will not only allow the team to use data to develop better projects but will also help them prevent any sort of repetition of errors done in the past.
3. Early Feedback:
Automating the entire testing process not only saves a lot of time but also helps the developers in collecting quick feedback. Collecting early feedback helps in ensuring that the bugs are fixed quickly and the better product is soon launched in the market.
Using AI will also allow the team to spot any or all sorts of bugs that might have occurred during the previous development process and avoid the same so as to ensure the delivery of a better performing product.
Since everything is stored on a machine, it becomes easier for the developers, testers and other team members to quickly trace and access the details of any testing process that has been executed.
Whether it is about missing a test case or identifying a dead case, all is possible with ease using AI.
5. Integrated Platform:
This type of testing uses one integrated platform and is also adaptable to client technology landscape. Having one single integrated platform makes this process a reliable process to execute a test.
Since the projects are built on open source stack making the project flexible and agile, cost effective and secure, AI testing can be executed much more smoothly.
6. Script Automation:
Using AI will end the need of creating a test design or automating a test script. This is because all this will be done and executed automatically by the AI algorithm.
With AI, it gets easier for the system to go through the log files as well as improve the test planning and coverage of the system.
Apart from testing, AI will also make developers as well as applications smarter. Using AI will make developers and applications smart in the following ways:
However, it is true that AI has an ability to simplify a lot of tasks. But, it is important to find a balance between the role of a machine and human.
Being completely dependent on any of the two might hamper the productivity as well as performance. Therefore, the best way out is to find a way in which both machine and humans can exist simultaneously, delivering maximum results.