The demand for software development using latest technologies and tools is increasing. Businesses and tech firms alike are expected to produce quality, fully developed, fully functioning software products in the shortest amount of time possible. Failure to do so can result in the reduction of customer satisfaction and a loss of profits. This has put a great deal of pressure on the testing process.
Ever since the use of technology has grown, so has the intricacy of the software we use which can increase the complexity of software testing. This complexity, coupled with a shortened delivery cycle has forced developers, software testers, and quality assurance experts to find better ways to get the job done.
This is where the shift left approach comes in. With shift left testing, software is tested earlier in the lifecycle (as the name suggests, shifted left in the project timeline). By testing early, there is a greater collaboration between testers and developers. Working simultaneously, they are able to speed up the delivery process, identify potential bottlenecks, and produce higher quality software.
Introducing automation into this early testing helps to prevent software defects rather than identifying and trying to correct, them later in the development cycle or after it has been released.
How does ML and NLP help in testing?
Automating the testing process can help support continuous delivery during software updates and it can greatly improve the time and resources spent on developing new software. Machine learning (ML) and Natural Language Processing (NLP) are two ways that Artificial Intelligence (AI) is being used to increase automation.
Previously, in software testing, bugs, errors, and system weaknesses could only be found if the testers entered specific parameters that searched for those failures. ML can catch a wider range of issues because it moves beyond those set parameters and develops test cases that are larger and more complex than would otherwise be possible.
Humans are an important part of software testing but ML enables a thoroughness and speed that would be impossible if driven by humans alone. Each time a new piece of code is written, it needs to be debugged and tested, it can be hard for a human to keep up. ML will learn what a tester would be looking for and spot any problems faster than a human could. And it enables continues, 24/7, software testing which would be impossible without a large testing team working around the clock. ML provides a faster, more cost effective solution.
NLP uses a variety of language based information to construct test cases that can be applied to software at all stages of development. It is also able to translate multiple coding languages to provide easier understanding and examination of the testing data. This broadens the number of people who are able to work on a project and removes some of the more tedious, error-prone parts of the process.
Because testing plays a huge role in software development, there are numerous tools, platforms and open source technologies available that can improve the testing process.
Open source tools like Selenium have been the go-to for automated testing for years and they are only becoming more popular. These tools can be used with, and improve the performance, of Agile methodologies and Devops to bring about a faster release while ensuring product quality. More than that, because Selenium is free, it dramatically reduces costs while simultaneously granting access to a community of experts that can support the process.
As software trends change, so do the tools used to support their development. Because frameworks like Selenium require specific scripting expertise, they are not always the best option. If a production team has a limited talent pool, it could make using this framework rather inefficient. New startups, like Mabl seek to simplify the process with ML. User interfaces are constantly changing in the development process which can lead to test script failures. These brittle tests make it nearly impossible to detect actual failures in the software. Mabl’s ML allows it to adapt to these changes and auto-heal the tests without developers having to spend hours re-writing lengthy code. There are many more Artificial Intelligence test automation tools such as Applitools, SauceLabs, Testim, Sealights, Test.AI and ReTest which are popular and used widely.
It is clear that as the need for automation and smart analytics continues to grow, new tools and platforms will arise that build upon the current foundation. These developments can produce major benefits to the quality of the software but also improve the overall function of a business. By automating the testing process, developers are free to focus on writing code which will improve their productivity and likely improve their code. And because this testing can be done simultaneously and continuously, it means the software is market ready in a shorter period of time. Time is indeed money and more time saved means more money saved. Rapid deployment of new or updated software also allows businesses to keep a competitive edge, no matter the industry.
The appetite for new and improved software is not about to disappear and this means that developers are being asked to turn out quality products at an increasingly rapid pace. Luckily, the growth of AI and ML have lead to automated software testing that simplifies and streamlines development, placing everyone, from developer to CEO in position to not only meet product expectations but to exceed them.
Market Research, RapidValue