Why you should employ AI in software testing2018-10-30T12:02:56.000Z 2018-10-30T12:02:56.000Z The testing procedure is longstanding and often its productivity does not meet the criterion of effectiveness set by the customers. Artificial Intelligence being able to perform a massive search in huge data volumes for a needle in a haystack can be of use here.
A job of software tester requires solid analytical skills, unconventional thinking and strong ability to focus on troubleshooting. In order to detect software failures or system weaknesses which might lead to the error software testers have to be immersed in the product deeper than developers did and to look at each individual element closer than anyone before.
However, the testing procedure is longstanding and often its productivity does not meet the criterion of effectiveness set by the customers. Artificial Intelligence being able to perform a massive search in huge data volumes for a needle in a haystack can be of use here.
Artificial Intelligence instruments allow various configurations of the present tests depending on a specific case or part of the code generated. Moreover, AI assistant can advise the tester which testing approaches and tools are more suitable for these or that software specifications.
In addition, Artificial Intelligence can identify the reasons of failures found by using previously accumulated experience. Multiplied by human testers’ expertise in product characteristics and customer requirements, Artificial Intelligence has a powerful capability to boost testing productiveness. Below several ways of successful AI deployment in software testing are outlined.
How Artificial Intelligence can assist human testers
Here the basic opportunities presented by AI to support testers are sketched:
- Highlight segments of a sprint with maximum risk probability that will help testers to range the areas to be tested. This feature comes in handy when testing timeframes are condensed and the incorrect prioritizing may cause negative consequences;
- Determine the tests that have to be conducted first to find the optimal solution to the problem. This possibility allows saving time that is necessary for troubleshooting as well as cutting the number of requests to the helpdesk and eliminating risks of financial loss because of software production operation interruption and data leakage;
- Separate off the error more swiftly and pinpoint the most probable reasons. Specifying precisely the code line causing the failure is the most efficient way to delineate areas of bugs location;
- Multiple source data combination: errors, log records, troubleshooting data and testing experience will assist testers to detect risk zones in software and to deliver better quality products on the market;
- Conduct synchronous informing on failures, problems and testing progress. When getting more details on quality parameters, the testing team is able to respond effectively to the raised challenges.
More functionality will fewer resources spent
By using Artificial Intelligence the development and testing teams can allocate less time and efforts to achieving better results in a more entertaining way. The AI-based tools will automatize routine manual testing work and give human testers more space for creative thinking.
Nevertheless, the testers should not be afraid of being replaced with AI testing robots: instead of taking over live testers’ functions the AI assistants would rather show where there can be a bug, so the tester will get clues on most risky parts to concentrate on.
Besides expanding the testing scope, Artificial Intelligence helps the testers to save more time for more exclusive work, for example, to analyze how the end users will feel when using the application, which features will make them happy or which will disappoint.
Testers have to learn how to work with AI
Since Artificial Intelligence systems are capable of delivering and analyzing huge volumes of data, the businesses need to employ the testers who can develop approaches and conceptions how to use it. In this context, the key tester’s function is to provide the vendor with a portion of data sufficient for concluding on apps release readiness. In this regard, Artificial Intelligence is a highly efficient instrument to make certain that each software solution produced is of good quality.
On this stage of Artificial Intelligence deployment in the testing job, the number of cases of such integration is still not so big on the market yet. Still, those ventures employing AI to verify software quality and functionality have already discovered the strong potential of identifying the most bug-risky zones of the product that requires additional testing or were not analyzed completely. In the near future, more cutting-edge AI testing instruments are anticipated to emerge in software product which is a positive call for the entire industry.
Anyhow before applying these AI instruments the businesses have to interconnect all development and testing information to ensure swift identification and analytical processing of the most vulnerable parts of software products. It is necessary to align the data across various testing ecosystems and storages like GitHub.
It may seem that Artificial Intelligence integration in software testing is a very complicated and time-consuming process. Still, obviously it is worth of efforts and time allocated since the businesses and the end users expect better quality applications delivered that becomes possible with making use of AI. Swift analytical assessment multiplied by the knowledge of a human tester provides customers with top-grade solutions. There is no reason to skip this chance of raising quality standards in software production with making testing mechanism more innovative and productive.
In addition, Artificial Intelligence allows businesses building efficient financial, manpower and time management strategies. As long as software failures require additional time for fixing the problems, more manpower and time assigned, the bugs often lead to financial loss or even to losing clients. Each venture would prefer avoiding such situations and here AI testing assistant will be of use by exploring all software parts including those that are likely to be skipped by a human tester due to the lack of time or low bug probability.
One more advantage of AI testers’ deployment is that they can assist the developers to extend their perception of end users’ anticipations from the software produced: development teams will have more opportunities to identify client’s expectations by using essential conclusions from huge volumes of data analyzed by AI. This is an option which can’t be supplied by cumbersome interviews and polling but it will definitely make a difference in a software testing industry.
These days you can find several efficient AI-based testing instruments on the market which prove their capability to disrupt exclusively manual software testing methodology:
- Testim.io, which is created for generation, performance, and support of automatic testing, it deploys Machine Learning and accentuates full-cycle operational testing and UI testing;
- Approvance, using Artificial Intelligence for testing scenarios creation based on users’ behavioral patterns which makes this tool completely client-oriented;
- Test.ai, a mobile testing tool employing Artificial Intelligence for regression tests performance, it is useful when it is necessary to evaluate productiveness of the app and it is aimed rather for observation than for operational assessment;
- Functionize, applying Machine Learning for operational testing it has lots in common with the aforementioned tools in terms of testing speed, ability to perform multiple tests simultaneously and ensuring deep analytical processing of the data.
As long as Artificial Intelligence is integrated into the software production industry, the experts are debating whether it is worthwhile to implement it entirely in their development processes.
But when the primary launch of AI is done, the businesses are agreeably surprised to explore that now they are able to generate better quality software with less manual efforts and budgets allocated. Instead of this, human testers can focus on more creative tasks, for example, making the products more user-friendly by detecting those parts that affect customers the most.
Certainly, in the long view, Artificial Intelligence won’t replace human testers but it will be gaining more significance, expanding its range of functions until integrated into the entire range of testing processes in order to release the top-notch software products.