Software testing makes the difference between a top-tier digital product and an average one. Not only does automatic testing massively replace manual testing, but a new advancement is about to enter the scene. Artificial intelligence development has already proven to be a winning strategy in a wide range of sectors, including financial trading, retail, and even cancer detection. Now it is time to bring its power into this area, and help testers make sense of petabytes of data in the shortest amount of time.
Manual testing will soon no longer be an option due to its rising costs and because it increases the time-span necessary to create a viable product. Furthermore, though manual testing only a limited amount of cases or scenarios can be handled. Also, it was not designed for the complexity currently posed by the growing number of IoT. In the words of Jason Arbon, manual testing is comparable to rolling down a car window by hand, a gesture that future generations will not understand and will be puzzled by its necessity.
On the contrary, testing based on AI is fast, traceable, and reliable. It offers the opportunity for the bots to learn on their own and has no limit to the amount of data to be tested. The best news is that in this case, there are early red flags and quick action can be taken.
Using AI for software testing comes with improvements in speed, quality, maintenance, and compatibility. The new technology offers the opportunity to track results, create databases with past outcomes and ultimately acts as a way to let go of redundancy. Here are the top reasons to consider AI for software testing, including websites, apps, and IoT devices.
Testing bots are designed to perform on their own, with no outside intervention unless it means a new set of training data. This means excellent cost-efficiency due to removing the need for long man-hours performed by manual testers. We will no longer need to select, manage and drive SUTs, as AI will do this for us.
Testing is a repetitive action; therefore, speed and reliability are top priorities. AI learned to recognize cancer from pictures as good as physicians, if not better. Using the same logic it can learn to identify the desirable patterns, allowing the testers to develop better cases in the meantime.
There is an essential difference between automation and AI. A lot of manual testing can be automated by hard-coding, yet AI offers a whole different range of opportunities since it can learn from past experiences and make decisions. Shortly put, it is non-deterministic, since the neuronal layers can create new answers to new questions by combining pre-existent inputs and expected outputs. Currently, the most significant challenge of working with such systems is that there is a range of possible outcomes and the algorithm needs to learn the best choice. A way to do this is to isolate data and look at system results.
By automating the processes, development teams, aided by testers can identify and isolate bugs quickly. Also, since the whole process is performed by bots, it means that it is possible to study more cases at once and pinpoint problems before moving into a new phase. This helps eliminate the errors rapidly and create logs that record changes and constitute a learning base.
Bots can create test cases and replicate themselves to act as thousands or millions of different end-users, powering through the software product in as little as one hour. They gather details about bugs, error dialogues, crashes, incompatibilities and put all that together in a report.
The advantage of using bots is that these adapt on the spot to changes and identify new paths and patterns. The algorithms can instantly learn about new requirements and quickly identify new failure patterns or defects. All you need to do is provide a few new sets of training data, and they are good to go.
The prediction is that in the future, software testing engineers will build AI systems that will test other AI systems, thus crossing the line between QA and data science.
Manual testing, through its repetitive and dull nature, was prone to errors caused by boredom and attention slips. When the same actions are performed on multi-core machines by bots, the chance of an honest mistake is null, dramatically increasing the end product. Furthermore, bots will become highly specialized, testing only a single feature, like a button or a search box.
Most software applications for current devices should be able to perform flawlessly on a very different range of devices. AI can act as an emulator of the end-user equipped with various terminals, all without leaving a single platform which gathers all the results in a matter of moments. Once some tests were performed, the results can be centralized and analyzed to identify problems.
Manual testers were forced to keep detailed logs of their work, usually using spreadsheets or dedicated platforms. By using AI, all the tests, results, changes, error codes and other data are automatically stored after each batch of tests, offering a way to validate what was already tested and even creating a training set for new test instances.
A recurring theme when talking about AI and automation is the fear of specialists becoming redundant and losing their jobs to machines. As the previous industrial revolutions proved, this is true up to a degree. People will be replaced by computers in performing tedious and repetitive work that is error-prone and does not require human capabilities such as critical thinking and problem-solving. In fact, software testers will be free from mindless tasks and more able to focus on limit cases and less frequent situations. AI for testing shouldn’t be intimidating since it sets the record straight: it gives machines the tedious work and people the time to think and be creative.