Software development companies of our time tend to develop more and more progressive programs and apps which need to meet all the technical requirements before getting to the end user. QA testing became an inseparable part of the whole process of the software product’s creation, therefore QA technologies are developing at a high speed to make the possible errors be eliminated even before they arise.
What is the role of artificial intelligence in software testing? Fair to say, automation has already invaded the QA world and is growing and being used more and more as we need QA services more and more often. As an artificial replica of a human intelligence, artificial intelligence in software testing itself learns to process data not only faster, but with a better quality. That’s why we’re getting to the question: in the future of AI and machine learning, will we still need a human hand?
Future of Artificial Intelligence: What to Expect
See also:
Needless to say, AI is already shaping our future in every industry, however, people who are related to IT, feel this machine touch better than anyone else. Did you know that AI-related innovations took around 40% of all the amount of innovations applied to IBM in 2022? Automation and robotisation claim to be the most massive trend of the 2020-2030s decade and related technologies and IT initiatives are growing exponentially and rapidly invading the market.
Elon Musk, one of the biggest innovators and technology supporters of our time, donated $10 million to OpenAI – an AI research and deployment company which claims to work on the most worthy and beneficial for humanity technologies. There’s a bunch of examples and evidence of AI taking place in our world and ingrowing into everything we know. Scientists expect future ML technology to be incredibly smart, instantly learning and increasingly focused on perception – basically, like a human mind, but with a monstrous data-processing strength.
“I could feel, I could smell a new kind of intelligence across the table”, – Gary Kasparov said. The new kind of intelligence penetrated into the IT industry and opened new horizons for software testing companies in USA and all over the world, as many services can now be performed smarter and make a better and more detailed overview of the projects. The side effect of the enhanced services for the more complex IT needs is the lack of enhanced IT management which is quite difficult to perform in such a dynamically developing IT environment.
How AI and ML Changed Software Testing Industry
As artificial intelligence is created to be a simulation of a human one, which means the ability to process, analyze, overview, order and draw predictions from data, it has to gradually improve its abilities that are being widely applied in QA testing. Machine learning is predicted to be a key component of the QA testing process and gradually improve its accuracy till it takes the minimal time to define an error.
According to Statista, the revenue from the artificial intelligence in software testing and machine learning software market worldwide will reach $135 billion by 2025. Automation testing and quality assurance services are reaching its peak and simultaneously adopting AI, as the demand of IT products and services of the new generation on the market grows rapidly. More releases simply mean more testing.
Based on the AI in software testing approach, now even the general testing can provide us with more functional and non-functional scenarios of a software. Thus, the bigger and more detailed overview helps to upgrade the software to the level where it does what it needs to do faster and more efficiently. Incorporation of ML into QA testing simply makes the whole process less rigorous, time and energy-consuming.
Of course, generally speaking, the software development life cycle has already become much easier, however, AI test automation tools still have limitations to get resolved. Unfortunately, we still haven’t reached the level of progressive software testing where the involvement of a developer or tester himself is not necessary. Being predetermined and set to work according to the request, testing programs, even those including ML elements, run the process and show the test status, code coverage, error findings and other metrics to a human who works with testing results furthermore.
Real Examples of AI in Software Testing
Adoption of Static Analysis
Successful static analysis is usually managed by the software testing team according to special requirements which are unique and need to be followed by the resulting metrics of a testing process. The bottleneck of static analysis is dealing with many false positive warnings about errors and other checkers which need to be further investigated by the team. However, here the human factor takes place and, depending on what the team takes as false positive, further investigation happens or not. ML approach, based on previous testing experiences and error findings, is able to classify false warnings as worth/not worth an expert’s investigation. This way, adoption of static processes becomes more optimized and requires less manual work.
API Test Maintenance Automation
How is an API test usually being performed? We make continuous repetitive requests to one or more API’s endpoint and then compare with the expected results. Basically, we playback the request several times to understand the scenario to work with. With a little touch of machine learning technology, we can run deeper, more reliable and scalable API tests, as ML will make different templates from various API resources. These templates will work as API libraries which would allow us to track different patterns and types of behavior when exercising APIs. This way, in the future different API testing results will create an advanced testing unit with various scenarios to apply.
Unit Test Parameterization
Sharing the same technology with the above-mentioned examples, ML is being implemented into the testing process. Thus, based on previous testing experiences, it creates new patterns and parameters to follow. Depending on results, AI technology is able to automatically generate unique unit test mockups of different complexity and use existing ones to improve the future options and cover as much code as possible.
Benefits of AI Based Software Testing in the Future
- Accuracy
Statistically, more than 70% of inaccurate results take place because of the human factor. Machines, programmed to work with big loads of data and perform complex checking and calculations, have 90% less possibility of making mistakes. There are no such things as skills, professionalism, attentiveness, logic, there are simply automated mechanisms which learn to improve and give better results.
2. Time
Once humans understand to befriend AI based software testing techniques and teach them to do the requested actions with no slow downs or errors, tests would take twice less time. Unfortunately, even though AI test automation tools are widely used in top software testing companies in USA and worldwide, it’s too early to talk about full automation. Nevertheless, can’t be denied, QA testing with ML involved has already become less time-consuming.
3. Better opportunities
Talking about better opportunities, we mean better test cases. Educating itself, AI would possibly offer new approaches to operate and apply. For example, regression tests, which usually take much time and knowledge to run, could be performed better as test scripts are generated automatically. Mistakes which are made during the manual testing could be analyzed and reprocessed by ML.
- Positive Forecasts
Living in the era of digital transformation and technologies, scientists see humanity’s future in it. Gradually but surely, ML would become an inseparable part of our everyday life and ease every area of it. Speaking about the near future, we tend to say that it will probably be shaped with AI and ML in software testing much faster than anything else, simply because any software, before getting in use, is being tested. High standards and demand in testing offer better help quality.
Conclusion
High-class quality assurance services are a hard find nowadays. Before AI invades and modernizes software testing completely, we advise you to work with well known international software development companies. Software testing services in USA in particular are in great demand this time, which makes it the best ground for ML technologies to grow and improve the QA services humans can maintain. Who knows, maybe in some years we’d just need to throw a final glance at the fully automated testing project…
