How is ML enhancing Functional Test Automation
On the high wave of tech progress, there is no one who hasn’t heard about Machine Learning. As the hottest buzzwords of this age, they restore a strong faith in a more advanced future for almost every part. And when it comes to the Software Industry, Automation plays a key role in fixing flaws in the QA process.
ML powers have hit somewhat a top position and now we have enough reasons for calling them the perfect tools, but does it mean the machine will replace the human? The answer is ‘No’. Here is a detailed outlook from our end:
What was the Traditional Testing Approach?
Every day, QA Engineers stumble upon a lot of difficulties and waste time in finding out the proper solutions. While adding new changes, existing code that has already gone through testing may stop working. Each time the development team expands on existing code, they must carry out new tests.
This testing cycles can take a long time while undertaking them manually
What is lacking?
The traditional QA process is based on the method of checking the set of simple tasks that together form the overall project. For doing this, a tester has to go through this way from the smallest elements up to the largest ones. Since the clients get impatient, traditional testing methods fail to keep up with their demands. At the initial stage of a project, testing can usually go in parallel with increasing functionality, but the more complex an application is, the more challenging it becomes to make sure it has full test coverage.
Why do we need an advanced approach?
That the current process checks only specific cases that you have chosen. If a new function is added, the previously created auto-test will still complete successfully, even if the new function does not work. Only research testing (by humans) will be able to detect these changes, but this most often does not occur, since this time is spent on re-performing basic tests, so situations often arise when manual testing checks the application’s performance as a whole item, losing details.
How does ML help?
Machine Learning (ML)is developed from the study of pattern recognition and computational learning approach in AI. The main purpose is to make machines learn without being in a detailed manner, One of the most common and well-used ML-methods is Deep learning that is based on learning data representations. It is also based on neural networks in the human body.
At its core, ML algorithms consist of three types:
- Supervised Learning – We are giving the right training data for the algorithm to learn.
- Unsupervised learning – We give a bunch of data and see what we can discover.
- Reinforced learning – Based on the idea of the reward function. Rewarding Good/Bad behavior and developing the algorithm to learn from it.
Adobe’s research from last year shows that only 15% of enterprises were using AI, but forecasted this number to double in only 12 months. If they were right this means that today one-third of organizations have implemented it already. Furthermore, the overall revenues from AI are expected to grow from $9.5 billion in 2018 to the impressive $118.6 billion in 2025 and that’s only 6 years from now.
The Final Word
ML and AI are undeniably growing to be significant elements in software testing and QA as well. And all this is for good reason. AI will have perfect accuracy, increases revenue and lower costs for all QA processes. Henceforth, it improves competitive positioning and customer experience. AI helps identify bugs quicker and faster. The testers can stop worrying about losing their jobs and start focusing on making better policies.
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