Machine learning is shaping many different industries, automating processes and increasing efficiency and productivity. Generally speaking, machine learning is used for the following things:
- Digital transformation: assimilating various types of data (video, images, structured, unstructured) across different sources (social media, mobile IoT, sensors, etc.).
- Automating decisions by switching from rules-based systems and using adaptable and cognitive decision-making processes.
As machine learning becomes more common across industries, it is already a disruptive technology that will revolutionize many facets of our economy and technology. Machine learning can do this by:
- Improving customer satisfaction by anticipating their needs before they arise
- Improving team engagement and productivity
- Optimizing company practices and automating internal processes so they are more efficient.
When it comes to automated testing using traditional frameworks such as Selenium we usually create test scripts that require frequent rounds of testing and debugging meanwhile machine learning in software testing significantly reduces the necessity of repetitive action as it creates ML models of how the website works recording huge amounts of data. It is also revolutionizing the manual tasks developers have had to perform for years. If you would like to know more, check out the rest of the article below.
Machine Learning Optimizes Software Testing by Predicting Test Case Failures and Defects
If you, like many other developers, wish that you could predict a test case failure before the process even begins, machine learning is just what you need.
Thanks to advances in AI technology, developers can now predict defects that could occur further down the line by training machine learning models to analyze historical data. This information includes software configuration management, test case logs, user requirements, and defects.
When a new user requirement is created, machine learning models can accurately predict potential test case failures by analyzing that historical data and spotting patterns in the software’s code and processes.
As such, these predicted failures and defects can help developers fix the potential before they become an issue. Some machine learning models help developers pick up on problems as early as the requirements phase of development, reducing manual efforts at a later date to detect problems with the software.
To put this in a real-world scenario. A developer may be able to predict future failures based on commit patterns and available files in the development phase. They can then eradicate those future defects before signing off on the software, avoiding extra effort in defect identification, triage, and subsequent re-testing.
Find Production Issues that Have Not Been Identified in Software Testing
Software platforms usually generally have a huge amount of data from incident logs, app server logs, database logs, and customer service representative logs; far too much information for a developer to manually mine in search of issues missed during testing.
If this was a critical issue, the developer would have to dive into the logs to fix the problem. Their testing efforts are then spent on coming up with a fix, before moving to the next issue, the next issue, and so on.
However, if machine learning were implemented by the developer team, the AI could mine the post-production data, link it back to the software development life cycle and give the developer actionable reports. By raising the issue to developers, machine learning can help testers and software developers avoid these critical defects in the future.
Machine Learning Can Optimize Test Data
Developers are often caught unaware by gaps in test data, both during the testing phase and post-production triage process. These occurrences may increase when the developer cannot keep up with the data and requirements of an application.
Let’s look at this with a practical example. If you are a developer working on a portal that is intended to function across many different countries, you’ll have a difficult time capturing every requirement and testing the system properly.
Thanks to machine learning, developers can now access production data depending on how they modify test data, increasing test coverage for that particular portal. As such, machine learning can prove useful when it comes to optimizing test case data and ensuring there are no gaps in any phase of the software testing process.
Machine Learning Can Satiate User Needs
In the future, machine learning and artificial intelligence may focus on satisfying user needs as far as software performance is concerned.
Digital software developers place great importance on the user experience and how your audience interacts with the platform. Nowadays, users are quick to abandon a website or mobile app due to poor performance and slow response times, looking for an alternative option instead.
However, using machine learning technology, developers can identify and analyze customer usage patterns on software and provide feedback for the testing team. This analysis would be impossible without the AI mining data from customer usage logs and relaying the information back to the relevant department.
Final Thoughts
As you can see, these are just a few of the ways machine learning improves the software testing process. Whether you are looking to pre-emptively fix issues, spot gaps in your testing process, or ensure an app meets user demands and interaction habits, machine learning can make the process so much simpler.