The security assessment is an inevitable part of any modern project nowadays. Digitalization of manual business processes requires the implementation of thoughtful development and testing approaches. This means that the software development lifecycle should change as security assessment itself and the analysis of its results require meaningful planning and extra team capacity, including Machine Learning Methods.
In addition to that, the testing process should be revised as well. The properly considered test strategy makes the whole testing process effective and valuable for project delivery and decreases the probability of dramatic consequences of personal data slippage or any other serious production issues.
What Is Machine Learning In The Software Development World?
Machine learning (ML) uses algorithms for decision-making and utilizes feedback from a human in order to update these algorithms. These years machine learning has become the gold standard for many companies, including the main phases of SDLC: development and testing.
The future of software testing fundamentals is about faster tests, especially in terms of the analysis of their results. The next generation of quality assurance is about less flaky and better-designed test scripts that cover various user scenarios and minimize manual effort spent on automated test development.
Moreover, testing automated scripts themselves should not bring additional security risks to a project. Therefore, it’s very important to work attentively with data. With the help of machine learning embedded into test automation frameworks, test engineers could better categorize test data, analyze business risks, and analyze what browsers and operating systems are less stable for the developed solution.
Machine Learning And Software Testing Evolution
Machine learning in software testing is still rudimentary. However, chances are that in just a few years machine learning will replace many of existing test automation approaches. The main benefit of machine learning in software testing is that ML-based testing tracks each user interaction with an application, learning the edge use cases and business flows. This analysis can help in understanding user behavior, and predict what application changes could lead to defects and drastic consequences for businesses. Automated tests, developed with the help of machine learning, definitely need less maintenance time.
Detecting and Preventing Attacks In Software Testing Using Machine Learning
Security audits are essential, but quite time-consuming processes on many modern projects. The security verification process itself is less challenging than the results triaging. There are a lot of well-designed scanners to perform security checks. However, even with a static application security testing approach (SAST), quite often the results are false positives. So, security engineers have to look through all the results. Developers and test engineers also need to do manual audits and come up with a decision if the risks are accepted or should be mitigated eventually.
Such an activity is not rocket science, however, the effort spent for understanding that some exploitable risks are not real vulnerabilities could be redirected to more business-oriented areas of the software development process.
Audit of security check results is crucial in terms of potential attack prevention. This analysis cannot be skipped or postponed if there is no capacity in the team to do that. Thus, ML-based security risk analysis can significantly reduce time on security testing and results from audits. Except for that, deep learning of end-to-end flow models can predict possible security attacks without additional analysis from human beings.
While being trained, the ML model collects data and additional characteristics based on your specific project architecture and security configurations from preceding analysis so this process can be automated and embedded to the whole testing process iteratively. Machine learning-assisted automated tests help in identifying environment- or configuration-specific test failures, providing this analysis much quicker than a test automation engineer analyses the root cause of a brittle test.
Moreover, test automation frameworks built on the top of machine learning can self-heal the tests on the fly or at least suggest the fix when in a classical approach test engineers need firstly recall business requirements, debug the failed tests in isolation, then as a whole test suite, fix the instability issue, create a pull request and go through the whole test development process.
Taking into account all mentioned above, the next generation of tests implemented on the machine learning basis will certainly impact the existing automation approaches and technologies, providing test frameworks with more stability and, therefore, credibility and value for businesses as release quality is the key point for project stakeholders. In addition, such tests will help to detect real application anomalies or intentional vulnerabilities on the fly, training on the huge amounts of data, and alert about them and thwart them on earlier stages.
