Machine learning is all the rage these days. It’s an advanced technology that promises better products, happier customers while pouring out our ailing souls for more peace in this galaxy. This means that software testers need to know how to benefit from machine learning while using it in software testing. The idea is to use the power of machine learning in testing while not getting crushed by it. The question is, how?
Ways by which you can benefit from machine learning while using it in software testing include:
Using Machine Learning to Test Software Test Automation and Isolating Bugs
When you use a software test automation tool, that is, when you automate testing of your application, you’re taking out the human element from the verification process. The usage of a machine-learning algorithm can be used to continue this automation. Once you’ve automated a given application testing task such as load testing, unit testing, UI testing, or behavioral testing, the engine associated with that test case will run a machine learning algorithm on the data collected during and after execution of the automated test. Machine learning in testing uses algorithms to analyze this data and detect anomalies. These anomalies are then used as signs for additional or alternative tests that need to be performed based on these anomalies.
Using Machine Learning to Perform Risk Management
It’s a common practice in software testing to perform threat modeling. Threat modeling is an activity designed to describe the threats that may constitute the various risks within your comprehensive testing environment. You can attempt to identify and mitigate these risks through threat modeling by creating strategies and processes for identifying, analyzing, mitigating, or eliminating potential risks that may cause harm or damage.
Through machine learning, you can try categorizing your applications based on the data collected from them, then visualizing this data using different machine learning algorithms that operate on different dimensions such as time, user action/state change, etc.
Using Machine Learning to Prioritize Test Cases Based on the Results
The possible benefits that can be derived from machine learning algorithms are only limited to the space of possibilities for testing. This space includes all the features, functions, applications, and events in your software and those test cases that may need to be performed to verify the behavior of these features, functions, and events. That’s why you should prioritize your test cases based on their potential contribution to this space through machine learning algorithms.
Using Machine Learning to Automate Performance Testing and Continuous Integration
Performance testing is a common practice that is mainly used to test the performance of your applications under various conditions or scenarios. These tests are usually performed manually and involve a tester manually performing actions on your software application and observing the effects. This can be done through exploratory testing and statistical techniques such as user simulation and system benchmarking.
Automating performance testing has two important implications:
First, using machine learning algorithms, you can create a scoring function through which you can evaluate the overall performance of your applications based on different metrics like response time, an average number of calls per second, for example, etc. Then, you can automate performance testing based on these scoring results. For example, test cases that are less likely to meet your performance requirements will be ranked low and not executed in the performance tests.
Second, you can use these results to perform continuous integration of your applications by automatically triggering code changes when a new application variation is created that complies with your performance expectations.
Using Machine Learning to Automate Usability Testing
Usability testing evaluates the ease of use (and learnability) of a software application’s user interface by performing usability tests and evaluating certain parameters that measure the quality of user experiences, such as ease of learning, efficiency, and errors.
Usability testing can be performed automatically through exploratory testing sessions with several tests initiated by the tester manually. However, these tests are usually recorded and used again for performance testing.
Using machine learning algorithms, you can automate usability testing by extracting features from the application’s UI that may be considered indicators for usability. These extracted features can be combined to create a test scenario or test case, which is automatically carried out over time to generate results and score against these different indicators.
Using Machine Learning to Automate Regression Testing
Regression testing means the process of testing a piece of software that has been modified after the initial release in a way that ensures consistency with the previous version. This behavior is often referred to as regression testing and can be performed iteratively or even continuously with the help of machine learning algorithms.
Regression testing can be automated in two ways:
First, through predictive feature extraction, you can detect changes in your software’s features and act on this information to create potential regression test cases during regression testing. These test cases will contain inputs (different values) whose outcomes (specific values) will be compared with the output values of the previous version.
Second, through predictive fault detection, you can detect patterns in your application’s historical data responsible for certain errors. These patterns can be detected through machine learning algorithms, and these errors can then be prioritized over other errors during regression testing.
Using Machine Learning to Automate Security Testing
Testing for security issues in software applications is a complex task that includes manual and automated tasks. Security testing is often performed manually by security testers and focuses on identifying potential vulnerabilities in an application’s design, implementation, and operation, i.e., the strengths of an application are evaluated against efforts to breach or circumvent them.
Manual testing involves specific actions (e.g., checking the application’s parameters settings, validating input/output data, setting up certain conditions, etc.) upon which you will base other test scenarios and eventually a test plan. On the other hand, automated security testing can reduce these actions drastically within your software and save time by reusing these tests multiple times over and over again.
Therefore, security testing is a space that involves machine learning algorithms to automate tasks related to identifying vulnerabilities within your codebase, including static analysis tools (such as code review tools) that can be used for this purpose.
There is several works to be done in the area of software testing and automation. A software tester is expected to perform multiple tasks with limited resources. Using machine learning algorithms can help save a significant amount of time, effort, and cost in this process.
However, automation is not an easy task that comes with a single solution that fits all your needs. You may need to start small by automating simple test cases or even one or two specific test cases if it makes sense to do so.
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