TestVikings Laboratory

Context: Search-based test generation typically targets structural coverage of source code. Past research suggests that targeting coverage alone is insufficient to yield tests that achieve common testing goals (e.g., discovering situations where a class-under-test throws exceptions) or detect faults. A suggested alternative is to perform multi-objective optimization targeting both coverage and additional objectives directly related to the goals of interest. However, it is not fully clear how coverage and goal-based objectives interact during the generation process and what effects this interaction will have on the generated test suites. Objectives: We assess five hypotheses about multi-objective test generation and the relationships between coverage-based and goal-based objectives, focusing on the effects on coverage, goal attainment, fault detection, test suite size, test case length, and the impact of the search budget. Methods: We generate test suites using the EvoSuite framework targeting Branch Coverage, three testing goals—Exception Count, Output Coverage, and Execution Time—and combinations of coverage and goal-based objectives. Results: Targeting multiple objectives does not reduce code coverage, yields no or minor reductions in goal attainment, and detects more faults compared to single-target configurations. It produces larger test suites, but test case length is not increased. The benefits of multi-objective optimization are often more limited than hypothesized in past research, but improved fault detection is still sufficient to recommend multi-objective optimization over targeting coverage or testing goals alone. Conclusion: Our study offers insights and guidance into how coverage and goal-based objectives interact during multi-objective test generation.

Much of the cost and effort required during the software testing process is invested in performing test maintenance - the addition, removal, or modification of test cases to keep the test suite in sync with the system-under-test or to otherwise improve its quality. Tool support could reduce the cost - and improve the quality - of test maintenance by automating aspects of the process or by providing guidance and support to developers. In this study, we explore the capabilities and applications of large language models (LLMs) - complex machine learning models adapted to textual analysis - to support test maintenance. We conducted a case study at Ericsson AB where we explore the triggers that indicate the need for test maintenance, the actions that LLMs can take, and the considerations that must be made when deploying LLMs in an industrial setting. We also propose and demonstrate a multi-agent architecture that can predict which tests require maintenance following a change to the source code. Collectively, these contributions advance our theoretical and practical understanding of how LLMs can be deployed to benefit industrial test maintenance processes.

Mutation testing is a potentially effective method to assess test suite adequacy. Researchers have made mutation testing more computationally efficient, and new frameworks are regularly emerging. However, there is still limited adoption of mutation testing in industry. We hypothesize that such adoption is hindered by a lack of guidance on how to effectively and efficiently utilize mutation testing in a development workflow. To that end, we have conducted an industrial case study exploring the technical challenges of implementing mutation testing in continuous integration, what information from mutation testing is of use to developers, and how that information should be presented (in textual and visual form). Our results reveal five technical challenges of integrating mutation testing and nine key findings regarding how the results of mutation testing are used and presented. We also offer a dashboard to visualize mutation testing results, as well as 16 recommendations for making effective use of mutation testing in practice1.