Search papers, labs, and topics across Lattice.
This paper introduces TestGeneralizer, a framework that generalizes existing test cases to comprehensively cover test scenarios by understanding the underlying requirements and generating diverse test instances. It leverages an initial test case to infer the developer's intent and associated scenarios, then generates a test scenario template and crystallizes it into various test scenario instances. Evaluated on 12 Java projects, TestGeneralizer significantly outperforms state-of-the-art baselines like ChatTester, improving mutation-based and LLM-assessed scenario coverage by 31.66% and 23.08%, respectively.
Stop writing incomplete tests: TestGeneralizer can automatically expand your existing tests to cover 31% more scenarios and catch more bugs.
Test cases are essential for software development and maintenance. In practice, developers derive multiple test cases from an implicit pattern based on their understanding of requirements and inference of diverse test scenarios, each validating a specific behavior of the focal method. However, producing comprehensive tests is time-consuming and error-prone: many important tests that should have accompanied the initial test are added only after a significant delay, sometimes only after bugs are triggered. Existing automated test generation techniques largely focus on code coverage. Yet in real projects, practical tests are seldom driven by code coverage alone, since test scenarios do not necessarily align with control-flow branches. Instead, test scenarios originate from requirements, which are often undocumented and implicitly embedded in a project's design and implementation. However, developer-written tests are frequently treated as executable specifications; thus, even a single initial test that reflects the developer's intent can reveal the underlying requirement and the diverse scenarios that should be validated. In this work, we propose TestGeneralizer, a framework for generalizing test cases to comprehensively cover test scenarios. TestGeneralizer orchestrates three stages: (1) enhancing the understanding of the requirement and scenario behind the focal method and initial test; (2) generating a test scenario template and crystallizing it into various test scenario instances; and (3) generating and refining executable test cases from these instances. We evaluate TestGeneralizer against three state-of-the-art baselines on 12 open-source Java projects. TestGeneralizer achieves significant improvements: +31.66% and +23.08% over ChatTester, in mutation-based and LLM-assessed scenario coverage, respectively.
NUS