Test smells 20 years later: detectability, validity, and reliability

Abstract

Test smells aim to capture design issues in test code that reduces its maintainability. These have been extensively studied and generally found quite prevalent in both human-written and automatically generated test-cases. However, most evidence of prevalence is based on specific static detection rules. Although those are based on the original, conceptual definitions of the various test smells, recent empirical studies indicate that developers perceive warnings raised by detection tools as overly strict and non-representative of the maintainability and quality of test suites. This leads us to re-assess test smell detection tools’ detection accuracy and investigate the prevalence and detectability of test smells more broadly. Specifically, we construct a hand-annotated dataset spanning hundreds of test suites both written by developers and generated by two test generation tools (EvoSuite and JTExpert) and performed a multi-stage, cross-validated manual analysis to identify the presence of six types of test smells in these. We then use this manual labeling to benchmark the performance and external validity of two test smell detection tools—one widely used in prior work and one recently introduced with the express goal to match developer perceptions of test smells. Our results primarily show that the current vocabulary of test smells is highly mismatched to real concerns: multiple smells were ubiquitous on developer-written tests but virtually never correlated with semantic or maintainability flaws; machine-generated tests actually often scored better, but in reality, suffered from a host of problems not well-captured by current test smells. Current test smell detection strategies poorly characterized the issues in these automatically generated test suites; in particular, the older tool’s detection strategies misclassified over 70% of test smells, both missing real instances (false negatives) and marking many smell-free tests as smelly (false positives). We identify common patterns in these tests that can be used to improve the tools, refine and update the definition of certain test smells, and highlight as of yet uncharacterized issues. Our findings suggest the need for (i) more appropriate metrics to match development practice, (ii) more accurate detection strategies to be evaluated primarily in industrial contexts.

On the diffusion of test smells and their relationship with test code quality of Java projects

“…Results indicated a significant difference in test suite quality, and the existing tests had a smaller distribution of test smells than those generated by tools. Recently, Panichella et al 28 investigated the test smell diffusion in automatically generated test cases and accessed the accuracy of test smell detection tools. The authors built a curated dataset of automatically generated test cases by analyzing 100 Java projects regarding six test smells.…”

Test Smell Detection Tools: A Systematic Mapping Study

“…This induces a test method with multiple assert statements, covering all the production method's execution paths. According to the literature, it hinders comprehension by making it difficult to determine which assertion has triggered the test failure [32].…”

“…According to the literature, it hinders comprehension by making it difficult to determine which method has triggered the test failure [32].…”

JUGE: An infrastructure for benchmarking Java unit test generators

“…The availability of such technologies can also impact computer science education, with available tools that can be used in practical courses. Finally, we also expect future investigation supported by JUGE in the context of test code quality and cost-effectiveness of both automatically and manually generated tests [90,91].…”