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.

Real software, the kind working programmers produce by the kLOC to solve real-world problems, tends to be "natural", like speech or natural language; it tends to be highly repetitive and predictable. Researchers have captured this naturalness of software through statistical models and used them to good effect in suggestion engines, porting tools, coding standards checkers, and idiom miners. This suggests that code that appears improbable, or surprising, to a good statistical language model is "unnatural" in some sense, and thus possibly suspicious. In this paper, we investigate this hypothesis. We consider a large corpus of bug fix commits (ca. 7,139), from 10 different Java projects, and focus on its language statistics, evaluating the naturalness of buggy code and the corresponding fixes. We find that code with bugs tends to be more entropic (i.e. unnatural), becoming less so as bugs are fixed. Ordering files for inspection by their average entropy yields cost-effectiveness scores comparable to popular defect prediction methods. At a finer granularity, focusing on highly entropic lines is similar in cost-effectiveness to some well-known static bug finders (PMD, FindBugs) and ordering warnings from these bug finders using an entropy measure improves the cost-effectiveness of inspecting code implicated in warnings. This suggests that entropy may be a valid, simple way to complement the effectiveness of PMD or FindBugs, and that search-based bug-fixing methods may benefit from using entropy both for fault-localization and searching for fixes.