Binary reverse engineering is used to understand and analyse programs for which the source code is unavailable. Decompilers can help, transforming opaque binaries into a more readable source code-like representation. Still, reverse engineering is difficult and costly, involving considering effort in labelling code with helpful summaries. While the automated summarisation of decompiled code can help reverse engineers understand and analyse binaries, current work mainly focuses on summarising source code, and no suitable dataset exists for this task. In this work, we extend large pre-trained language models of source code to summarise de-compiled binary functions. Further-more, we investigate the impact of input and data properties on the performance of such models. Our approach consists of two main components; the data and the model. We first build CAPYBARA, a dataset of 214K decompiled function-documentation pairs across various compiler optimisations. We extend CAPYBARA further by removing identifiers, and deduplicating the data. Next, we fine-tune the CodeT5 base model with CAPYBARA to create BinT5. BinT5 achieves the state-of-the-art BLEU-4 score of 60.83, 58.82 and, 44.21 for summarising source, decompiled, and obfuscated decompiled code, respectively. This indicates that these models can be extended to decompiled binaries successfully. Finally, we found that the performance of BinT5 is not heavily dependent on the dataset size and compiler optimisation level. We recommend future research to further investigate transferring knowledge when working with less expressive input formats such as stripped binaries.

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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.@en

Test smells attempt to capture design issues in test code that reduce their maintainability. Previous work found such smells to be highly common in automatically generated test-cases, but based this result on specific static detection rules; although these are based on the original definition of “test smells”, a recent empirical study showed that developers perceive these as overly strict and non-representative of the maintainability and quality of test suites. This leads us to investigate how effective such test smell detection tools are on automatically generated test suites. In this paper, we build a dataset of 2,340 test cases automatically generated by EVOSUITE for 100 Java classes. We performed a multi-stage, cross-validated manual analysis to identify six types of test smells and label their instances. We benchmark the performance 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 show that these test smell detection strategies poorly characterized the issues in automatically generated test suites; the older tool’s detection strategies, especially, 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; and (ii) more accurate detection strategies, to be evaluated primarily in industrial contexts.@en