Neural Unit Test Suggestions
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Testing is widely recognized as an important stage of the software development lifecycle. Effective software testing can provide benefits such as documentation, bug finding, and preventing regressions. In particular, unit tests document a unit's intended functionality. A test oracle, typically expressed as an condition, documents the intended behavior of the unit under a given test prefix. Synthesizing a functional test oracle is a challenging problem, as it has to capture the intended functionality and not the implemented functionality. In this paper, we propose (Neural Unit Test Suggestions), a unified transformer-based neural approach to infer both exceptional and assertion test oracles based on the context of the focal method. Our approach can handle units with ambiguous or missing documentations, and even units with a missing implementation. We evaluate our approach on both oracle inference accuracy and functional bug-finding. Our technique improves accuracy by 33% over existing oracle inference approaches, achieving 96% overall accuracy on a held out test dataset. Furthermore, we show that when integrated with a automated test generation tool (EvoSuite), our approach finds 54 real world bugs in large-scale Java programs, including bugs that are not found by any other automated testing method in our evaluation.
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