JavaDL: Automatically Incrementalizing Java Bug Pattern Detection

Static checker frameworks support software developers by automatically discovering bugs that fit general-purpose bug patterns. These frameworks ship with hundreds of detectors for such patterns and allow developers to add custom detectors for their own projects. However, existing frameworks generally encode detectors in imperative specifications, with extensive details of not only what to detect but also how. These details not only add complexity to maintenance and extensibility, but also interfere with the framework’s ability to change how detection is done, for instance, to make the detectors incremental.
In this paper, we present JavaDL, a Datalog-based declarative specification language for bug pattern detection in Java code. JavaDL seamlessly supports both exhaustive and incremental evaluation from the same detector specification . The system further allows developers to concisely describe bug detectors via syntactic pattern matching for local AST matching, and via Datalog-style logical rules for nonlocal reasoning. We compare our approach against the well-established SpotBugs and Error Prone tools by re-implementing several of their detectors in JavaDL. We find that our implementations are substantially smaller and similarly effective at detecting bugs on the Defects4J benchmark suite, and run with competitive runtime performance. In our experiments, neither incremental nor exhaustive analysis is able to consistently outperform the other analysis mode, which highlights the value of our ability to transparently switch execution modes. We argue that our approach highlights the potential of clear-box static checker frameworks that constrain the bug detector specification language to enable the framework to adapt and enhance the detectors.

Presented at OOPSLA 2021, part of SPLASH 2021.
By Alexandru Dura, Christoph Reichenbach, Emma Söderberg