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Static Analysis Using Facebook Infer Focused on Deadlock Detection
Marcin, Vladimír ; Rogalewicz, Adam (referee) ; Vojnar, Tomáš (advisor)
Static analysis has nowadays become one of the most popular ways of catching bugs early in the modern software. However, a frequent problem of static analysers, which are reasonably precise, is their scalability. Moreover, these which are efficient and scale (e.g.: Coverity, KlockWork, etc.) are often proprietary and difficult to openly evaluate or extend. An improvement to this state of practice is brought Facebook Infer, which offers an open-source framework for compositional and incremental static analysis. In this thesis, we present our Low-Level Deadlock Detector (L2D2) extending the capabilities of Infer. Our algorithm fits the compositional analysis, based on a context independent computation of a summary for each function, which results in its high scalability. We have implemented the algorithm and evaluated it on a benchmark consisting of real-life programs derived from the Debian GNU/Linux with in total 11.4 MLOC. While neither sound nor complete, our approach is effective in practice, finding all known deadlocks and giving false alarms in less than 4% of the considered programs only.
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Static Analysis Using Facebook Infer Focused on Deadlock Detection
Marcin, Vladimír ; Rogalewicz, Adam (referee) ; Vojnar, Tomáš (advisor)
Static analysis has nowadays become one of the most popular ways of catching bugs early in the modern software. However, a frequent problem of static analysers, which are reasonably precise, is their scalability. Moreover, these which are efficient and scale (e.g.: Coverity, KlockWork, etc.) are often proprietary and difficult to openly evaluate or extend. An improvement to this state of practice is brought Facebook Infer, which offers an open-source framework for compositional and incremental static analysis. In this thesis, we present our Low-Level Deadlock Detector (L2D2) extending the capabilities of Infer. Our algorithm fits the compositional analysis, based on a context independent computation of a summary for each function, which results in its high scalability. We have implemented the algorithm and evaluated it on a benchmark consisting of real-life programs derived from the Debian GNU/Linux with in total 11.4 MLOC. While neither sound nor complete, our approach is effective in practice, finding all known deadlocks and giving false alarms in less than 4% of the considered programs only.
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