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Dynamic Analysis of Parallel Applications Using .NET Framework
Ling, David ; Hrubý, Martin (referee) ; Smrčka, Aleš (advisor)
The thesis deals with a design and implementation of the dynamic analyser of parallel applications on the .NET Framework platform. The problematic of synchronization in parallel applications, the instrumentation of such an applications, testing of parallel applications and a specifics of these problems for C\# language and for the platform .NET Framework are discussed in the theoretical part. Selected algorithms for detection of deadlocks (the algorithm of Goodlock) and data-race errors (the algorithm of FastTrack and AtomRace) are described in detail in this part as well. Requirements for the dynamic analyser and the system design is made in the following part of this thesis. The thesis also contains a description of the implementation of the proposed solution, a description of the entire testing of the implemented tool. Last but not least, the thesis describes the sample of using dynamic analysers in a particular application environment.
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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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Dynamic Analysis of Parallel Applications Using .NET Framework
Ling, David ; Hrubý, Martin (referee) ; Smrčka, Aleš (advisor)
The thesis deals with a design and implementation of the dynamic analyser of parallel applications on the .NET Framework platform. The problematic of synchronization in parallel applications, the instrumentation of such an applications, testing of parallel applications and a specifics of these problems for C\# language and for the platform .NET Framework are discussed in the theoretical part. Selected algorithms for detection of deadlocks (the algorithm of Goodlock) and data-race errors (the algorithm of FastTrack and AtomRace) are described in detail in this part as well. Requirements for the dynamic analyser and the system design is made in the following part of this thesis. The thesis also contains a description of the implementation of the proposed solution, a description of the entire testing of the implemented tool. Last but not least, the thesis describes the sample of using dynamic analysers in a particular application environment.
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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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