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Automatically Detecting and Mitigating Issues in Program Analyzers

dc.contributor.authorMansur, Muhammad Numair
dc.contributor.editorHerrmann, Andrea
dc.date.accessioned2024-07-26T10:37:42Z
dc.date.available2024-07-26T10:37:42Z
dc.date.issued2024
dc.description.abstractThis dissertation tackles two major challenges that impede the incorporation of static analysis tools into software development workflows, despite their potential to detect bugs and vulnerabilities in software before deployment. The first challenge addressed is unintentional unsoundness in program analyzers, such as SMT solvers and Datalog engines, which are susceptible to undetected soundness issues that can lead to severe consequences, particularly in safety-critical software. The dissertation presents novel, publicly available techniques that detected over 55 critical soundness bugs in these tools. The second challenge is balancing soundness, precision, and performance in static analyzers, which struggle with integration into diverse development scenarios due to their inability to scale and adapt to different program sizes and resource constraints. To combat this, the dissertation introduces an approach to automatically tailor abstract interpreters to specific code and resource conditions and presents a method for horizontally scaling analysis tools in cloud-based platforms.en
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/44196
dc.language.isoen
dc.pubPlaceBonn
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSoftwaretechnik-Trends Band 44, Heft 2
dc.relation.ispartofseriesSoftwaretechnik-Trends
dc.subjectStatic Analysis
dc.subjectSoundness
dc.subjectPrecision
dc.subjectPerformance
dc.subjectScalability
dc.subjecttool
dc.titleAutomatically Detecting and Mitigating Issues in Program Analyzersen
dc.typeText/Journal Article
mci.reference.pages68-69

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