We launch P_(ARF)—a toolkit for adaptively tuning abstraction strategies of static program analyzers in a fully automated manner.P_(ARF) models various types of external parameters(encoding abstraction strategies)as ...We launch P_(ARF)—a toolkit for adaptively tuning abstraction strategies of static program analyzers in a fully automated manner.P_(ARF) models various types of external parameters(encoding abstraction strategies)as random variables subject to probability distributions over latticed parameter spaces.It incrementally refines the probability distributions based on accumulated intermediate results generated by repeatedly sampling and analyzing,thereby ultimately yielding a set of highly accurate abstraction strategies.P_(ARF) is implemented on top of F_(RAMA)-C/E_(VA)—an off-the-shelf open-source static analyzer for C programs.P_(ARF) provides a web-based user interface facilitating the intuitive configuration of static analyzers and visualization of dynamic distribution refinement of the abstraction strategies.It further supports the identification of dominant parameters in F_(RAMA)-C/E_(VA) analysis.Benchmark experiments and a case study demonstrate the competitive performance of P_(ARF) for analyzing complex,large-scale real-world programs.展开更多
基金supported by the Zhejiang Provincial Natural Science Foundation Major Program under Grant No.LD24F020013the CCF-Huawei Populus Grove Fund under Grant No.CCF-HuaweiFM202301+1 种基金the Fundamental Research Funds for the Central Universities of China under Grant No.226-2024-00140the Zhejiang University Education Foundation's Qizhen Talent Program.
文摘We launch P_(ARF)—a toolkit for adaptively tuning abstraction strategies of static program analyzers in a fully automated manner.P_(ARF) models various types of external parameters(encoding abstraction strategies)as random variables subject to probability distributions over latticed parameter spaces.It incrementally refines the probability distributions based on accumulated intermediate results generated by repeatedly sampling and analyzing,thereby ultimately yielding a set of highly accurate abstraction strategies.P_(ARF) is implemented on top of F_(RAMA)-C/E_(VA)—an off-the-shelf open-source static analyzer for C programs.P_(ARF) provides a web-based user interface facilitating the intuitive configuration of static analyzers and visualization of dynamic distribution refinement of the abstraction strategies.It further supports the identification of dominant parameters in F_(RAMA)-C/E_(VA) analysis.Benchmark experiments and a case study demonstrate the competitive performance of P_(ARF) for analyzing complex,large-scale real-world programs.
