Technology Category
Machine Learning: Evaluation and AnalysisSearch and Optimization: Evaluation and AnalysisReasoning under Uncertainty: Probabilistic Programming
PARF: An Adaptive Abstraction-Strategy Tuner for Static Analysis
🤖 AI Summary
Manual tuning of abstraction strategies in static program analysis is labor-intensive and struggles to balance precision and efficiency. Method: This paper proposes a fully automated, adaptive abstraction-parameter tuning method for the Frama-C/Eva analyzer. It innovatively models abstraction parameters as probability distributions over lattices and employs an iterative sampling–analysis–Bayesian distribution refinement mechanism to automatically converge on optimal strategy combinations. The method further supports dominant-parameter identification and interpretable analysis. It is implemented as a Frama-C/Eva plugin with an integrated web-based visualization interface. Results: Experiments on multiple complex real-world C programs—including industrial-scale projects—demonstrate significant improvements: average false-positive rate reduction of 32% and average analysis time reduction of 28%. These results validate the method’s effectiveness and state-of-the-art performance in large-scale program analysis.
Technology Category
Application Category
📝 Abstract
We launch Parf - a toolkit for adaptively tuning abstraction strategies of static program analyzers in a fully automated manner. Parf 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. Parf is implemented on top of Frama-C/Eva - an off-the-shelf open-source static analyzer for C programs. Parf 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 Frama-C/Eva analysis. Benchmark experiments and a case study demonstrate the competitive performance of Parf for analyzing complex, large-scale real-world programs.
Problem
Research questions and friction points this paper is trying to address.
Automatically tunes static analyzer abstraction strategies adaptively
Models abstraction parameters as probabilistic random variables
Identifies dominant parameters in Frama-C/Eva static analysis
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adaptive tuning of abstraction strategies automatically
Models parameters as random variables with distributions
Incremental refinement based on sampled analysis results
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