@article{Wang2025, 
author = {Zhong-Yi Wang and Ming-Shuai Chen and Teng-Jie Lin and Lin-Yu Yang and Jun-Hao Zhuo and Qiu-Ye Wang and Sheng-Chao Qin and Xiao Yi and Jian-Wei Yin},
title = {PARF: An Adaptive Abstraction-Strategy Tuner for Static Analysis},
year = {2025},
journal = {Journal of Computer Science and Technology},
volume = {40},
number = {4},
pages = {993-1005},
keywords = {static analysis, automatic parameter tuning, FRAMA-C/EVA, program verification, abstraction strategy},
url = {https://www.sciopen.com/article/10.1007/s11390-025-5140-6},
doi = {10.1007/s11390-025-5140-6},
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.}
}