Year: 2025
Type: article
Abstract: In the context of static analysis based on abstract interpretation, we propose a lightweight pre-analysis step which is meant to suggest, at each program point, which program variables are likely to be unconstrained for a specific class of numeric abstract properties. Using the outcome of this pre-analysis step as an oracle, we simplify the statements of the program being analyzed by propagating this lack of information, aiming at fine-tuning the precision/efficiency trade-off of the target static analysis. A thorough experimental evaluation considering real world programs shows that the idea underlying the approach is promising. We first discuss and evaluate several variants of the pre-analysis step, measuring their accuracy at predicting unconstrained variables, so as to identify the most effective ones. Then we evaluate how these pre-analyses affect the target static analysis, showing that they can improve the efficiency of the more costly analysis while having a limited effect on its precision.
Source: PeerJ Computer Science
Institutions: University of Parma, Ca' Foscari University of Venice
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,395
Funder: Università degli Studi di Parma
OpenAlex ID: W4416777822