Learning graph-based heuristics for pointer analysis without handcrafting application-specific features

We present Graphick, a new technique for automatically learning graph-based heuristics for pointer analysis. Striking a balance between precision and scalability of pointer analysis requires designing good analysis heuristics. For example, because applying context sensitivity to all methods in a rea...

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Veröffentlicht in:Proceedings of ACM on programming languages Jg. 4; H. OOPSLA; S. 1 - 30
Hauptverfasser: Jeon, Minseok, Lee, Myungho, Oh, Hakjoo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 13.11.2020
Schlagworte:
Automated static analysis
Formal methods
Software and its engineering
Software functional properties
Software organization and properties
Data-driven static analysis
Context sensitivity
Heap abstraction
Machine learning for program analysis
Pointer analysis
ISSN:2475-1421, 2475-1421
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Zusammenfassung:We present Graphick, a new technique for automatically learning graph-based heuristics for pointer analysis. Striking a balance between precision and scalability of pointer analysis requires designing good analysis heuristics. For example, because applying context sensitivity to all methods in a real-world program is impractical, pointer analysis typically uses a heuristic to employ context sensitivity only when it is necessary. Past research has shown that exploiting the program's graph structure is a promising way of developing cost-effective analysis heuristics, promoting the recent trend of ``graph-based heuristics'' that work on the graph representations of programs obtained from a pre-analysis. Although promising, manually developing such heuristics remains challenging, requiring a great deal of expertise and laborious effort. In this paper, we aim to reduce this burden by learning graph-based heuristics automatically, in particular without hand-crafted application-specific features. To do so, we present a feature language to describe graph structures and an algorithm for learning analysis heuristics within the language. We implemented Graphick on top of Doop and used it to learn graph-based heuristics for object sensitivity and heap abstraction. The evaluation results show that our approach is general and can generate high-quality heuristics. For both instances, the learned heuristics are as competitive as the existing state-of-the-art heuristics designed manually by analysis experts.
ISSN:2475-1421
2475-1421
DOI:10.1145/3428247