Change Pattern Detection for Optimising Incremental Static Analysis

Research output: Chapter in Book/Report/Conference proceedingConference paper


Static analyses can be used by developers to compute properties of a program, enabling e.g., bug detection and program verification. However, reanalysing a program from scratch upon every change is time-consuming, especially in settings where code changes often, such as within IDEs. To avoid such full reanalyses, incremental analyses instead reuse parts of the previous analysis result, and reanalyse the changed code as necessary.

While incrementality improves the analysis time, we introduce a complementary approach that further reduces the analysis time. A traditional incremental analysis updates previous analysis results without domain-specific knowledge. However, the effect of particular source code changes on analysis results can be predicted. Performing a traditional incremental analysis of the changed code might therefore be unnecessary. Instead, we propose to detect code change patterns of which the effect on analysis results can be predicted and to update these results accordingly, saving potentially expensive computations.

In this paper, we explore the idea of adapting the analysis results for behaviour-preserving change patterns. In particular, we consider consistent renamings, inverted conditionals, and moved function definitions within Scheme programs. We implemented our approach and evaluated it on 30 programs. We show decreases in incremental analysis time between 3% and 99% on 25 programs that contain at least one behaviour-preserving change pattern.
Original languageEnglish
Title of host publicationSCAM 23rd IEEE International Working Conference on Source Code Analysis and Manipulation
Publication statusAccepted/In press - Oct 2023


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