| Title | Fast and Precise Application Code Analysis using a Partial Library |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Utture, Akshay, Palsberg, Jens |
| Conference Name | 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) |
| Date Published | may |
| Keywords | codes, composability, Costs, Human Behavior, Libraries, pubcrawl, Resiliency, software engineering, static analysis, static code analysis |
| Abstract | Long analysis times are a key bottleneck for the widespread adoption of whole-program static analysis tools. Fortunately, however, a user is often only interested in finding errors in the application code, which constitutes a small fraction of the whole program. Current application-focused analysis tools overapproximate the effect of the library and hence reduce the precision of the analysis results. However, empirical studies have shown that users have high expectations on precision and will ignore tool results that don't meet these expectations. In this paper, we introduce the first tool QueryMax that significantly speeds up an application code analysis without dropping any precision. QueryMax acts as a pre-processor to an existing analysis tool to select a partial library that is most relevant to the analysis queries in the application code. The selected partial library plus the application is given as input to the existing static analysis tool, with the remaining library pointers treated as the bottom element in the abstract domain. This achieves a significant speedup over a whole-program analysis, at the cost of a few lost errors, and with no loss in precision. We instantiate and run experiments on QueryMax for a cast-check analysis and a null-pointer analysis. For a particular configuration, QueryMax enables these two analyses to achieve, relative to a whole-program analysis, an average recall of 87%, a precision of 100% and a geometric mean speedup of 10x. |
| DOI | 10.1145/3510003.3510046 |
| Citation Key | utture_fast_2022 |
Fast and Precise Application Code Analysis using a Partial Library