Name
Abstract | ||
|---|---|---|
Static program analysis often encounters problems in analyzing library code. Most real-world programs use library functions intensively, and library functions are usually written in different languages. For example, static analysis of JavaScript programs requires analysis of the standard built-in library implemented in host environments. A common approach to analyze such opaque code is for analysis developers to build models that provide the semantics of the code. Models can be built either manually, which is time consuming and error prone, or automatically, which may limit application to different languages or analyzers. In this paper, we present a novel mechanism to support automatic modeling of opaque code, which is applicable to various languages and analyzers. For a given static analysis, our approach automatically computes analysis results of opaque code via dynamic testing during static analysis. By using testing techniques, the mechanism does not guarantee sound over-approximation of program behaviors in general. However, it is fully automatic, is scalable in terms of the size of opaque code, and provides more precise results than conventional over-approximation approaches. Our evaluation shows that although not all functionalities in opaque code can (or should) be modeled automatically using our technique, a large number of JavaScript built-in functions are approximated soundly yet more precisely than existing manual models. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-16722-6_3 | FASE |
Field | DocType | Citations |
Static program analysis,Programming language,Computer science,Static analysis,Dynamic testing,Opacity,Semantics,JavaScript,Scalability | Conference | 1 |
PageRank | References | Authors |
0.36 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joonyoung Park | 1 | 1 | 1.37 |
| Alexander Jordan | 2 | 3 | 2.07 |
| Sukyoung Ryu | 3 | 185 | 25.77 |