T ng of virtual method calls and instantiation of method summaries. Since our approach generates polymorphic method summaries, it can be context-sensitive without reanalyzing the same method multiple times. We have implemented this algorithm in a tool called Scuba, and we compare it with k-CFA and k-obj algorithms on Java applications from the DaCapo and Ashes benchmarks. Our results show that the new algorithm achieves better or comparable precision to k-CFA and k-obj analyses at only a fraction of the cost.
CITATION STYLE
Feng, Y., Wang, X., Dillig, I., & Dillig, T. (2015). Bottom-up context-sensitive pointer analysis for java. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9458, pp. 465–484). Springer Verlag. https://doi.org/10.1007/978-3-319-26529-2_25
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