Pick your contexts well: understanding object-sensitivity

By Yannis Smaragdakis, Martin Bravenboer, Ondřej Lhoták. ACM-SIGACT Symposium on Principles of Programming Languages, 2011

332 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/80af0dfde58a4f1e4f7ff35fa2c882a4ab3bbad2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Patrick Traynor
2. Mayur Naik
3. William Enck
4. Martin C. Rinard
5. Kevin R. B. Butler
6. Xin Zhang
7. Ravi Mangal
8. Aditya V. Nori

In Computational Theory and Mathematics

1. Mayur Naik
2. Martin C. Rinard
3. Xin Zhang
4. Ravi Mangal
5. Aditya V. Nori

In Information Systems

1. Aditya V. Nori
2. Patrick Traynor
3. Mayur Naik
4. William Enck
5. Martin C. Rinard
6. Kevin R. B. Butler
7. Jeff H. Perkins

Citations in the corpus

• A user-guided approach to program analysis (2015, methodology): “Datalog has been shown to suffice for expressing a variety of analyses, including pointer and call-graph analyses [7,40,41,44], concurrency analyses [30,31], security analyses [11,27], and reflection analysis [25].”
• Information Flow Analysis of Android Applications in DroidSafe (2015, methodology): “For a rigorous description of object sensitivity see [18] (note that our PTA implements a 3Full+2Heap analysis modified as described below).”
• *droid (2016, background): “Others are precise but not necessarily sound, such as the many precision-increasing variations on points-to sets [Lhot´ak and Hendren 2003; Smaragdakis et al. 2011, 2014].”