A Posteriori Soundness for Non-deterministic Abstract Interpretations
VMCAI '09 Proceedings of the 10th International Conference on Verification, Model Checking, and Abstract Interpretation
Proceedings of the 14th international conference on Architectural support for programming languages and operating systems
Proceedings of the 38th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Data-driven equivalence checking
Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications
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This dissertation describes a new program analysis technique called random interpretation that uses the power of randomization to verify and discover program properties. Random interpretation is inspired by, and combines the strengths of, the two complementary techniques for program analysis: random testing and abstract interpretation. Random testing is simple and finds real bugs in programs, but cannot prove absence of bugs. Abstract interpretation, on the other hand, is a class of sound and deterministic program analyses that find all bugs, but also report spurious bugs (false positives). Often these analyses are complicated and have long running time. In this dissertation, we describe few random interpretation based program analyses that are more efficient as well as simpler than their deterministic counterparts that had been state-of-the-art for almost 30 years. We then show how to extend these intra-procedural analyses to an inter-procedural setting, and how to combine these analyses. We also discuss our experience experimenting with some of these algorithms.