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SIGSOFT
2004
ACM

Efficient incremental algorithms for dynamic detection of likely invariants

14 years 4 months ago
Efficient incremental algorithms for dynamic detection of likely invariants
Dynamic detection of likely invariants is a program analysis that generalizes over observed values to hypothesize program properties. The reported program properties are a set of likely invariants over the program, also known as an operational abstraction. Operabstractions are useful in testing, verification, bug detection, refactoring, comparing behavior, and many other tasks. Previous techniques for dynamic invariant detection scale poorly or report too few properties. Incremental algorithms are attractive because they process each observed value only once and thus scale well with data sizes. Previous incremental algorithms only checked and reported a small number of properties. This paper takes steps toward correcting this problem. The paper presents two new incremental algorithms for invariant detection and compares them analytically and experimentally to two existing algorithms. Furthermore, the paper presents four optimizations and shows how to implement them in the context of i...
Jeff H. Perkins, Michael D. Ernst
Added 20 Nov 2009
Updated 20 Nov 2009
Type Conference
Year 2004
Where SIGSOFT
Authors Jeff H. Perkins, Michael D. Ernst
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