NDLI: Efficient incremental algorithms for dynamic detection of likely invariants

Content Provider	ACM Digital Library
Author	Perkins, Jeff H. Ernst, Michael D.
Abstract	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. Operational abstractions 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 incremental algorithms. The result is more scalable invariant detection that does not sacrifice functionality.
Starting Page	23
Ending Page	32
Page Count	10
File Format	PDF
ISSN	01635948
DOI	10.1145/1041685.1029901
Journal	ACM SIGSOFT Software Engineering Notes (SOEN)
Volume Number	29
Issue Number	6
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1979-04-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Incremental algorithm Dynamic invariant detection Reversing optimizations Batch algorithm
Content Type	Text
Resource Type	Article

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