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invariants-incremental-fse2004

invariants-incremental-fse2004 - Efcient Incremental...

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Efficient Incremental Algorithms for Dynamic Detection of Likely Invariants Jeff H. Perkins Michael D. Ernst Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory 32 Vassar Street, Cambridge MA USA [email protected], [email protected] Abstract Dynamic detection of likely invariants is a program analysis that generalizes over observed values to hypothesize program proper- ties. The reported program properties are a set of likely invariants over the program, also known as an operational abstraction. Opera- tional 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 in- cremental algorithms for invariant detection and compares them analytically and experimentally to two existing algorithms. Fur- thermore, the paper presents four optimizations and shows how to implement them in the context of incremental algorithms. The re- sult is more scalable invariant detection that does not sacrifice func- tionality. Categories and Subject Descriptors: F.3.1 [ Logics and Mean- ings of Programs ]: Specifying and Verifying and Reasoning about Programs— Invariants General Terms: Algorithms, Performance Keywords: dynamic invariant detection, incremental algorithm, batch algorithm, reversing optimizations 1. Introduction This paper presents and evaluates algorithms and optimizations for obtaining an operational abstraction — a formal description of properties that held on a series of program runs and can be expected to hold on future runs. The task of generating an operational ab- straction is also known as dynamic detection of likely invariants, or dynamic invariant detection. Dynamic invariant detection is an important and practical prob- lem. Operational abstractions have been used in verifying safety properties [35, 30, 31], automating theorem-proving [27, 28], iden- tifying refactoring opportunities [19], predicate abstraction [8, 9], generating test cases [38, 39, 13, 14], selecting and prioritizing test cases [16], explaining test failures [12], predicting incompatibilities Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.
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