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csallner06dynamically - Dynamically Discovering Likely...

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Dynamically Discovering Likely Interface Invariants Christoph Csallner, Yannis Smaragdakis College of Computing Georgia Institute of Technology Atlanta, GA 30332, USA { csallner,yannis } @cc.gatech.edu ABSTRACT Dynamic invariant detection is an approach that has re- ceived considerable attention in the recent research liter- ature. A natural question arises in languages that sepa- rate the interface of a code module from its implementa- tion: does an inferred invariant describe the interface or the implementation? Furthermore, if an implementation is al- lowed to refine another, as, for instance, in object-oriented method overriding, what is the relation between the inferred invariants of the overriding and the overridden method? The problem is of great practical interest. Invariants derived by real tools, like Daikon, often suffer from internal inconsisten- cies when overriding is taken into account, becoming unsuit- able for some automated uses. We discuss the interactions between overriding and inferred invariants, and describe the implementation of an invariant inference tool that produces consistent invariants for interfaces and overridden methods. Categories and Subject Descriptors: D.2.7 [ Software Engineering ]: Distribution, Maintenance, and Enhance- ment— Restructuring, reverse engineering, and reengineer- ing ; D.3.3 [ Programming Languages ]: Language Con- structs and Features— Abstract data types, polymorphism, and inheritance ; F.3.1 [ Logics and Meanings of Pro- grams ]: Specifying and Verifying and Reasoning about Programs— Pre- and post-conditions General Terms: Algorithms, Documentation, Languages Keywords: Dynamic analysis, invariant detection, inter- faces, method overriding 1. INTRODUCTION Dynamic invariant detection tools like Daikon [3] and DIDUCE [4] have attracted a lot of attention in the re- cent research literature. Such tools attempt to monitor a large number of program executions and heuristically infer abstract logical properties of the program. Empirically, the invariant detection approach has proven effective for pro- gram understanding tasks. Nevertheless, the greatest value of program specifications is in automating program reason- ing tasks. Indeed, Daikon produces specifications in several formal specification languages (e.g., in JML [5] for Java) and the resulting annotations have been used to automatically guide tools such as test case generators [9, 7]. Using inferred invariants automatically in other tools Copyright is held by the author/owner. ICSE’06, May 20–28, 2006, Shanghai, China. ACM 1-59593-085-X/06/0005. places a much heavier burden on the invariant inference en- gine. Treating inferred invariants, which are heuristics, as true invariants means that they need to be internally consis- tent. Otherwise a single contradiction is sufficient to throw off any automatic reasoning engine (be it a theorem prover, a constraint solver, a model checker, or other) that uses the invariants. In this paper, we discuss how an invariant detection tool can produce consistent invariants in a lan- guage that allows indirection in the calling of code. Object-
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