Automatically assigned DDC number: 005131

Manually assigned DDC number: 005131

Number of references: 35

Title: Dynamically Discovering Likely Program Invariants to Support Program Evolution

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Subject: Michael D. Ernst,Jake Cockrell,William G. Griswold,David Notkin Dynamically Discovering Likely Program Invariants to Support Program Evolution

Description: Explicitly stated program invariants can help programmers by identifying program properties that must be preserved when modifying code. In practice, however, these invariants are usually implicit. An alternative to expecting programmers to fully annotate code with invariants is to automatically infer likely invariants from the program itself. This research focuses on dynamic techniques for discovering invariants from execution traces. This article reports three results. First, it describes techniques for dynamically discovering invariants, along with an implementation, named Daikon, that embodies these techniques. Second, it reports on the application of Daikon to two sets of target programs. In programs from Gries's work on program derivation, the system rediscovered predefined invariants. In a C program lacking explicit invariants, the system discovered invariants that assisted a software evolution task. These experiments demonstrate that, at least for small programs, invariant inference is both accurate and useful. Third, it analyzes scalability issues such as invariant dectection runtime and accuracy as functions of test suites and program points instrumented.

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 2000-04-24

Pubyear: 2000

Format: ps

Identifier: http://citeseer.ist.psu.edu/292512.html

Source: http://www.cs.washington.edu/homes/mernst/pubs/invariants-tse.ps.gz

Language: en

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