Software fault identification via dynamic analysis and machine learning
Name
56822830-MIT.pdf
Description
Full printable version
Size
2.73 MB
Format
Adobe PDF
Checksum (MD5)
29fd6c411fcb27293d6c353c8cf2a067
Author(s)
Brun, Yuriy, 1981-
Advisor(s)
Michael D. Ernst.
Date Issued
2003
Publisher
Massachusetts Institute of Technology
Abstract
I propose a technique that identifies program properties that may indicate errors. The technique generates machine learning models of run-time program properties known to expose faults, and applies these models to program properties of user-written code to classify and rank properties that may lead the user to errors. I evaluate an implementation of the technique, the Fault Invariant Classifier, that demonstrates the efficacy of the error finding technique. The implementation uses dynamic invariant detection to generate program properties. It uses support vector machine and decision tree learning tools to classify those properties. Given a set of properties produced by the program analysis, some of which are indicative of errors, the technique selects a subset of properties that are most likely to reveal an error. The experimental evaluation over 941,000 lines of code, showed that a user must examine only the 2.2 highest-ranked properties for C programs and 1.7 for Java programs to find a fault-revealing property. The technique increases the relevance (the concentration of properties that reveal errors) by a factor of 50 on average for C programs, and 4.8 for Java programs.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (p. 65-67).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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