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  1. Proceedings of the 8th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering (PASTE '08)
  2. Towards a more efficient static software change impact analysis method
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Program analysis for compiler validation
A field study in static extraction of runtime architectures
A non-null annotation inferencer for Java bytecode
Path projection for user-centered static analysis tools
Effective identification of failure-inducing changes: a hybrid approach
Java memory model aware software validation
Specification mining of symbolic scenario-based models
Sharing analysis of arrays, collections, and recursive structures
Addressing common crosscutting problems with Arcum
Towards a more efficient static software change impact analysis method
Elyze: enabling safe parallelism in event-driven servers
Static analysis for inference of explicit information flow
XFindBugs: eXtended FindBugs for AspectJ

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Towards a more efficient static software change impact analysis method

Content Provider ACM Digital Library
Author Jashki, Mohammad-Amin Zafarani, Reza Bagheri, Ebrahim
Abstract Impact analysis methods are commonly employed to reduce the likelihood of encountering faulty or unexpected behavior from a software program as a result of developers' oblivious modifications. In this paper, we propose a static impact analysis technique that creates clusters of closely associated software program files based on their co-modification history in the software repository. The proposed method benefits from dimensionality reduction techniques to reduce the complexity of the collected information and perform the impact analysis process faster. The method has been tested on four different open source project repositories, namely Firefox, Firebird, Thunderbird, and FileZilla. The results of the impact analysis method performance in terms of precision (impact set identification accuracy) and execution time cost have been reported in this paper. The proposed method shows promising behavior when used with several specific clustering techniques such as DBscan and X-Means.
Starting Page 84
Ending Page 90
Page Count 7
File Format PDF
ISBN 9781605583822
DOI 10.1145/1512475.1512493
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2008-11-09
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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