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  1. Proceedings of the 2008 workshop on Defects in large software systems (DEFECTS '08)
  2. Predicting fault-prone modules based on metrics transitions
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A report on a survey and study of static analysis users
A criterion for filtering code clone related bugs
Predicting fault-prone modules based on metrics transitions
Defect classification and defect types revisited
Exploring the relationship of history characteristics and defect count: an empirical study
Retrieving similar code fragments based on identifier similarity for defect detection
Can data transformation help in the detection of fault-prone modules?
Do bad smells indicate "trouble" in code?
The impact of product development on the lifecycle of defects
Nearest neighbor sampling for cross company defect predictors: abstract only
Comparing methods to identify defect reports in a change management database
SZZ revisited: verifying when changes induce fixes

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Predicting fault-prone modules based on metrics transitions

Content Provider ACM Digital Library
Author Kusumoto, Shinji Higo, Yoshiki Murao, Kenji Inoue, Katsuro
Abstract This paper describe a method for identifying fault-prone modules. The method utilizes metrics transitions rather than raw metrics values. Metrics transitions are measured from the source code of consecutive versions, which is archived in software repositories. Metrics transitions should be an good indicator of software quality because they reflect how the software system has evolved. This paper exhibits a case study, which is a comparison between metrics transitions and CK metrics suite. In the case study, the metrics transitions could precisely identify fault-prone modules.
Starting Page 6
Ending Page 10
Page Count 5
File Format PDF
ISBN 9781605580517
DOI 10.1145/1390817.1390820
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2008-07-20
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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