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| Content Provider | ACM Digital Library |
|---|---|
| Author | Tassé, Josée |
| Abstract | Current approaches for defect prediction usually analyze files (or modules) and their development as work is done on a given release, to predict post-release defects. What is missing is an approach for predicting bugs to be detected in a more short-term interval, even within the development of a particular version. In this paper, we propose a defect predictor that looks into change bursts in a given file, analyzing the number of changes and their types, and then predict whether the file is likely to have a bug found within the next 3 months after that change burst. An analogy-based classifier is used for this task: the prediction is made based on comparisons with similar change bursts that occurred in other files. New metrics are described to capture the change type of a file (e.g., small local change, massive change all in one place, multiple changes scattered throughout the file). |
| Starting Page | 1 |
| Ending Page | 4 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450320160 |
| DOI | 10.1145/2499393.2499397 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-10-09 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Analogy-based classifier Short-term prediction Change burst Change type metrics Defect prediction |
| Content Type | Text |
| Resource Type | Article |
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