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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shao, D. Khurshid, S. Perry, D.E. |
| Copyright Year | 2009 |
| Abstract | Changes to source code have become a critical factor in fault predictions. Text or syntactic approaches have been widely used. Textual analysis focuses on changed text fragments while syntactic analysis focuses on changed syntactic entities. Although both of them have demonstrated their advantages in experimental results, they only study code fragments modified during changes. Because of semantic dependencies within programs, we believe that code fragments impacted by changes are also helpful. Given a source code change, we identify its impact by program slicing along the variable def-use chains. To evaluate the effectiveness of change impacts in fault detection and prediction, we compare impacted code with changed code according to size and fault density. Our experiment on the change history of a successful industrial project shows that: fault density in changed and impacted fragments are higher than other areas; for large changes, their impacts have higher fault density than changes themselves; interferences within change impact contribute to the high fault density in large changes. Our study suggests that, like change itself, change impact is also a high priority indicator in fault prediction, especially for changes of large scales. |
| Starting Page | 131 |
| Ending Page | 141 |
| File Size | 213884 |
| Page Count | 11 |
| File Format | |
| ISBN | 9780769535999 |
| ISSN | 15300803 |
| DOI | 10.1109/ASWEC.2009.14 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-04-14 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fault detection History Data analysis Australia Software engineering Interference Large-scale systems Programming Software quality Design for experiments fault prediction semantic analysis empirical study version management system |
| Content Type | Text |
| Resource Type | Article |
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