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| Content Provider | ACM Digital Library |
|---|---|
| Author | Falessi, Davide Shull, Forrest Kidwell, Bill Huffman Hayes, Jane |
| Abstract | Bug classification is a well-established practice which supports important activities such as enhancing verification and validation (V&V) efficiency and effectiveness. The state of the practice is manual and hence classification errors occur. This paper investigates the sensitivity of the value of bug classification (specifically, failure type classification) to its error rate; i.e., the degree to which misclassified historic bugs decrease the V&V effectiveness (i.e., the ability to find bugs of a failure type of interest). Results from the analysis of an industrial database of more than 3,000 bugs show that the impact of classification error rate on V&V effectiveness significantly varies with failure type. Specifically, there are failure types for which a 5% classification error can decrease the ability to find them by 66%. Conversely, there are failure types for which the V&V effectiveness is robust to very high error rates. These results show the utility of future research aimed at: 1) providing better tool support for decreasing human errors in classifying the failure type of bugs, 2) providing more robust approaches for the selection of V&V techniques, and 3) including robustness as an important criterion when evaluating technologies. . |
| Starting Page | 512 |
| Ending Page | 515 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450327688 |
| DOI | 10.1145/2591062.2591122 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-05-31 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Metrics Bug classification Software quality Human factor Verification and validation Testing |
| Content Type | Text |
| Resource Type | Article |
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