Name
Abstract | ||
|---|---|---|
We target the problem of software bug reports classification. Our main aim is to build a classifier that is capable of classifying newly incoming bug reports into two predefined classes: corrective (defect fixing) report and perfective (major maintenance) report. This helps maintainers to quickly understand these bug reports and hence, allocate resources for each category. For this purpose, we propose a distinctive feature set that is based on the occurrences of certain keywords. The proposed feature set is then fed into a number of classification algorithms for building a classification model. The results of the proposed feature set achieved high accuracy in classification with SVM classification algorithm reporting an average accuracy of (93.1%) on three different open source projects.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3357419.3357424 | Proceedings of the 9th International Conference on Information Communication and Management |
Keywords | Field | DocType |
Software maintenance, automatic classification, bug reports | Software engineering,Computer science,Software bug | Conference |
ISBN | Citations | PageRank |
978-1-4503-7188-9 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmed Fawzi Otoom | 1 | 12 | 5.76 |
| Sara Al-jdaeh | 2 | 0 | 0.34 |
| Maen Hammad | 3 | 95 | 6.95 |