Mining effort data from the OSS repository of developer's bug fix activity
During the evolution of any software, efforts are made to fix bugs or to add new features in software. In software engineering, previous history of effort data is required to build an effort estimation model, which estimates the cost and complexity of any software. Therefore, the role of effort data...
| Main Authors: | , , , , |
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| Format: | Journal Article |
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Universiti Malaysia Sarawak
2010
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| Online Access: | http://hdl.handle.net/20.500.11937/45283 |
| _version_ | 1848757239121182720 |
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| author | Ahsan, S. Afzal, M. Zaman, S. Guetl, Christian Wotawa, F. |
| author_facet | Ahsan, S. Afzal, M. Zaman, S. Guetl, Christian Wotawa, F. |
| author_sort | Ahsan, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | During the evolution of any software, efforts are made to fix bugs or to add new features in software. In software engineering, previous history of effort data is required to build an effort estimation model, which estimates the cost and complexity of any software. Therefore, the role of effort data is indispensable to build state-of-the-art effort estimation models. Most of the Open Source Software does not maintain any effort related information. Consequently there is no state-of-the-art effort estimation model for Open Source Software, whereas most of the existing effort models are for commercial software. In this paper we present an approach to build an effort estimation model for Open Source Software. For this purpose we suggest to mine effort data from the history of the developer’s bug fix activities. Our approach determines the actual time spend to fix a bug, and considers it as an estimated effort.Initially, we use the developer’s bug-fix-activity data to construct the developer’s activity log-book. The log-book is used to store the actual time elapsed to fix a bug. Subsequently, the log-book information is used to mine the bug fix effort data. Furthermore, the developer’s bug fix activity data is used to define three different measures for the developer’s contribution or expertise level. Finally, we used the bug-fix-activity data to visualize the developer’s collaborations and the involved source files. In order to perform an experiment we selected the Mozilla open source project and downloaded 93,607 bug reports from the Mozilla project bug tracking system i.e., Bugzilla. We also downloaded the available CVS-log data from the Mozilla project repository. In this study we reveal that in case of Mozilla only 4.9% developers have been involved in fixing 71.5% of the reported bugs. |
| first_indexed | 2025-11-14T09:24:56Z |
| format | Journal Article |
| id | curtin-20.500.11937-45283 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:24:56Z |
| publishDate | 2010 |
| publisher | Universiti Malaysia Sarawak |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-452832017-01-30T15:19:50Z Mining effort data from the OSS repository of developer's bug fix activity Ahsan, S. Afzal, M. Zaman, S. Guetl, Christian Wotawa, F. mining effort data estimation models developer expertise and open source software Software repository During the evolution of any software, efforts are made to fix bugs or to add new features in software. In software engineering, previous history of effort data is required to build an effort estimation model, which estimates the cost and complexity of any software. Therefore, the role of effort data is indispensable to build state-of-the-art effort estimation models. Most of the Open Source Software does not maintain any effort related information. Consequently there is no state-of-the-art effort estimation model for Open Source Software, whereas most of the existing effort models are for commercial software. In this paper we present an approach to build an effort estimation model for Open Source Software. For this purpose we suggest to mine effort data from the history of the developer’s bug fix activities. Our approach determines the actual time spend to fix a bug, and considers it as an estimated effort.Initially, we use the developer’s bug-fix-activity data to construct the developer’s activity log-book. The log-book is used to store the actual time elapsed to fix a bug. Subsequently, the log-book information is used to mine the bug fix effort data. Furthermore, the developer’s bug fix activity data is used to define three different measures for the developer’s contribution or expertise level. Finally, we used the bug-fix-activity data to visualize the developer’s collaborations and the involved source files. In order to perform an experiment we selected the Mozilla open source project and downloaded 93,607 bug reports from the Mozilla project bug tracking system i.e., Bugzilla. We also downloaded the available CVS-log data from the Mozilla project repository. In this study we reveal that in case of Mozilla only 4.9% developers have been involved in fixing 71.5% of the reported bugs. 2010 Journal Article http://hdl.handle.net/20.500.11937/45283 Universiti Malaysia Sarawak fulltext |
| spellingShingle | mining effort data estimation models developer expertise and open source software Software repository Ahsan, S. Afzal, M. Zaman, S. Guetl, Christian Wotawa, F. Mining effort data from the OSS repository of developer's bug fix activity |
| title | Mining effort data from the OSS repository of developer's bug fix activity |
| title_full | Mining effort data from the OSS repository of developer's bug fix activity |
| title_fullStr | Mining effort data from the OSS repository of developer's bug fix activity |
| title_full_unstemmed | Mining effort data from the OSS repository of developer's bug fix activity |
| title_short | Mining effort data from the OSS repository of developer's bug fix activity |
| title_sort | mining effort data from the oss repository of developer's bug fix activity |
| topic | mining effort data estimation models developer expertise and open source software Software repository |
| url | http://hdl.handle.net/20.500.11937/45283 |