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研究生:高瑋辰
研究生(外文):Wei-Chen Kao
論文名稱:利用隱含標籤機制來改進軟體錯誤報告嚴重性預測之研究
論文名稱(外文):Improving Severity Prediction of Software Bug Reports with Implicitly Tagging
指導教授:楊正仁楊正仁引用關係
指導教授(外文):Cheng-Zen Yang
口試委員:王正豪范金鳳
口試委員(外文):Jenq-Haur WangChin-Feng Fan
口試日期:2014-07-15
學位類別:碩士
校院名稱:元智大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:22
中文關鍵詞:軟體錯誤報告嚴重性預測標籤機制相似度計算
外文關鍵詞:Bug ReportsSeverity PredictionTagging MechanismSimilarity Computation
相關次數:
  • 被引用被引用:0
  • 點閱點閱:310
  • 評分評分:
  • 下載下載:8
  • 收藏至我的研究室書目清單書目收藏:0
對於大型軟體專案而言,軟體錯誤報告在軟體開發與維護中扮演極為重要的角
色。尤其每天都有許多錯誤報告產生,因此軟體錯誤的嚴重性將影響除錯的優先
順序。在本研究中,我們考慮錯誤報告中的標籤資訊,計算軟體錯誤報告間的語
意相似程度,來進行隱含標籤的標註,進而提升嚴重性預測的效果。實驗結果顯
示,所提出的標註標籤機制,在多數的狀況中可提升軟體錯誤報告之嚴重性預測
效果。
For large-scale software projects, bug reports play an important role in software development and maintenance. While many bug reports may be received daily, the severity of
the bug reports influences the fixing priority. In this research, we consider the abundant information embedded in tags, and propose a tagging mechanism to assign implicit tags
to bug reports by calculating the report similarity. The experimental results show that the proposed tagging mechanism can improve the prediction performance in the most cases.
目 錄
中文摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
英文摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
誌謝 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
目錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
表目錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
圖目錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1 Bug Tracking Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 Severity Prediction Research . . . . . . . . . . . . . . . . . . . . . . . . 4
2.3 BM25F ext Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3 Prediction Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.1 Report Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.1.1 Text Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.1.2 Tag Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2 Tagging Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.3 Classifier Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2 Evaluation Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.3.1 Influence of Tags . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.3.2 Performance of Severity Prediction . . . . . . . . . . . . . . . . 15
5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
[1] Liang Gou, Shaoke Zhang, Jing Wang, and Xiaolong (Luke) Zhang, “TagNetLens: Multiscale Visualization of Knowledge Structures in Social Tags,” in Proceedings of the 3rd International Symposium on Visual Information Communication (VINCI’10), 2010, pp. 18:1–18:9.
[2] Ahmed Lamkanfi, Serge Demeyer, Emanuel Giger, and Bart Goethals, “Predicting the Severity of a Reported Bug,” in Proceedings of the 7th IEEE Working Conference on Mining Software Repositories (MSR ’10), 2010, pp. 1–10.
[3] Ahmed Lamkanfi, Serge Demeyer, Quinten David Soetens, and Tim Verdonck,
“Comparing Mining Algorithms for Predicting the Severity of a Reported Bug,” in Proceedings of the 15th European Conference on Software Maintenance and Reengineering (CSMR 2011), 2011, pp. 249–258.
[4] Ahmed Lamkanfi, Javier P´erez, and Serge Demeyer, “The Eclipse and Mozilla Defect Tracking Dataset: A Genuine Dataset for Mining Bug Information,” in Proceedings of the 10th Working Conference on Mining Software Repositories (MSR ’13),2013, pp. 203–206.
[5] Tim Menzies and Andrian Marcus, “Automated Severity Assessment of Software Defect Reports,” in Proceedings of the 24th IEEE International Conference on Soft-ware Maintenance (ICSM 2008), Sep. 2008, pp. 346–355.
[6] Chengnian Sun, “Software Bug Management from Bug Reports to Bug Signatures,” Ph.D. dissertation, National University of Singapore, 2013.
[7] Chengnian Sun, David Lo, Siau-Cheng Khoo, and Jing Jiang, “Towards More Accurate Retrieval of Duplicate Bug Reports,” in Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE’11). IEEE, 2011, pp. 253–262.
[8] Yuan Tian, David Lo, and Chengnian Sun, “Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction,” in Proceedings
of the 19th Working Conference on Reverse Engineering (WCRE ’12), Oct. 2012, pp. 215–224.
[9] Michael Wurst, “Word Vector Tool,” http://sourceforge.net/projects/wvtool/.
[10] Jifeng Xuan, He Jiang, Zhilei Ren, and Weiqin Zou, “Developer Prioritization in Bug Repositories,” in Proceedings of the 34th International Conference on Software
Engineering (ICSE ’12), Jun. 2012, pp. 25–35.
[11] Cheng-Zen Yang, Chun-Chi Hou, Wei-Chen Kao, and Ing-Xiang Chen, “An Empirical Study on Improving Severity Prediction of Defect Reports Using Feature Selection,” in Proceedings of the 19th Asia-Pacific Software Engineering Conference(APSEC 2012), 2012, pp. 240–249.
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