使用特徵選取方法改進軟體錯誤報告嚴重性預測之研究

Improving Severity Prediction of Defect Reports using Feature Selection

侯俊吉(Chun-Chi Hou)

指導教授 ： 楊正仁

元智大學/資訊學院/資訊工程學系/碩士(2013年)

https://doi.org/10.6838/YZU.2013.00320

摘要

軟體錯誤報告在軟體維護中扮演重要的角色。對於嚴重的報告，軟體工程師往往都會優先處理。因此軟體錯誤報告的嚴重性預測成為近年來一個重要議題。過往研究雖然討論了錯誤報告中具有嚴重性的代表字詞，但並未討論如何運用它們來增強預測表現。本研究因此以六種特徵選取方法來抽取嚴重性與非嚴重性代表詞，加權之後來增強預測效能。經由實驗顯示，透過適當的加權，預測的效能都可以提升。

關鍵字

軟體錯誤報告 ； 嚴重性預測 ； 特徵選取 ； 嚴重性代表詞

並列摘要

Defect reports play an important role in software maintenance. Software engineers often process severe defect reports in their first priority. Therefore, severity prediction of defect reports becomes an important research issue. Although past studies have discussed the representative indicators of the severity, they do not discuss the usage of the indicators to improve the prediction performance. This research addresses this issue with six feature selection methods to extract both severe indicators and non-severe indicators. The experimental results show that appropriately reweighting these indicators can improve the prediction performance.

並列關鍵字

Defect Reports ； Severity Prediction ； Feature Selection ； Severe Indicators

參考文獻

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