Cross-language bug localization
Bug localization refers to the process of identifying source code files that contain defects from textual descriptions in bug reports. Existing bug localization techniques work on the assumption that bug reports, and identifiers and comments in source code files, are written in the same language (i....
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2014
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sg-smu-ink.sis_research-34222015-11-15T13:49:19Z Cross-language bug localization Xia, Xin LO, David Wang, Xingen Zhang, Chenyi Wang, Xinyu Bug localization refers to the process of identifying source code files that contain defects from textual descriptions in bug reports. Existing bug localization techniques work on the assumption that bug reports, and identifiers and comments in source code files, are written in the same language (i.e., English). However, software users from non-English speaking countries (e.g., China) often use their native languages (e.g., Chinese) to write bug reports. For this setting, existing studies on bug localization would not work as the terms that appear in the bug reports do not appear in the source code. We refer to this problem as cross-language bug localization. In this paper, we propose a cross-language bug localization algorithm named CrosLocator, which is based on language translation. Since different online translators (e.g., Google and Microsoft translators) have different translation accuracies for various texts, CrosLocator uses multiple translators to convert a non-English textual description of a bug report into English -- each bug report would then have multiple translated versions. For each translated version, CrosLocator applies a bug localization technique to rank source code files. Finally, CrosLocator combines the multiple ranked lists of source code files. Our preliminary experiment on Ruby-China shows that CrosLocator could achieve mean reciprocal rank (mrr) and mean average precision (map) scores of up to 0.146 and 0.116, which outperforms a baseline approach by an average of 10% and 12% respectively. 2014-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2422 info:doi/10.1145/2597008.2597788 https://ink.library.smu.edu.sg/context/sis_research/article/3422/viewcontent/p275_xia.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Bug Localization Cross-language Translator Rank Software Engineering |
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Bug Localization Cross-language Translator Rank Software Engineering Xia, Xin LO, David Wang, Xingen Zhang, Chenyi Wang, Xinyu Cross-language bug localization |
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Bug localization refers to the process of identifying source code files that contain defects from textual descriptions in bug reports. Existing bug localization techniques work on the assumption that bug reports, and identifiers and comments in source code files, are written in the same language (i.e., English). However, software users from non-English speaking countries (e.g., China) often use their native languages (e.g., Chinese) to write bug reports. For this setting, existing studies on bug localization would not work as the terms that appear in the bug reports do not appear in the source code. We refer to this problem as cross-language bug localization. In this paper, we propose a cross-language bug localization algorithm named CrosLocator, which is based on language translation. Since different online translators (e.g., Google and Microsoft translators) have different translation accuracies for various texts, CrosLocator uses multiple translators to convert a non-English textual description of a bug report into English -- each bug report would then have multiple translated versions. For each translated version, CrosLocator applies a bug localization technique to rank source code files. Finally, CrosLocator combines the multiple ranked lists of source code files. Our preliminary experiment on Ruby-China shows that CrosLocator could achieve mean reciprocal rank (mrr) and mean average precision (map) scores of up to 0.146 and 0.116, which outperforms a baseline approach by an average of 10% and 12% respectively. |
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Xia, Xin LO, David Wang, Xingen Zhang, Chenyi Wang, Xinyu |
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Xia, Xin LO, David Wang, Xingen Zhang, Chenyi Wang, Xinyu |
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Xia, Xin |
title |
Cross-language bug localization |
title_short |
Cross-language bug localization |
title_full |
Cross-language bug localization |
title_fullStr |
Cross-language bug localization |
title_full_unstemmed |
Cross-language bug localization |
title_sort |
cross-language bug localization |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2014 |
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https://ink.library.smu.edu.sg/sis_research/2422 https://ink.library.smu.edu.sg/context/sis_research/article/3422/viewcontent/p275_xia.pdf |
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