The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
O resumo de relatórios de bugs foi explorado em pesquisas anteriores para ajudar os desenvolvedores a compreender informações importantes para o processo de resolução de bugs. À medida que a tecnologia de mineração de texto avança, muitas abordagens de resumo têm sido propostas para fornecer resumos substanciais sobre relatórios de bugs. Neste artigo, propomos uma abordagem de sumarização aprimorada chamada TSM, primeiro estendendo um modelo semântico usado no AUSUM com as informações antropogênicas e processuais em relatórios de bugs e depois integrando o modelo semântico estendido com as informações textuais superficiais usadas no BRC. Conduzimos experimentos com um conjunto de dados de projetos de software realistas. Em comparação com as abordagens de base BRC e AUSUM, o TSM demonstra o melhor desempenho na obtenção de melhorias relativas de 34.3% e 7.4% na medida F1, respectivamente. Os resultados experimentais mostram que o TSM pode efetivamente melhorar o desempenho.
Cheng-Zen YANG
Yuan Ze University
Cheng-Min AO
Yuan Ze University
Yu-Han CHUNG
Yuan Ze University
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Cheng-Zen YANG, Cheng-Min AO, Yu-Han CHUNG, "Towards an Improvement of Bug Report Summarization Using Two-Layer Semantic Information" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 7, pp. 1743-1750, July 2018, doi: 10.1587/transinf.2017KBP0016.
Abstract: Bug report summarization has been explored in past research to help developers comprehend important information for bug resolution process. As text mining technology advances, many summarization approaches have been proposed to provide substantial summaries on bug reports. In this paper, we propose an enhanced summarization approach called TSM by first extending a semantic model used in AUSUM with the anthropogenic and procedural information in bug reports and then integrating the extended semantic model with the shallow textual information used in BRC. We have conducted experiments with a dataset of realistic software projects. Compared with the baseline approaches BRC and AUSUM, TSM demonstrates the enhanced performance in achieving relative improvements of 34.3% and 7.4% in the F1 measure, respectively. The experimental results show that TSM can effectively improve the performance.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017KBP0016/_p
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@ARTICLE{e101-d_7_1743,
author={Cheng-Zen YANG, Cheng-Min AO, Yu-Han CHUNG, },
journal={IEICE TRANSACTIONS on Information},
title={Towards an Improvement of Bug Report Summarization Using Two-Layer Semantic Information},
year={2018},
volume={E101-D},
number={7},
pages={1743-1750},
abstract={Bug report summarization has been explored in past research to help developers comprehend important information for bug resolution process. As text mining technology advances, many summarization approaches have been proposed to provide substantial summaries on bug reports. In this paper, we propose an enhanced summarization approach called TSM by first extending a semantic model used in AUSUM with the anthropogenic and procedural information in bug reports and then integrating the extended semantic model with the shallow textual information used in BRC. We have conducted experiments with a dataset of realistic software projects. Compared with the baseline approaches BRC and AUSUM, TSM demonstrates the enhanced performance in achieving relative improvements of 34.3% and 7.4% in the F1 measure, respectively. The experimental results show that TSM can effectively improve the performance.},
keywords={},
doi={10.1587/transinf.2017KBP0016},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - Towards an Improvement of Bug Report Summarization Using Two-Layer Semantic Information
T2 - IEICE TRANSACTIONS on Information
SP - 1743
EP - 1750
AU - Cheng-Zen YANG
AU - Cheng-Min AO
AU - Yu-Han CHUNG
PY - 2018
DO - 10.1587/transinf.2017KBP0016
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2018
AB - Bug report summarization has been explored in past research to help developers comprehend important information for bug resolution process. As text mining technology advances, many summarization approaches have been proposed to provide substantial summaries on bug reports. In this paper, we propose an enhanced summarization approach called TSM by first extending a semantic model used in AUSUM with the anthropogenic and procedural information in bug reports and then integrating the extended semantic model with the shallow textual information used in BRC. We have conducted experiments with a dataset of realistic software projects. Compared with the baseline approaches BRC and AUSUM, TSM demonstrates the enhanced performance in achieving relative improvements of 34.3% and 7.4% in the F1 measure, respectively. The experimental results show that TSM can effectively improve the performance.
ER -