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.
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 -