Deep Bug Triage Model Based on Multi-head Self-attention Mechanism

Abstract

Abstract: At present, bug tracking system realizes the matching of bug and fixer through bug report. However, the previous bug triage model relies too much on the text quality of the bug report, introduces a lot of redundant information in natural language, and ignores the community relationship between the fixers when the meta field of the bug report is used as the label attribute, which makes the model performance worse. Aiming at the above problems, this paper proposes a multi-head self-attention deep bug triage (MSDBT). The text description of the bug report and the fixer sequence generated from meta field are vectorized; the multi-head self-attention mechanism is used to perform parallel attention calculation among the internal input elements. The results of experiments on four open source software projects show that MSDBT has obvious advantages over the previous model in terms of recall index.

Key words: bug tracking system, bug triage, deep learning, fixer community, multi-head self-attention mechanism

WAN Fa-yang, YU Xu, XU Qi-jiang. Deep Bug Triage Model Based on Multi-head Self-attention Mechanism[J]. Computer and Modernization, 2021, 0(11): 39-43.

References ［23］

［1］	CUBRANIC D, MURPHY G C. Automatic bug triage using text categorization［C］// Proceedings of the 16th International Conference on Software Engineering & Knowledge Engineering. 2004:92-97.
［2］	TAMRAWI A, NGUYEN T T, AL-KOFAHI J M, et al. Fuzzy set and cache-based approach for bug triaging［C］// Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering. 2011:365-375.
［3］	XUAN J F, JIANG H, REN Z L, et al. Developer prioritization in bug repositories［C］// Proceedings of the 34th International Conference on Software Engineering. 2012:25-35.
［4］	ANVIK J, HIEW L, MURPHY G C. Who should fix this bug?［C］// Proceedings of the 28th International Conference on Software Engineering. 2006:361-370.
［5］	TIAN Y, WIJEDASA D, LO D, et al. Learning to rank for bug report assignee recommendation［C］// Proceedings of the 24th IEEE International Conference on Program Comprehension. 2016. DOI: 10.1109/ICPC.2016.7503715.
［6］	GAY G, HAIDUC S, MARCUS A, et al. On the use of relevance feedback in IR-based concept location［C］// Proceedings of the 25th IEEE International Conference on Software Maintenance. 2009:351-360.
［7］	YE X, BUNESCU R， LIU C. Learning to rank relevant files for bug reports using domain knowledge［C］// Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering. 2014:689-699.
［8］	XIA X, LO D, DING Y, et al. Improving automated bug triaging with specialized topic model［J］. IEEE Transactions on Software Engineering, 2017,43(3):272-297.
［9］	YANG G, ZHANG T, LEE B. Towards semi-automatic bug triage and severity prediction based on topic model and multi-feature of bug reports［C］// Proceedings of the IEEE 38th Annual Computer Software and Applications Conference. 2014:97-106.〖HJ1.42mm〗
［10］	XIE X H, ZHANG W, YANG Y, et al. DRETOM: Developer recommendation based on topic models for bug resolution［C］// Proceedings of the 8th International Conference on Predictive Models in Software Engineering. 2012:19-28.
［11］	XIA X, LO D, WANG X Y, et al. Dual analysis for recommending developers to resolve bugs［J］. Journal of Software:Evolution and Process, 2015,27(3):195-220.
［12］	LEE S R, HEO M J, LEE C G, et al. Applying deep learning based automatic bug triager to industrial projects［C］// Proceedings of the 11th Joint Meeting on Foundations of Software Engineering. 2017:926-931.
［13］	MANI S, SANKARAN A, ARALIKATTE R. DeepTriage: Exploring the effectiveness of deep learning for bug triaging［C］// Proceedings of the 2019 ACM India Joint International Conference on Data Science and Management of Data. 2019:171-179.
［14］	席圣渠，姚远，徐锋，等. 基于循环神经网络的缺陷报告分派方法［J］. 软件学报, 2018,29（8）:2322-2335.
［15］	刘烨,黄金筱,马于涛. 基于混合神经网络和注意力机制的软件缺陷自动分派方法［J］. 计算机研究与发展, 2020,57(3):459-473.
［16］	宋化志,马于涛. DeepTriage:一种基于深度学习的软件缺陷自动分配方法［J］. 小型微型计算机系统, 2019,40(1):126-132.
［17］	李元香,董夏磊,项正龙，等. 基于图卷积神经网络的软件缺陷分派方法［J］. 武汉大学学报（理学版）, 2020,66(3):244-252.
［18］	WU W J, ZHANG W, YANG Y, et al. DREX: Developer recommendation with k-nearest-neighbor search and expertise ranking［C］// Proceedings of the 18th Asia-Pacific Software Engineering Conference. 2011:389-396.
［19］	JEONG G, KIM S, ZIMMERMANN T. Improving bug triage with bug tossing graphs［C］// Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering. 2009:111-120.
［20］	BHATTACHARYA P, NEAMTIU I. Fine-grained incremental learning and multi-feature tossing graphs to improve bug triaging［C］// Proceedings of the 2010 IEEE International Conference on Software Maintenance. 2010. DOI: 10.1109/ICSM.2010.5609736.
［21］	ZHANG W, WANG S, WANG Q. KSAP: An approach to bug report assignment using KNN search and heterogeneous proximity［J］. Information and Software Technology, 2016,70:68-84.
［22］	NAGUIB H, NARAYAN N, BRUGGE B, et al. Bug report assignee recommendation using activity profiles［C］// Proceedings of the 10th Working Conference on Mining Software Repositories. 2013:22-30.
［23］	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need［C］// Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017:6000-6010.

[1]	HU Chong-jia, LIU Jin-zhou, FANG Li. Unsupervised Domain Adaptation for Outdoor Point Cloud Semantic Segmentation [J]. Computer and Modernization, 2024, 0(01): 74-79.
[2]	LIN Wei. Incremental News Recommendation Method Based on Self-supervised Learning and Data Replay [J]. Computer and Modernization, 2023, 0(12): 1-6.
[3]	LIANG Tian-kai, HUANG Kang-hua, LIU Kai-hang, LAN Lan, ZENG Bi. Deep Federated Image Classification Method Based on Bilateral Homomorphic Encryption [J]. Computer and Modernization, 2023, 0(12): 36-40.
[4]	QIU Kai-xing, FENG Guang. A Multi-label Image Classification Model Based on Dual Feature Attention [J]. Computer and Modernization, 2023, 0(12): 41-47.
[5]	ZHANG Bo-quan, MAI Hai-peng, CHEN Jia-min, Pang Jin-ju. White Matter Hyperintensities Segmentation Based on High Gray Value#br# Attention Mechanism [J]. Computer and Modernization, 2023, 0(12): 67-75.
[6]	LI Yan-man, WANG Bi-heng, ZHAO Ling-yan. Safety Helmet Detection Based on Lightweight YOLOv5 [J]. Computer and Modernization, 2023, 0(10): 59-64.
[7]	LI Shi-da, XIANG Jian-wen. A Weakened Joint Reinforcement Method to Improve Robustness of Image Recognition Models [J]. Computer and Modernization, 2023, 0(10): 70-76.
[8]	SHEN Jia-wei, LU Yi-ming, CHEN Xiao-yi, QIAN Mei-ling, LU Wei-zhong, . Review of Research on Human Behavior Detection Methods Based on Deep Learning [J]. Computer and Modernization, 2023, 0(09): 1-9.
[9]	LIU Chan-yi, HUANG Dan, XUE Lin-yan, WANG Tao, ZHU Tao, . COVID-19 X-ray Classification Based on Improved Efficientnet Network [J]. Computer and Modernization, 2023, 0(09): 94-99.
[10]	MA Guo-xiang, YANG Ling-fei, YAN Chuan-bo, ZHANG Zhi-hao, SUN Bing, WANG Xiao-rong. Ultrasonic Image Diagnosis of Hepatic Echinococcosis Based on Deep DenseNet Network [J]. Computer and Modernization, 2023, 0(09): 100-104.
[11]	NONG Hao-cheng, REN De-jun, REN Qiu-lin, LIU Peng-li, HUANG De-cheng. Surface Anomaly Detection Algorithm of Flexible Plastic Packaging Based on Improved ConvNeXt [J]. Computer and Modernization, 2023, 0(08): 12-17.
[12]	OUYANG Fei, WU Xu, XIANG Dong-sheng. Garbage Classification and Detection Method Based on Improved YOLOX [J]. Computer and Modernization, 2023, 0(08): 68-73.
[13]	HU Rui-jie, CHE Dou. Review of Infrared Small Target Detection [J]. Computer and Modernization, 2023, 0(08): 79-86.
[14]	JIANG Lei, TANG Jian, YANG Chao-yue, LYU Ting-ting. Bearing Fault Diagnosis Based on CWGAN-GP and CNN [J]. Computer and Modernization, 2023, 0(07): 1-6.
[15]	WANG Jia-chen, ZHANG Hong-xin, LIU Qing-hua, . CT Image Generation of Pneumonia Based on Generative Adversarial Network [J]. Computer and Modernization, 2023, 0(07): 20-24.