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ACM Softw. Eng."],"published-print":{"date-parts":[[2024,7,12]]},"abstract":"\n Just-in-time defect prediction (JIT-DP) is used to predict the defect-proneness of a commit and just-in-time defect localization (JIT-DL) is used to locate the exact buggy positions (defective lines) in a commit. Recently, various JIT-DP and JIT-DL techniques have been proposed, while most of them use a post-mortem way (e.g., code entropy, attention weight, LIME) to achieve the JIT-DL goal based on the prediction results in JIT-DP. These methods do not utilize the label information of the defective code lines during model building. In this paper, we propose a unified model JIT-Smart, which makes the training process of just-in-time defect prediction and localization tasks a mutually reinforcing multi-task learning process. Specifically, we design a novel defect localization network (DLN), which explicitly introduces the label information of defective code lines for supervised learning in JIT-DL with considering the class imbalance issue. To further investigate the accuracy and cost-effectiveness of JIT-Smart, we compare JIT-Smart with 7 state-of-the-art baselines under 5 commit-level and 5 line-level evaluation metrics in JIT-DP and JIT-DL. The results demonstrate that JIT-Smart is statistically better than all the state-of-the-art baselines in JIT-DP and JIT-DL. In JIT-DP, at the median value, JIT-Smart achieves F1-Score of\n \n \n \n 0.475<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , AUC of\n \n \n \n 0.886<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , Recall@20%Effort of\n \n \n \n 0.823<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , Effort@20%Recall of\n \n \n \n 0.01<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n and Popt of\n \n \n \n 0.942<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n and improves the baselines by\n \n \n \n 19<\/mml:mn>\n .89%<\/mml:mn>\n -<\/mml:mi>\n 702<\/mml:mn>\n .74%<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ,\n \n \n \n 1<\/mml:mn>\n .23%<\/mml:mn>\n -<\/mml:mi>\n 31<\/mml:mn>\n .34%<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ,\n \n \n \n 9<\/mml:mn>\n .44%<\/mml:mn>\n -<\/mml:mi>\n 33<\/mml:mn>\n .16%<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ,\n \n \n \n 21<\/mml:mn>\n .6%<\/mml:mn>\n -<\/mml:mi>\n 53<\/mml:mn>\n .82%<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n and\n \n \n \n 1<\/mml:mn>\n .94%<\/mml:mn>\n -<\/mml:mi>\n 34<\/mml:mn>\n .89%<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , respectively. In JIT-DL, at the median value, JIT-Smart achieves Top-5 Accuracy of\n \n \n \n 0.539<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n and Top-10 Accuracy of\n \n \n \n 0.396<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , Recall@\n \n \n \n 20<\/mml:mn>\n %<\/mml:mi>\n \n \n Effort<\/mml:mtext>\n <\/mml:mrow>\n \n l<\/mml:mi>\n i<\/mml:mi>\n n<\/mml:mi>\n e<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n of\n \n \n \n 0.726<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , Effort@\n \n \n \n 20<\/mml:mn>\n %<\/mml:mi>\n \n \n Recall<\/mml:mtext>\n <\/mml:mrow>\n \n l<\/mml:mi>\n i<\/mml:mi>\n n<\/mml:mi>\n e<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n of\n \n \n \n 0.087<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n and\n \n \n \n \n \n IFA<\/mml:mtext>\n <\/mml:mrow>\n \n l<\/mml:mi>\n i<\/mml:mi>\n n<\/mml:mi>\n e<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n of\n \n \n \n 0.098<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n and improves the baselines by\n \n \n \n 101<\/mml:mn>\n .83%<\/mml:mn>\n -<\/mml:mi>\n 178<\/mml:mn>\n .35%<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ,\n \n \n \n 101<\/mml:mn>\n .01%<\/mml:mn>\n -<\/mml:mi>\n 277<\/mml:mn>\n .31%<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ,\n \n \n \n 257<\/mml:mtext>\n .88%<\/mml:mn>\n -<\/mml:mi>\n 404<\/mml:mn>\n .63%<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ,\n \n \n \n 71<\/mml:mn>\n .91%<\/mml:mn>\n -<\/mml:mi>\n 74<\/mml:mn>\n .31%<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n and\n \n \n \n 99<\/mml:mn>\n .11%<\/mml:mn>\n -<\/mml:mi>\n 99<\/mml:mn>\n .41%<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , respectively. Statistical analysis shows that our JIT-Smart performs more stably than the bestperforming model. Besides, JIT-Smart also achieves the best performance compared with the state-of-the-art baselines in cross-project evaluation.\n <\/jats:p>","DOI":"10.1145\/3643727","type":"journal-article","created":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T10:22:09Z","timestamp":1720779729000},"page":"1-23","source":"Crossref","is-referenced-by-count":11,"title":["JIT-Smart: A Multi-task Learning Framework for Just-in-Time Defect Prediction and Localization"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8234-3186","authenticated-orcid":false,"given":"Xiangping","family":"Chen","sequence":"first","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6258-5233","authenticated-orcid":false,"given":"Furen","family":"Xu","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Zhuhai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9548-0208","authenticated-orcid":false,"given":"Yuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Zhuhai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8662-5690","authenticated-orcid":false,"given":"Neng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Zhuhai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7878-4330","authenticated-orcid":false,"given":"Zibin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2024,7,12]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2009.06.055"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00076"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.5555\/1622407.1622416"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2017.08.004"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1423"},{"key":"e_1_3_1_8_2","article-title":"Codebert: A pre-trained model for programming and natural languages","author":"Feng Zhangyin","year":"2020","unstructured":"Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al. 2020. 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