{"title":"通过深度半监督学习恢复问题与提交之间的可追溯性联系","authors":"Jianfei Zhu , Guanping Xiao , Zheng Zheng , Yulei Sui","doi":"10.1016/j.jss.2024.112109","DOIUrl":null,"url":null,"abstract":"<div><p>Traceability links between issues and commits record valuable information about the evolutionary history of software projects. Unfortunately, these links are often missing. While deep learning stands as the current state-of-the-art (SOTA) in automated traceability links recovery (TLR), its effectiveness is faced with the practical problem of limited labeled data during training. To overcome this challenge, in this paper, we propose <span>DSSLink</span>, a novel method based on deep semi-supervised learning, enhancing deep-learning-based link recovery tasks. <span>DSSLink</span> first learns knowledge from labeled data through pre-trained model and then leverages deep semi-supervised learning to infer pseudo-labels on unlabeled data. The extended dataset of pseudo-labeled and labeled data re-trains the deep learning model in an iterative process. Our extensive evaluations are conducted on two SOTA traceability methods (T-BERT and BTLink) across four GitHub projects and 11 Apache projects. Specifically, the maximum F1-score improvements for GitHub and Apache projects reached 22.9% and 43.5%, respectively. Evaluation results show that <span>DSSLink</span> is effective in enhancing TLR performance and outperforms TraceFUN, a recent approach that utilizes unlabeled data for TLR. The source code of <span>DSSLink</span> is available at <span>https://github.com/DSSLink</span><svg><path></path></svg>.</p><p><em>Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board</em>.</p></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep semi-supervised learning for recovering traceability links between issues and commits\",\"authors\":\"Jianfei Zhu , Guanping Xiao , Zheng Zheng , Yulei Sui\",\"doi\":\"10.1016/j.jss.2024.112109\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Traceability links between issues and commits record valuable information about the evolutionary history of software projects. Unfortunately, these links are often missing. While deep learning stands as the current state-of-the-art (SOTA) in automated traceability links recovery (TLR), its effectiveness is faced with the practical problem of limited labeled data during training. To overcome this challenge, in this paper, we propose <span>DSSLink</span>, a novel method based on deep semi-supervised learning, enhancing deep-learning-based link recovery tasks. <span>DSSLink</span> first learns knowledge from labeled data through pre-trained model and then leverages deep semi-supervised learning to infer pseudo-labels on unlabeled data. The extended dataset of pseudo-labeled and labeled data re-trains the deep learning model in an iterative process. Our extensive evaluations are conducted on two SOTA traceability methods (T-BERT and BTLink) across four GitHub projects and 11 Apache projects. Specifically, the maximum F1-score improvements for GitHub and Apache projects reached 22.9% and 43.5%, respectively. Evaluation results show that <span>DSSLink</span> is effective in enhancing TLR performance and outperforms TraceFUN, a recent approach that utilizes unlabeled data for TLR. The source code of <span>DSSLink</span> is available at <span>https://github.com/DSSLink</span><svg><path></path></svg>.</p><p><em>Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board</em>.</p></div>\",\"PeriodicalId\":51099,\"journal\":{\"name\":\"Journal of Systems and Software\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-05-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Systems and Software\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0164121224001547\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Systems and Software","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0164121224001547","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
Deep semi-supervised learning for recovering traceability links between issues and commits
Traceability links between issues and commits record valuable information about the evolutionary history of software projects. Unfortunately, these links are often missing. While deep learning stands as the current state-of-the-art (SOTA) in automated traceability links recovery (TLR), its effectiveness is faced with the practical problem of limited labeled data during training. To overcome this challenge, in this paper, we propose DSSLink, a novel method based on deep semi-supervised learning, enhancing deep-learning-based link recovery tasks. DSSLink first learns knowledge from labeled data through pre-trained model and then leverages deep semi-supervised learning to infer pseudo-labels on unlabeled data. The extended dataset of pseudo-labeled and labeled data re-trains the deep learning model in an iterative process. Our extensive evaluations are conducted on two SOTA traceability methods (T-BERT and BTLink) across four GitHub projects and 11 Apache projects. Specifically, the maximum F1-score improvements for GitHub and Apache projects reached 22.9% and 43.5%, respectively. Evaluation results show that DSSLink is effective in enhancing TLR performance and outperforms TraceFUN, a recent approach that utilizes unlabeled data for TLR. The source code of DSSLink is available at https://github.com/DSSLink.
Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.
期刊介绍:
The Journal of Systems and Software publishes papers covering all aspects of software engineering and related hardware-software-systems issues. All articles should include a validation of the idea presented, e.g. through case studies, experiments, or systematic comparisons with other approaches already in practice. Topics of interest include, but are not limited to:
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The journal welcomes state-of-the-art surveys and reports of practical experience for all of these topics.