Leveraging Context Information for Self-Admitted Technical Debt Detection
This program is tentative and subject to change.
Self-Admitted Technical Debt (SATD) refers to nonoptimal software design or implementation acknowledged and explicitly documented in the code by developers. Detecting SATD and understanding its evolution can help developers better manage their development activities and monitor the software quality. In recent years, numerous approaches have been proposed to automatically identify SATD. However, these approaches still suffer from a high number of false positives (i.e., non-SATD comments are detected as SATD).
To further advance this field, in this paper, we conduct an empirical study to evaluate the performance of the state-of-the-art SATD detection tools and investigate the causes behind the false positives. By manually analyzing 137 false positive cases, we identify the main types of comments that are easily misclassified. To address this issue, we propose a new approach, CASTI, integrating context information into CodeBERT, a pre-trained model for programming languages. Our evaluation demonstrates that CASTI can significantly reduce the false positives and the context information does help improve the performance.
This program is tentative and subject to change.
Sun 27 AprDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 12:30 | Vulnerabilities, Technical Debt, DefectsEarly Research Achievements (ERA) / Research Track / Replications and Negative Results (RENE) at 205 | ||
11:00 10mTalk | CalmDroid: Core-Set Based Active Learning for Multi-Label Android Malware Detection Research Track Minhong Dong Tiangong University, Liyuan Liu Tiangong University, Mengting Zhang Tiangong University, Sen Chen Tianjin University, Wenying He Hebei University of Technology, Ze Wang Tiangong University, Yude Bai Tianjin University | ||
11:10 10mTalk | Towards Task-Harmonious Vulnerability Assessment based on LLM Research Track Zaixing Zhang Southeast University, Chang Jianming , Tianyuan Hu Southeast University, Lulu Wang Southeast University, Bixin Li Southeast University | ||
11:20 10mTalk | Slicing-Based Approach for Detecting and Patching Vulnerable Code Clones Research Track Hakam W. Alomari Miami University, Christopher Vendome Miami University, Himal Gyawali Miami University | ||
11:30 6mTalk | Revisiting Security Practices for GitHub Actions Workflows Early Research Achievements (ERA) | ||
11:36 6mTalk | Leveraging multi-task learning to improve the detection of SATD and vulnerability Replications and Negative Results (RENE) Barbara Russo Free University of Bolzano, Jorge Melegati Free University of Bozen-Bolzano, Moritz Mock Free University of Bozen-Bolzano Pre-print | ||
11:42 10mTalk | Leveraging Context Information for Self-Admitted Technical Debt Detection Research Track Miki Yonekura Nara Institute of Science and Technology, Yutaro Kashiwa Nara Institute of Science and Technology, Bin Lin Hangzhou Dianzi University, Kenji Fujiwara Nara Women’s University, Hajimu Iida Nara Institute of Science and Technology | ||
11:52 6mTalk | Personalized Code Readability Assessment: Are We There Yet? Replications and Negative Results (RENE) Antonio Vitale Politecnico di Torino, University of Molise, Emanuela Guglielmi University of Molise, Rocco Oliveto University of Molise, Simone Scalabrino University of Molise | ||
11:58 6mTalk | Automated Refactoring of Non-Idiomatic Python Code: A Differentiated Replication with LLMs Replications and Negative Results (RENE) Pre-print | ||
12:04 10mResearch paper | Sonar: Detecting Logic Bugs in DBMS through Generating Semantic-aware Non-Optimizing Query Research Track Shiyang Ye Zhejiang University, Chao Ni Zhejiang University, Jue Wang Nanjing University, Qianqian Pang zhejang university, Xinrui Li School of Software Technology, Zhejiang University, xiaodanxu College of Computer Science and Technology, Zhejiang university | ||
12:14 6mTalk | A Study on Applying Large Language Models to Issue Classification Replications and Negative Results (RENE) | ||
12:20 10mLive Q&A | Session's Discussion: "Vulnerabilities, Technical Debt, Defects" Research Track | ||