ASE 2023
Mon 11 - Fri 15 September 2023 Kirchberg, Luxembourg
Wed 13 Sep 2023 15:30 - 15:45 at Room FR - Industry Challenge (Competition) Chair(s): Sun Jianwen

Just-In-Time defect prediction models can identify defect-inducing commits at check-in time and many approaches are proposed with remarkable performance. However, these approaches still have a few limitations which affect their effectiveness and practical usage: (1) partially using semantic information or structure information of code, (2) coarsely providing results to a commit (buggy or clean), and (3) independently investigating the defect prediction model and defect repair model.

In this study, to handle the aforementioned limitations, we propose a unified defect prediction and repair framework named COMPDEFECT, which can identify whether a changed function inside a commit is defect-prone, categorize the type of defect, and repair such a defect automatically if it falls into several scenarios, e.g., defects with single statement fixes, or those that match a small set of defect templates. Technically, the first two tasks in COMPDEFECT are treated as a multiclass classification task, while the last task is treated as a sequence generation task.

To verify the effectiveness of COMPDEFECT, we first build a large-scale function-level dataset (i.e., 21,047) named Function- SStuBs4J and then compare COMPDEFECT with tens of state-of-the-art (SOTA) approaches by considering five performance measures. The experimental results indicate that COMPDEFECT outperforms all SOTAs with a substantial improvement in three tasks separately. Moreover, the pipeline experimental results also indicate the feasibility of COMPDEFECT to unify three tasks in a model.

CompDefect: Unifying Defect Prediction, Categorization, and Repair by Multi-task Deep Learning (ase-in-presentation.pptx)2.19MiB

Wed 13 Sep

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

14:45 - 15:45
Industry Challenge (Competition)Industry Challenge (Competition) at Room FR
Chair(s): Sun Jianwen
MalWuKong: Towards Fast, Accurate, and Multilingual Detection of Malicious Code Poisoning in OSS Supply Chains
Industry Challenge (Competition)
Ningke Li Huazhong University of Science and Technology, Shenao Wang Huazhong University of Science and Technology, Mingxi Feng Huazhong University of Science and Technology, Kailong Wang Huazhong University of Science and Technology, Meizhen Wang Huazhong University of Science and Technology, Haoyu Wang Huazhong University of Science and Technology
Minecraft: Automated Mining of Software Bug Fixes with Precise Code Context
Industry Challenge (Competition)
Sai Krishna Avula IIT Gandhinagar, Venkatesh Vobbilisetti NIT Raipur, Shouvick Mondal IIT Gandhinagar, India
Pre-print Media Attached File Attached
CiD4HMOS: A Solution to HarmonyOS Compatibility IssuesRecorded talk
Industry Challenge (Competition)
Tianzhi Ma , Yanjie Zhao Monash Univerisity, Li Li Beihang University, Liang Liu Nanjing University of Aeronautics and Astronautics
Media Attached
Industry talk
Unifying Defect Prediction, Categorization, and Repair by Multi-task Deep LearningRecorded talk
Industry Challenge (Competition)
Chao Ni Zhejiang University, Kaiwen Yang Zhejiang University, Yan Zhu Zhejiang University, Xiang Chen Nantong University, Xiaohu Yang Zhejiang University
Media Attached File Attached