FFL: Fine-grained Fault Localization for Student Programs via Syntactic and Semantic Reasoning

Author:

Nguyen Thanh-Dat1,Le-Cong Thanh2,Luong Duc-Minh2,Duong Van-Hai3,Le Xuan-Bach D.1,Lo David4,Huynh Quyet-Thang2

Affiliation:

1. The University of Melbourne

2. Hanoi University of Science and Technology

3. Independent Researcher

4. Singapore Management University

Publisher

IEEE

Reference43 articles.

1. Software Fault Localization Using DStar (D*)

2. The DStar Method for Effective Software Fault Localization

3. Codeflaws: a programming competition benchmark for evaluating automated program repair tools;tan;2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C),2017

4. Axiomatic attribution for deep networks;sundararajan;International Conference on Machine Learning,2017

5. FLUCCS: using code and change metrics to improve fault localization

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1. Towards Better Graph Neural Network-Based Fault Localization through Enhanced Code Representation;Proceedings of the ACM on Software Engineering;2024-07-12

2. Fault Localization for Novice Programs Combining Static Analysis and Dynamic Detection;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02

3. Flexible control flow graph alignment for delivering data-driven feedback to novice programming learners;Journal of Systems and Software;2024-04

4. Graph-Based Fault Localization in Python Projects with Class-Imbalanced Learning;Communications in Computer and Information Science;2024

5. VsusFL: Variable-suspiciousness-based Fault Localization for novice programs;Journal of Systems and Software;2023-11

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