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Large Language Models for Test-Free Fault Localization
Open accessAbstract
Fault Localization (FL) aims to automatically localize buggy lines of code, a key first step in many manual and automatic debugging tasks. Previous FL techniques assume the provision of input tests, and often require extensive program analysis, program instrumentation, or data preprocessing. Prior work on deep learning for APR struggles to learn from small datasets and produces limited results on real-world programs. Inspired by the ability of large language models (LLMs) of code to adapt to new tasks based on very few examples, we investigate the applicability of LLMs to line level fault localization. Specifically, we propose to overcome the left-to-right nature of LLMs by fine-tuning a small set of bidirectional adapter layers on top of the representations learned by LLMs to produce LLMAO, the first language model based fault localization approach that locates buggy lines of code without any test coverage information. We fine-tune LLMs with 350 million, 6 billion, and 16 billion parameters on small, manually curated corpora of buggy programs such as the Defects4J corpus. We observe that our technique achieves substantially more confidence in fault localization when built on the larger models, with bug localization performance scaling consistently with the LLM size. Our empirical evaluation shows that LLMAO improves the Top-1 results over the state-of-the-art machine learning fault localization (MLFL) baselines by 2.3%--54.4%, and Top-5 results by 14.4%-35.6%. LLMAO is also the first FL technique trained using a language model architecture that can detect security vulnerabilities down to the code line level.
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“Meanwhile, most such LLM-based techniques either cannot perform repository-level FL due to the limited context window of LLMs [98], or rely on complicated/costly agentic design to navigate through the codebase [48, 79].”
From Agentless: Demystifying LLM-based Software Engineering Agents (2024) · cites this paper as [98]
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“We use SWE-bench because it unites many separate SE tasks, such as automated program repair [10, 40, 55], bug localization [4, 58], and testing [22, 46, 56] under a single task formulation that faithfully mirrors practical SE.”
From SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering (2024) · cites this paper as [58]
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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582
Scaling Laws for Neural Language Models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020) · arXiv · doi:10.48550/arxiv.2001.08361 · Cited by 9,550 · cited 1×
“A key property of LLMs is that their performance improves consistently with the scale of their computational budget [16], which is itself a function of the model and training data size.”
BackgroundLearning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., & Frasconi, P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279181 · Cited by 8,542
A Neural Probabilistic Language Model
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C., Jaz, K., Hofmann, T., Poggio, T., & Shawe-Taylor, J. S. (2000) · Cited by 3,451
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.12712 · Cited by 1,526
Defects4J: a database of existing faults to enable controlled testing studies for Java programs
Just, R., Jalali, D., & Ernst, M. D. (2014) · Proceedings of the 2014 International Symposium on Software Testing and Analysis · doi:10.1145/2610384.2628055 · Cited by 1,447
Empirical evaluation of the tarantula automatic fault-localization technique
Jones, J. A., & Harrold, M. J. (2005) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/1101908.1101949 · Cited by 1,274
Deep Learning Based Vulnerability Detection: Are We There Yet?
Chakraborty, S., Krishna, R., Ding, Y., & Ray, B. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3087402 · Cited by 970
Research works printed but not linked: 5
- L. Tunstall, L. Von Werra, and T. Wolf, Natural language processing with transformers. O'Reilly Media, Inc., 2022.
- B. Ray, V. Hellendoorn, S. Godhane, Z. Tu, A. Bacchelli, and P. Devanbu, "On the" naturalness" of buggy code," in Proceedings of the 38th International Conference on Software Engineering, pp. 428--439, 2016.
- L. Gao, S. Biderman, S. Black, L. Golding, T. Hoppe, C. Foster, J. Phang, H. He, A. Thite, N. Nabeshima, et al., "The pile: An 800gb dataset of diverse text for language modeling," arXiv preprint arXiv:2101.00027, 2020. · arXiv
- P. S. Kochhar, X. Xia, D. Lo, and S. Li, "Practitioners' expectations on automated fault localization," in Proceedings of the 25th International Symposium on Software Testing and Analysis, pp. 165--176, 2016.
- H. Pearce, B. Ahmad, B. Tan, B. Dolan-Gavitt, and R. Karri, "An empirical cyber-security evaluation of github copilot's code contributions," ArXiv abs/2108.09293, 2021. · arXiv
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Liu, C., Lin, Y., Huang, Y., Chang, J., Qi, B., Jiang, B., Huang, Z., & Dong, J. S. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00117 · Cited by 2
Large Language Models for Fault Diagnosis
Qi, Z., Ren, J., Gan, W., & Yu, P. S. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402351 · Cited by 2
DCoL-A: Agentic dual chain of thinking helps LLMs pretend logic solvers
Chen, M., Wu, L.-I., Liu, R., Chang, X., Xue, J., & Li, G. (2026) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2026.103780 · Cited by 2
COSMosFL: Ensemble of Small Language Models for Fault Localisation
Cho, H., Kang, S., An, G., & Yoo, S. (2025) · International Workshop on Large Language Models for Code (LLM4Code) · doi:10.1109/llm4code66737.2025.00007 · Cited by 2
Analysis of the Effectiveness of Large Language Model Feature in Source Code Defect Detection
He, T., Yang, M., Hu, W. P., & Chen, Y. (2024) · International Conference on Artificial Intelligence and Computer Information Technology (AICIT) · doi:10.1109/aicit62434.2024.10730232 · Cited by 1
Can Language Models Pretend Solvers? Logic Code Simulation with LLMs
Chen, M., Li, G., Wu, L.-I., Liu, R., Su, Y., Chang, X., & Xue, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0602-3_6 · Cited by 6
PAFL: Enhancing Fault Localizers by Leveraging Project-Specific Fault Patterns
Kim, D., Jeon, M., Hwang, D., & Oh, H. (2025) · Proceedings of the ACM on Programming Languages · doi:10.1145/3720526 · Cited by 1
SecureMind: A Framework for Benchmarking Large Language Models in Memory Bug Detection and Repair
Wang, H., Jacob, D., Kelly, D., Elkhatib, Y., Singer, J., & Wang, Z. (2025) · ACM SIGPLAN International Symposium on Memory Management (ISMM) · doi:10.1145/3735950.3735954 · Cited by 1
Simple Fault Localization Using Execution Traces
Prenner, J. A., & Robbes, R. (2025) · IEEE/ACM International Workshop on Automated Program Repair (APR) · doi:10.1109/apr66717.2025.00013 · Cited by 1
Artificial Intelligence Techniques-Driven Automated Localization of Software Faults
Pranav Raj, S., & Venkatraman, K. (2025) · International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iciss63372.2025.11076249 · Cited by 1
Hou, Z., Duan, Z., Yang, M., Wu, M., & Yang, S. (2025) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs65678.2025.00015 · Cited by 1
Empirical Evaluation of LLMs for Automated Program Fault Localisation
Liu, Y., Wang, X., Liu, H., Huang, R., & Wu, Y. (2025) · IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) · doi:10.1109/qrs-c65679.2025.00063 · Cited by 1
Reinforcement Learning vs Supervised Learning: A tug of war to generate refactored code accurately
Palit, I., & Sharma, T. (2025) · International Conference on Evaluation and Assessment in Software Engineering (EASE) · doi:10.1145/3756681.3756971 · Cited by 8
Bug Localization Based on Context-Aware Code Translation and Feature Fusion
Cheng, W., Zhang, R., Wang, G., & Chen, Y. (2026) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s0218194025501153 · Cited by 1
LOSVER: Line-Level Modifiability Signal-Guided Vulnerability Detection and Classification
Nam, D., & Baik, J. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00092 · Cited by 1
Chowdhury, K., Banik, D., & Shamim, S. I. (2026) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3803437.3808241 · Cited by 1
Le, T.-H.-C., Hua, H.-Q.-B., & Le, X.-B. (2026) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3832783.3834420 · Cited by 1
Towards Connecting Bugs and Architecture in Software Systems: A Perspective
Rao, A. E., & Chimalakonda, S. (2024) · IEEE International Conference on Software Architecture Companion (ICSA-C) · doi:10.1109/icsa-c63560.2024.00025 · Cited by 1
Improve Graph Learning Fault Localization Via Multimodal Code RepresentationPreprint
Yu, B., & Guo, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4840043 · Cited by 1
Rafi, M. N., Kim, D. J., Chen, T.-H., & Wang, S. (2024) · arXiv · doi:10.48550/arxiv.2412.18750
“Recent research has explored the use of LLMs in various software engineering tasks, including Fault Localization (FL) [14, 41] and Automatic Program Repair (APR) [39, 45], which show great potential for automatically resolving real-world issues in large codebases.”
It’s Organic: Software Testing of Emerging Domains (Keynote)
Cohen, M. B. (2024) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3663529.3674720
SpecNLP: A Pre-trained Model Enhanced with Spectrum Profile for Bug Localization
Farzandway, M., & Ghassemi, F. (2024) · IEEE International Conference on Artificial Intelligence Testing (AITest) · doi:10.1109/aitest62860.2024.00018
Automatically Learning a Precise Measurement for Fault Diagnosis Capability of Test Cases
Zhao, Y., Sun, Z., Wang, G., Liang, Q., Zhang, Y., Lou, Y., Hao, D., & Zhang, L. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3712189
Statistics in Medicine: Tools for Health Research
Salem, S. M. A., Thajil, A. S. Q., lafta, N. S. T., Sahin, A. K. A., & alkarim, M. S. A. A. (2025) · Bright Sky Publications eBooks · doi:10.62906/bs.book.351
Metadata
- Authors
- Yang, A. Z. H., Le Goues, C., Martins, R., & Hellendoorn, V.
- Affiliations
- Carnegie Mellon University (US)
- Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
- Venue
- Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
- Publisher
- ACM
- Published
- 2024-02-06
- Pages
- 1-12
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W4391579639
- Cited by
- 144
- References
- 48
- Citations by year
- 2026: 53 · 2025: 64 · 2024: 27
- FWCI
- 39.31
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- NSF (National Science Foundation) (CCF-1750116)
- National Science Foundation (CCF-1762363)
- Topics
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Keywords
- large language models, LLM fine-tuning, Defects4J, vulnerability detection, automated program repair
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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