Program Repair
35 papers with code • 3 benchmarks • 8 datasets
Task of teaching ML models to modify an existing program to fix a bug in a given code.
Benchmarks
These leaderboards are used to track progress in Program Repair
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Datasets
DeepFix
xCodeEval
TFix's Code Patches Data
Subtasks
Latest papers with no code
Teaching Large Language Models to Self-Debug
In particular, we demonstrate that Self-Debugging can teach the large language model to perform rubber duck debugging; i. e., without any human feedback on the code correctness or error messages, the model is able to identify its mistakes by investigating the execution results and explaining the generated code in natural language.
RunBugRun -- An Executable Dataset for Automated Program Repair
With this dataset we follow several goals: we want to lift Neural Program Repair beyond fully static code representations, foster the use of execution-based features and, by including several different languages, counterbalance the predominance of Java in the current landscape of APR datasets and benchmarks.
Keep the Conversation Going: Fixing 162 out of 337 bugs for $0.42 each using ChatGPT
For earlier patches that failed to pass all tests, we combine the incorrect patches with their corresponding relevant test failure information to construct a new prompt for the LLM to generate the next patch.
Revisiting the Plastic Surgery Hypothesis via Large Language Models
Traditional APR tools have largely leveraged the plastic surgery hypothesis by designing manual or heuristic-based approaches to exploit such existing code ingredients.
Conversational Automated Program Repair
As such, we leverage the long-term context window of LLMs to not only avoid generating previously incorrect patches but also incorporate validation feedback to help the model understand the semantic meaning of the program under test.
Improving Automated Program Repair with Domain Adaptation
The results show that our proposed framework can improve the effectiveness of TFix by 13. 05% and CodeXGLUE by 23. 4%.
Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5
Specifically, we propose three objectives to adapt the generic CodeT5 for debugging: a bug detection objective to determine whether a given code snippet is buggy or not, a bug localization objective to identify the buggy lines, and a program repair objective to translate the buggy code to its fixed version.
Repairing Bugs in Python Assignments Using Large Language Models
We propose to use a large language model trained on code, such as Codex, to build an APR system -- MMAPR -- for introductory Python programming assignments.
Repair Is Nearly Generation: Multilingual Program Repair with LLMs
We show that RING can outperform language-specific repair engines for three of these languages.
BigIssue: A Realistic Bug Localization Benchmark
As machine learning tools progress, the inevitable question arises: How can machine learning help us write better code?
