A Survey of Learning-based Automated Program Repair
importance 5/10
ai assisted
Software Testing and Debugging Techniques software engineering stage: research
Problem
Fixing software bugs automatically to reduce development and maintenance burden.
Why it matters
Manual bug fixing is costly and central to software maintenance.
What the AI did
The survey describes learning-based APR techniques that use neural networks to translate buggy code snippets into fixed code, learning bug-fixing patterns from open-source repositories.
Results
not stated in source
Impact
The survey states learning-based APR techniques have achieved remarkable performance, without concrete figures.
Record
| Venue | ACM Transactions on Software Engineering and Methodology |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Quanjun Zhang, Chunrong Fang, Yuxiang Ma, Weisong Sun, Zhenyu Chen |
| Citations | 132 (OpenAlex, at import time) |
| References | 265 |
| Model or method | Neural machine translation (NMT) models |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1145/3631974 |
| Open access | bronze |
| Keywords | automated program repair, deep learning, neural machine translation, bug fixing, survey |
Importance rating
5
real but narrower advance
Automated bug fixing addresses costly software maintenance, but the abstract is a survey claiming 'remarkable performance' with no quantified scale or results.
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