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Found 6 papers, 4 papers with code
How Effective Are Neural Networks for Fixing Security Vulnerabilities
The results call for innovations to enhance automated Java vulnerability repair such as creating larger vulnerability repair training data, tuning LLMs with such data, and applying code simplification transformation to facilitate vulnerability repair.
KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair
KNOD has two major novelties, including (1) a novel three-stage tree decoder, which directly generates Abstract Syntax Trees of patched code according to the inherent tree structure, and (2) a novel domain-rule distillation, which leverages syntactic and semantic rules and teacher-student distributions to explicitly inject the domain knowledge into the decoding procedure during both the training and inference phases.
Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training
Our study of 22 mitigation techniques and five baselines reveals up to 12. 6% fairness variance across identical training runs with identical seeds.
CURE: Code-Aware Neural Machine Translation for Automatic Program Repair
Finally, CURE uses a subword tokenization technique to generate a smaller search space that contains more correct fixes.
CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair
To address these challenges, we propose a new G&V technique—CoCoNuT, which uses ensemble learning on the combination of convolutional neural networks (CNNs) and a new context-aware neural machine translation (NMT) architecture to automatically fix bugs in multiple programming languages.
ENCORE: Ensemble Learning using Convolution Neural Machine Translation for Automatic Program Repair
We propose ENCORE, a new G&V technique, which uses ensemble learning on convolutional neural machine translation (NMT) models to automatically fix bugs in multiple programming languages.
