CodeBLEU improves correlation with human programmer scores on code synthesis tasks by adding syntactic AST matching and semantic data-flow matching to the standard BLEU n-gram approach.
Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing
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abstract
In this paper, we present an approach to incorporate retrieved datapoints as supporting evidence for context-dependent semantic parsing, such as generating source code conditioned on the class environment. Our approach naturally combines a retrieval model and a meta-learner, where the former learns to find similar datapoints from the training data, and the latter considers retrieved datapoints as a pseudo task for fast adaptation. Specifically, our retriever is a context-aware encoder-decoder model with a latent variable which takes context environment into consideration, and our meta-learner learns to utilize retrieved datapoints in a model-agnostic meta-learning paradigm for fast adaptation. We conduct experiments on CONCODE and CSQA datasets, where the context refers to class environment in JAVA codes and conversational history, respectively. We use sequence-to-action model as the base semantic parser, which performs the state-of-the-art accuracy on both datasets. Results show that both the context-aware retriever and the meta-learning strategy improve accuracy, and our approach performs better than retrieve-and-edit baselines.
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cs.SE 3representative citing papers
GraphCodeBERT uses data flow graphs in pre-training to capture semantic code structure and reaches state-of-the-art results on code search, clone detection, translation, and refinement.
CodeXGLUE supplies a standardized collection of 10 code-related tasks, 14 datasets, an evaluation platform, and BERT-, GPT-, and encoder-decoder-style baselines.
citing papers explorer
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CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
CodeBLEU improves correlation with human programmer scores on code synthesis tasks by adding syntactic AST matching and semantic data-flow matching to the standard BLEU n-gram approach.
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GraphCodeBERT: Pre-training Code Representations with Data Flow
GraphCodeBERT uses data flow graphs in pre-training to capture semantic code structure and reaches state-of-the-art results on code search, clone detection, translation, and refinement.
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CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
CodeXGLUE supplies a standardized collection of 10 code-related tasks, 14 datasets, an evaluation platform, and BERT-, GPT-, and encoder-decoder-style baselines.