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The performance of neural code search is significantly influenced by thequality of the training data from which the neural models are derived. A largecorpus of high-quality query and code pairs is demanded to establish a precisemapping from the natural language to the programming language. Due to thelimited availability, most widely-used code search datasets are establishedwith compromise, such as using code comments as a replacement of queries. Ourempirical study on a famous code search dataset reveals that over one-third ofits queries contain noises that make them deviate from natural user queries.Models trained through noisy data are faced with severe performance degradationwhen applied in real-world scenarios. To improve the dataset quality and makethe queries of its samples semantically identical to real user queries iscritical for the practical usability of neural code search. In this paper, wepropose a data cleaning framework consisting of two subsequent filters: arule-based syntactic filter and a model-based semantic filter. This is thefirst framework that applies semantic query cleaning to code search datasets.Experimentally, we evaluated the effectiveness of our framework on twowidely-used code search models and three manually-annotated code retrievalbenchmarks. Training the popular DeepCS model with the filtered dataset fromour framework improves its performance by 19.2% MRR and 21.3% Answer@1, onaverage with the three validation benchmarks.

On the Importance of Building High-quality Training Datasets for Neural Code Search