Neural Approaches for Natural Language Query Answering over Source Code
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During software development, developers need to ensure that the developed code is bug-free and the best coding practices are followed during the code development process. To guarantee this, the developers require answers to queries about specific aspects of the code relevant to the development. Powerful code-query languages such as CodeQL have been developed for this purpose. Use of such code-query languages, however, requires expertise in writing formal queries. For each separate query, one needs to write several lines in a code-query language. To remedy these problems, we propose to represent each query by a natural language phrase and answer such queries using neural networks. We aim to perform model training such that a single model can answer multiple queries as opposed to writing separate formal queries for each task. Such a model can answer these queries against unseen code. With this motivation, we introduce the novel NlCodeQA dataset. It includes 171,346 labeled examples where each input consists of a natural language query and a code snippet. The labels are answer spans in the input code snippet with respect to the input query. State-of-the-art BERT-style neural architectures were trained using the NlCodeQA data. Preliminary experimental results show that the proposed model achieves the exact match accuracy of 86.30%. The proposed use of natural language query and neural models for query understanding will help increase the productivity of software developers and pave the way for designing machine learning based code analysis tools that can complement the existing code analysis systems for complex code queries that are either hard or expensive to represent using a formal query language.