Open AccessPosted Content
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search.
TL;DR: The methodology used to obtain the corpus and expert labels, as well as a number of simple baseline solutions for the task are described.
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Abstract: Semantic code search is the task of retrieving relevant code given a natural language query. While related to other information retrieval tasks, it requires bridging the gap between the language used in code (often abbreviated and highly technical) and natural language more suitable to describe vague concepts and ideas.
To enable evaluation of progress on code search, we are releasing the CodeSearchNet Corpus and are presenting the CodeSearchNet Challenge, which consists of 99 natural language queries with about 4k expert relevance annotations of likely results from CodeSearchNet Corpus. The corpus contains about 6 million functions from open-source code spanning six programming languages (Go, Java, JavaScript, PHP, Python, and Ruby). The CodeSearchNet Corpus also contains automatically generated query-like natural language for 2 million functions, obtained from mechanically scraping and preprocessing associated function documentation. In this article, we describe the methodology used to obtain the corpus and expert labels, as well as a number of simple baseline solutions for the task.
We hope that CodeSearchNet Challenge encourages researchers and practitioners to study this interesting task further and will host a competition and leaderboard to track the progress on the challenge. We are also keen on extending CodeSearchNet Challenge to more queries and programming languages in the future.
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Citations
GrammarT5: Grammar-Integrated Pretrained Encoder-Decoder Neural Model for Code
Qihao Zhu,Qing-Lin Liang,Zeyu Sun,Yingfei Xiong,Lu Zhang,Shengyu Cheng +5 more
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TL;DR: GrammarT5 is a grammar-integrated encoder-decoder pretrained neural model for code that utilizes a novel grammar-integrated representation, TGRS, to capture code structure and syntax. GrammarT5 achieves SOTA performance on various code-related tasks and outperforms existing models.
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Exploring Data Augmentation for Code Generation Tasks
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TL;DR: A novel view on the learning of concepts admitting completeitary descriptions is introduced and a set of benchmarking tasks aimed at conceptual understanding by machine learning models are laid down.
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