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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
Towards Code Watermarking with Dual-Channel Transformations
Borui Yang,Liyao Xiang,Bo Li +2 more
TL;DR: SrcMarker is introduced, a watermarking system to unobtrusively encode ID bitstrings into source code, without affecting the usage and semantics of the code.
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PyTorrent: A Python Library Corpus for Large-scale Language Models.
Mehdi Bahrami,N. C. Shrikanth,Shade Ruangwan,Lei Liu,Yuji Mizobuchi,Masahiro Fukuyori,Chen Wei-Peng,Kazuki Munakata,Tim Menzies +8 more
TL;DR: PyTorrent as mentioned in this paper is a large-scale collection of both semantic and natural language resources to leverage active Software Engineering research areas such as code reuse and code comprehensibility, and it contains 218,814 Python package libraries from PyPI and Anaconda environments.
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Clone-Seeker: Effective Code Clone Search Using Annotations.
TL;DR: Clone-Seeker as discussed by the authors uses a pre-processed list of identifiers from the code clones augmented with a list of keywords indicating the semantics of the code clone, which can be extracted from a manually annotated general description of the clone class, or automatically generated from the source code of the entire clone class.
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Assessing Project-Level Fine-Tuning of ML4SE Models
TL;DR: It is shown that per-project fine-tuning can greatly improve the models’ quality as they capture the project’s domain and naming conventions.
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Language-Agnostic Representation Learning of Source Code from Structure and Context.
TL;DR: This article proposed a new model which jointly learns on Context and Structure of source code, and obtained state-of-the-art results on all five programming languages considered in this work.
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