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
CommitBench: A Benchmark for Commit Message Generation
Maximilian Schall,Tamara Czinczoll,Gerard de Melo +2 more
TL;DR: CommitBench is a new benchmark for commit message generation that addresses data quality issues and biases in existing datasets. It includes a large-scale dataset and evaluation metrics for comparing models.
OCoR: an overlapping-aware code retriever
Qihao Zhu,Zeyu Sun,Xiran Liang,Yingfei Xiong,Lu Zhang +4 more
- 21 Dec 2020
TL;DR: Zhang et al. as mentioned in this paper proposed a novel neural architecture named OCoR1, which embeds names by characters to capture the overlaps between names and introduces a novel overlap matrix to represent the degrees of overlaps.
How Robust Is a Large Pre-trained Language Model for Code Generationƒ A Case on Attacking GPT2
01 Mar 2023
TL;DR: In this article , the Modifier for Code Generation Attack (M-CGA) method is proposed to measure the robustness of a model by producing adversarial examples that can cause the model to produce code that is incorrect or does not meet the criteria for use.
Peer Review
Exploring Automated Code Evaluation Systems and Resources for Code Analysis: A Comprehensive Survey
Md. Mostafizer Rahman,Yutaka Watanobe,Atsushi Shirafuji,Mohamed Hamada +3 more
- 08 Jul 2023
TL;DR: A comprehensive survey on automated code evaluation system (AES) and their resources is presented in this paper , which explores the application areas of AESs, available resources, and resource utilization for coding tasks.
•Posted Content
Memorization and Generalization in Neural Code Intelligence Models.
Md. Rafiqul Islam Rabin,Md. Rafiqul Islam Rabin,Aftab Hussain,Vincent J. Hellendoorn,Mohammad Amin Alipour +4 more
TL;DR: This work evaluates the memorization and generalization tendencies in neural code intelligence models through a case study across several benchmarks and model families by leveraging established approaches from other fields that use DNNs, such as introducing targeted noise into the training dataset.
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