Anne

Vinayakarao, Venkatesh; Sarma, Anita; Purandare, Rahul; Jain, Shuktika; Jain, Shuchi

doi:10.1145/3018661.3018691

Cited by 20 publications

(4 citation statements)

References 30 publications

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“…Code Retrieval. As introduced in Section 1, code retrieval has been studied widely with information retrieval methods [14,17,23,32,56] and recent deep learning models [3,13,21]. Particularly, Keivanloo et al [23] extracted abstract programming patterns and their associated NL keywords from code snippets in a code base, with which a given NL query can be projected to a set of associated programming patterns facilitating code content-based search.…”

Section: Related Workmentioning

confidence: 99%

“…Particularly, Keivanloo et al [23] extracted abstract programming patterns and their associated NL keywords from code snippets in a code base, with which a given NL query can be projected to a set of associated programming patterns facilitating code content-based search. Similarly, Vinayakarao et al [56] built an entity discovery system to mine NL phrases and their associated syntactic patterns, based on which they annotated each line of code snippets with NL phrases. Such annotations were utilized to improve NL keywordbased search engines.…”

Section: Related Workmentioning

confidence: 99%

“…a given NL query in the code retrieval task). As exemplified by [56], such an annotation can be taken as the representation of the corresponding code snippet, based on which the aforementioned lexical mismatch issue in a naive keyword-based search engine can be alleviated. A similar idea of improving retrieval by adding extra annotations to items is also explored in document retrieval [47] and image search [61].…”

mentioning

confidence: 99%

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CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning

Yao

Peddamail

Sun

2019

The World Wide Web Conference

107

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To accelerate software development, much research has been performed to help people understand and reuse the huge amount of available code resources. Two important tasks have been widely studied: code retrieval, which aims to retrieve code snippets relevant to a given natural language query from a code base, and code annotation, where the goal is to annotate a code snippet with a natural language description. Despite their advancement in recent years, the two tasks are mostly explored separately. In this work, we investigate a novel perspective of Code annotation for Code retrieval (hence called "CoaCor"), where a code annotation model is trained to generate a natural language annotation that can represent the semantic meaning of a given code snippet and can be leveraged by a code retrieval model to better distinguish relevant code snippets from others. To this end, we propose an effective framework based on reinforcement learning, which explicitly encourages the code annotation model to generate annotations that can be used for the retrieval task. Through extensive experiments, we show that code annotations generated by our framework are much more detailed and more useful for code retrieval, and they can further improve the performance of existing code retrieval models significantly. 1

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning

Yao

Peddamail

Sun

2019

The World Wide Web Conference

107

View full text Add to dashboard Cite

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“…As introduced in previous sections, code retrieval has been studied widely with information retrieval methods [12,14,18,19,29] and recent deep learning methods [3,11,17]. Chen et al [8] used VAEs to model both source code and natural language.…”

Section: Related Work 61 Code Retrievalmentioning

confidence: 99%

Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning

Xie

Zhang

et al. 2020

Proceedings of the Web Conference 2020

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Code summarization generates brief natural language description given a source code snippet, while code retrieval fetches relevant source code given a natural language query. Since both tasks aim to model the association between natural language and programming language, recent studies have combined these two tasks to improve their performance. However, researchers have yet been able to effectively leverage the intrinsic connection between the two tasks as they train these tasks in a separate or pipeline manner, which means their performance can not be well balanced. In this paper, we propose a novel end-to-end model for the two tasks by introducing an additional code generation task. More specifically, we explicitly exploit the probabilistic correlation between code summarization and code generation with dual learning, and utilize the two encoders for code summarization and code generation to train the code retrieval task via multi-task learning. We have carried out extensive experiments on an existing dataset of SQL and Python, and results show that our model can significantly improve the results of the code retrieval task over the-state-of-art models, as well as achieve competitive performance in terms of BLEU score for the code summarization task.

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IR-based technique for linearizing abstract method invocation in plagiarism-suspected source code pair

Karnalim

2019

Journal of King Saud University - Computer and Information Scie

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Anne

Cited by 20 publications

References 30 publications

CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning

CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning

Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning

IR-based technique for linearizing abstract method invocation in plagiarism-suspected source code pair

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