Code search: A survey of techniques for finding code

L Di Grazia, M Pradel - ACM Computing Surveys, 2023 - dl.acm.org
The immense amounts of source code provide ample challenges and opportunities during
software development. To handle the size of code bases, developers commonly search for …

Opportunities and challenges in code search tools

C Liu, X Xia, D Lo, C Gao, X Yang… - ACM Computing Surveys …, 2021 - dl.acm.org
Code search is a core software engineering task. Effective code search tools can help
developers substantially improve their software development efficiency and effectiveness. In …

Graph neural networks: foundation, frontiers and applications

L Wu, P Cui, J Pei, L Zhao, X Guo - … of the 28th ACM SIGKDD Conference …, 2022 - dl.acm.org
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

When deep learning met code search

J Cambronero, H Li, S Kim, K Sen… - Proceedings of the 2019 …, 2019 - dl.acm.org
There have been multiple recent proposals on using deep neural networks for code search
using natural language. Common across these proposals is the idea of embedding code …

Infercode: Self-supervised learning of code representations by predicting subtrees

NDQ Bui, Y Yu, L Jiang - 2021 IEEE/ACM 43rd International …, 2021 - ieeexplore.ieee.org
Learning code representations has found many uses in software engineering, such as code
classification, code search, comment generation, and bug prediction, etc. Although …

A survey on machine learning techniques for source code analysis

T Sharma, M Kechagia, S Georgiou, R Tiwari… - arXiv preprint arXiv …, 2021 - arxiv.org
The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

Assessing generalizability of codebert

X Zhou, DG Han, D Lo - 2021 IEEE International Conference on …, 2021 - ieeexplore.ieee.org
Pre-trained models like BERT have achieved strong improvements on many natural
language processing (NLP) tasks, showing their great generalizability. The success of pre …

Self-supervised contrastive learning for code retrieval and summarization via semantic-preserving transformations

NDQ Bui, Y Yu, L Jiang - Proceedings of the 44th International ACM …, 2021 - dl.acm.org
We propose Corder, a self-supervised contrastive learning framework for source code
model. Corder is designed to alleviate the need of labeled data for code retrieval and code …

Aroma: Code recommendation via structural code search

S Luan, D Yang, C Barnaby, K Sen… - Proceedings of the ACM …, 2019 - dl.acm.org
Programmers often write code that has similarity to existing code written somewhere. A tool
that could help programmers to search such similar code would be immensely useful. Such …

Improving code search with co-attentive representation learning

J Shuai, L Xu, C Liu, M Yan, X Xia, Y Lei - Proceedings of the 28th …, 2020 - dl.acm.org
Searching and reusing existing code from a large-scale codebase, eg, GitHub, can help
developers complete a programming task efficiently. Recently, Gu et al. proposed a deep …