CoDesc: A Large Code–Description Parallel Dataset
By Masum Hasan, Tanveer Muttaqueen, Abdullah Al Ishtiaq, Kazi Sajeed Mehrab, Md. Mahim Anjum Haque, Tahmid Hasan, Wasi Uddin Ahmad, Anindya Iqbal, Rifat Shahriyar. 2021
41 citations recorded by the source corpus.
Translation between natural language and source code can help software development by enabling developers to comprehend, ideate, search, and write computer programs in natural language. Despite growing interest from the industry and the research community, this task is often difficult due to the lack of large standard datasets suitable for training deep neural models, standard noise removal methods, and evaluation benchmarks. This leaves researchers to collect new small-scale datasets, resulting in inconsistencies across published works. In this study, we present CoDesc -- a large parallel dataset composed of 4.2 million Java methods and natural language descriptions. With extensive analysis, we identify and remove prevailing noise patterns from the dataset. We demonstrate the proficiency of CoDesc in two complementary tasks for code-description pairs: code summarization and code search. We show that the dataset helps improve code search by up to 22\% and achieves the new state-of-the-art in code summarization. Furthermore, we show CoDesc's effectiveness in pre-training--fine-tuning setup, opening possibilities in building pretrained language models for Java. To facilitate future research, we release the dataset, a data processing tool, and a benchmark at \url{https://github.com/csebuetnlp/CoDesc}.
Subfields: Evaluation & Benchmarks, Code Generation & Program Synthesis
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Citations in the corpus
- LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code (2024, background): “…(Mir et al., 2022; Wei et al., 2023a; Malik et al., 2019), code summarization (LeClair et al., 2019; Iyer et al., 2016; Barone and Sennrich, 2017; Hasan et al., 2021; Alon et al., 2018), code security (Liguori et al., 2022; Pearce et al., 2022; Tony et al., 2023), etc. 2023) demonstrated…”
- Multilingual training for Software Engineering (2021, background): “…an active research area, including many projects such as CodeNN [30], DeepCom [26], Astattgru[40], CodeBERT [18], Rencos [74],SecNN [42],PLBART [1], CoTexT[54], ProphetNet-X [55], NCS [2], Code2seq[7], Re 2 Com [71], and many more [19, 24, 25, 27, 28, 38, 39, 41, 49, 50, 66, 67, 69, 70, 72, 73].”