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30th IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Fu, Liming Liang, Peng Zhang, Beiqi Wuhan Univ Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781450392983

code review is a mature practice for software quality assurance in software development with which reviewers check the code that has been committed by developers, and verify the quality of code. During the code review discussions, reviewers and developers might use code snippets to provide necessary information (e.g., suggestions or explanations). However, little is known about the intentions and impacts of code snippets in code reviews. To this end, we conducted a preliminary study to investigate the nature of code snippets and their purposes in code reviews. We manually collected and checked 10,790 review comments from the Nova and Neutron projects of the OpenStack community, and finally obtained 626 review comments that contain code snippets for further analysis. The results show that: (1) code snippets are not prevalently used in code reviews, and most of the code snippets are provided by reviewers. (2) We identified two high-level purposes of code snippets provided by reviewers (i.e., Suggestion and Citation) with six detailed purposes, among which, Improving code Implementation is the most common purpose. (3) For the code snippets in code reviews with the aim of suggestion, around 68.1% was accepted by developers. The results highlight promising research directions on using code snippets in code reviews.

关键词： code snippet code Review OpenStack

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PLangRec: Deep-learning model to predict the programming language from single line of code

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 166卷

作者： Rodriguez-Prieto, Oscar Pato, Alejandro Ortin, Francisco Univ Oviedo Comp Sci Dept C Leopoldo Calvo Sotelo 18 Oviedo 33007 Spain Munster Technol Univ Comp Sci Dept Rossa Ave Cork Ireland

Programming language detection from source code excepts remains an active research field, which has already been addressed with machine learning and natural language processing. Identifying the language of short code snippets poses both benefits and challenges across various scenarios, such as embedded code analysis, forums, Q&A systems, search engines, source code repositories, and text editors. Existing approaches for language detection typically require multiple lines or even the entire file contents. In this article, we propose a character- level deep learning model designed to predict the programming language from a single line of code. To this aim, we construct a balanced dataset comprising 434.18 million instances across 21 languages, significantly exceeding the size of existing datasets by three orders of magnitude. Leveraging this dataset, we train a deep bidirectional recurrent neural network that achieves a 95.07% accuracy and macro-F1 score fora single-line code. To predict the programming language of multiple lines (e.g., code snippets) and entire files, we build a stacking ensemble meta-model that leverages our single-line model to efficiently recognize the language of multiple lines of code. Our system outperforms the state-of-the-art approaches not only fora single line of code, but also for snippets of 5 and 10 lines and whole files of source code. We also present PLangRec, an open-source language detection system that includes our trained models. PLangRec is freely available as a user-friendly web application, a web API, and a Python desktop program.

关键词： Deep learning Massive source code datasets Language recognition Big data code snippet PLangRec

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Automatic bi-modal question title generation for Stack Overflow with prompt learning

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EMPIRICAL SOFTWARE ENGINEERING 2024年第3期29卷 63-63页

作者： Yang, Shaoyu Chen, Xiang Liu, Ke Yang, Guang Yu, Chi Nantong Univ Sch Informat Sci & Technol Nantong Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

When drafting question posts for Stack Overflow, developers may not accurately summarize the core problems in the question titles, which can cause these questions to not get timely help. Therefore, improving the quality of question titles has attracted the wide attention of researchers. An initial study aimed to automatically generate the titles by only analyzing the code snippets in the question body. However, this study ignored the helpful information in their corresponding problem descriptions. Therefore, we propose an approach SOTitle+ by considering bi-modal information (i.e., the code snippets and the problem descriptions) in the question body. Then we formalize the title generation for different programming languages as separate but related tasks and utilize multi-task learning to solve these tasks. Later we fine-tune the pre-trained language model codeT5 to automatically generate the titles. Unfortunately, the inconsistent inputs and optimization objectives between the pre-training task and our investigated task may make fine-tuning hard to fully explore the knowledge of the pre-trained model. To solve this issue, SOTitle+ further prompt-tunes codeT5 with hybrid prompts (i.e., mixture of hard and soft prompts). To verify the effectiveness of SOTitle+, we construct a large-scale high-quality corpus from recent data dumps shared by Stack Overflow. Our corpus includes 179,119 high-quality question posts for six popular programming languages. Experimental results show that SOTitle+ can significantly outperform four state-of-the-art baselines in both automatic evaluation and human evaluation. In addition, our ablation studies also confirm the effectiveness of component settings (such as bi-modal information, prompt learning, hybrid prompts, and multi-task learning) of SOTitle+. Our work indicates that considering bi-modal information and prompt learning in Stack Overflow title generation is a promising exploration direction.

关键词： Question Title Generation Bi-modal Information code snippet Problem Description Prompt Learning Multi-task Learning

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Stylometry for real-world expert coders: a zero-shot approach

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2429-e2429页

作者： Gurioli, Andrea Gabbrielli, Maurizio Zacchiroli, Stefano Univ Bologna Dept Comp Sci & Engn Bologna Italy Inst Polytech Paris LTCI Telecom Paris Paris France

code stylometry is the application of stylometry techniques to determine the authorship of software source code snippets. It is used in the industry to address use cases like plagiarism detection, code audits, and code review assignments. Most works in the code stylometry literature use machine learning techniques and (1) rely on datasets coming from in vitro coding competition for training, and (2) only attempt to recognize authors present in the training dataset (in-distribution authors). In this work we give a fresh look at code stylometry and challenge both these assumptions: (1) we recognize expert authors who contribute to real-world open-source projects, and (2) we show how to accurately recognize authors not present in the training set (out-distribution authors). We assemble a novel open dataset of code snippets for code stylometry tasks consisting of 114,400 code snippets, authored by 104 authors having contributed 1,100 snippets each. We develop a K-nearest neighbors algorithm (k-NN) classifier for the code stylometry task and train it on the dataset. Our system achieves a top accuracy of 69% among five randomly selected in-distribution authors, thus improving state of the art by more than 20%. We also show that when moving from in-distribution to out- distribution authors, the classification performances of the k-NN classifier remain the same, achieving a top accuracy of 71% among five randomly-selected out-distribution authors.

关键词： code stylometry code authorship attribution Machine learning Deep learning Data mining code snippet Source code Zero shot Metric learning

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From Typing to Insights: An Interactive code Visualization for Enhanced Student Support Using Keystroke Data 56

From Typing to Insights: An Interactive Code Visualization f...

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56th Annual SIGCSE Technical Symposium on Computer Science Education, SIGCSE TS 2025

作者： James, Friday E. Feldhausen, Russel Bean, Nathan H. Weese, Josh Allen, David S. Friend, Michelle Department of Computer Science Kansas State University Manhattan KS United States Department of Curriculum and Instruction Kansas State University Manhattan KS United States Department of Teacher Education University of Nebraska Omaha Omaha NE United States

ISBN: (纸本)9798400705328

The continuous rising of digital learning platforms has unarguably brought about a surge in the amount of data obtained from different learning environments, which presents a great opportunity for computer science teachers to gain an understanding of students' coding processes. This is vital for enhancing student support as teachers can gain insights into students' thought process, strategies and identify areas where students might be struggling while tackling programming tasks. Traditional assessment methods such as feedback after homework submissions or completed lab assignments often results in late and untimely intervention that would prevent early dropouts and massive failures as the students' learning journey is neglected. We leverage students' keystroke data obtained from Python and Java-based introductory programming courses delivered through CODIO learning platform - covering 3 semesters and having 13 programming assignments each, to design an interactive code visualization platform using Streamlit. The application features an interface that reproduces students' code snippets with JavaScript enabled syntax highlighting. It includes a combination of dropdown menu and an adjustable slider which enables an instructor to navigate through the timestamps and have a detailed view of students' coding processes. The intervention ultimately fosters a more supportive learning environment and helps boost students' confidence. © 2025 Copyright held by the owner/author(s).

关键词： code snippet coding process insight intervention keystroke data

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Big code Search: A Bibliography

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ACM COMPUTING SURVEYS 2024年第1期56卷 25-25页

作者： Kim, Kisub Ghatpande, Sankalp Kim, Dongsun Zhou, Xin Liu, Kui Bissyande, Tegawende F. Klein, Jacques Le Traon, Yves Singapore Management Univ 81 Victoria St Singapore 188065 Singapore 6 Rue Richard Coudenhove Kalergi L-1359 Luxembourg Luxembourg Kyungpook Natl Univ 80 Daehak ro Daegu South Korea 360 Jiangshu Rd Hangzhou 310051 Zhejiang Peoples R China Univ Luxembourg 6 Rue Richard Coudenhove Kalergi L-1359 Luxembourg Luxembourg

code search is an essential task in software development. Developers often search the internet and other code databases for necessary source code snippets to ease the development efforts. code search techniques also help learn programming as novice programmers or students can quickly retrieve (hopefully good) examples already used in actual software projects. Given the recurrence of the code search activity in software development, there is an increasing interest in the research community. To improve the code search experience, the research community suggests many code search tools and techniques. These tools and techniques leverage several different ideas and claim a better code search performance. However, it is still challenging to illustrate a comprehensive view of the field considering that existing studies generally explore narrow and limited subsets of used components. This study aims to devise a grounded approach to understanding the procedure for code search and build an operational taxonomy capturing the critical facets of code search techniques. Additionally, we investigate evaluation methods, benchmarks, and datasets used in the field of code search.

关键词： code search code recommendation code retrieval find code code snippet code search procedure

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SOTitle: A Transformer-based Post Title Generation Approach for Stack Overflow 29

SOTitle: A Transformer-based Post Title Generation Approach ...

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29th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Liu, Ke Yang, Guang Chen, Xiang Yu, Chi Nantong Univ Sch Informat Sci & Technol Nantong Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing Peoples R China

ISBN: (纸本)9781665437868

On Stack Overflow, developers can not only browse question posts to solve their programming problems but also gain expertise from the question posts to help improve their programming skills. Therefore, improving the quality of question posts in Stack Overflow has attracted the wide attention of researchers. A concise and precise title can play an important role in helping developers understand the key information of the question post, which can improve the post quality. However, the quality of the generated title is not high due to the lack of professional knowledge related to their questions or the poor presentation ability of developers. A previous study aimed to automatically generate the title by analyzing the code snippets in the question post. However, this study ignored the useful information in the corresponding problem description. Therefore, we propose an approach SOTitle for automatic post title generation by leveraging the code snippets and the problem description in the question post (i.e., the multi-modal input). SOTitle follows the Transformer structure, which can effectively capture long-term dependencies through a multi-head attention mechanism. To verify the effectiveness of SOTitle, we construct a large-scale high-quality corpus from Stack Overflow, which includes 1,168,257 high-quality question posts for four popular programming languages. Experimental results show that SOTitle can significantly outperform six state-of-the-art baselines in both automatic evaluation and human evaluation. To encourage follow-up studies, we make our corpus and approach publicly available.

关键词： Post title generation Question post quality Stack Overflow mining Transformer code snippet Problem description

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Ontology-based code snippets management in a cloud environment

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第8期10卷 2971-2985页

作者： Shi, Jianjun Ji, Weixing Gao, Zhiwei Gao, Yujin Wang, Yizhuo Liao, Xinyi Shi, Feng Beijing Inst Technol Sch Comp Sci & Technol 5 South Zhongguancun St Beijing 100081 Peoples R China

A code snippet is a small region of reusable source code that is common to many functions. Skilled management and reuse of snippets can improve programming efficiency in practice. However, we find that most snippets are posted to online blogs or snippet management systems with little description and tagging, which leads to an embarrassing situation in which existing snippets are difficult to reuse. This occurs because snippets are usually hastilypushed to online services or saved in text files by programmers and also because existing management systems do not provide efficient labeling and reusing frameworks. In this paper, we propose to annotate snippets with a well-formed domain-specific ontology-programming ontology. With a thorough investigation of real world snippets, we designed a programming ontology to annotate and recommend snippets. We show how to annotate a snippet with ontology terms based on text classification models. In addition, we built a snippet management system that stores user snippets in the cloud and automatically recommends snippets, so that the retrieval of snippets becomes trivial in popular integrated developing environments, such as Eclipse and Visual Studio. Our evaluation results demonstrate that theontology annotation algorithm is able to automatically annotate a snippet with a high degree of accuracy. The shared domain knowledge also makes it possible to share snippets among programmers and systems. As the number of labeled snippets increases, deep learning models can be trained and used to annotate code snippets with high accuracy.

关键词： Ontology code snippet Programming language Cloud computing Deep learning

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Dual-interactive fusion for code-mixed deep representation learning in tag recommendation

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INFORMATION FUSION 2023年第1期99卷

作者： Li, Lin Wang, Peipei Zheng, Xinhao Xie, Qing Tao, Xiaohui Velasquez, Juan D. Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China Univ Southern Queensland Sch Math Phys & Comp Toowoomba Australia Inst Sistemas Complejos Ingn ISCI Santiago Chile Univ Chile Fac Phys & Math Sci Dept Ind Engn Santiago Chile

Automatic tagging on software information sites is a tag recommendation service. It aims to recommend content-based tags for a software object to help developers make distinctions among software objects. Due to deep correlations between software objects and tags, it is challenging to simultaneously consider the code snippet and text description of a software object. Towards automatic tagging service, we propose a novel CDR4Tag method, code-mixed Deep Representation learning via dual-interactive fusion for Tag recommendation on software information sites. In CDR4Tag, a code-mixed dual interaction strategy is designed to fuse the deep semantic correlations between software objects and tags into a joint representation space. On the basis of it, the matching probability is predicted to complete our tag recommendation. Comprehensive experimental results on four software information site datasets have demonstrated the effectiveness of our proposed CDR4Tag in tag recommendation compared with the state-of-the-art methods.

关键词： Interactive information fusion code snippet Automatic tagging

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On opportunistic software reuse

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COMPUTING 2020年第11期102卷 2385-2408页

作者： Makitalo, Niko Taivalsaari, Antero Kiviluoto, Arto Mikkonen, Tommi Capilla, Rafael Univ Helsinki Dept Comp Sci Helsinki Finland Nokia Bell Labs Tampere Finland Rey Juan Carlos Univ Dept Comp Sci Madrid Spain

The availability of open source assets for almost all imaginable domains has led the software industry toopportunistic design-an approach in which people develop new software systems in an ad hoc fashion by reusing and combining components that were not designed to be used together. In this paper we investigate this emerging approach. We demonstrate the approach with an industrial example in *** and various subsystems are used in an opportunistic way. Furthermore, to study opportunistic reuse as a phenomenon, we present the results of three contextual interviews and a survey with reuse practitioners to understand to what extent opportunistic reuse offers improvements over traditional systematic reuse approaches.

关键词： Software reuse Software engineering Opportunistic design Opportunistic reuse Software architecture code snippet

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