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Unveiling Python Version Compatibility Challenges in code snippets on Stack Overflow

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2024年第8期E107D卷 1007-1015页

作者： Yang, Shiyu Kanda, Tetsuya German, Daniel M. Higo, Yoshiki Osaka Univ Grad Sch Informat Sci & Technol Suita 5650871 Japan Univ Victoria Dept Comp Sci Victoria BC V8P 5C2 Canada

Stack Overflow, a leading Q&A platform for developers, is a substantial reservoir of Python code snippets. Nevertheless, the incompatibility issues between Python versions, particularly Python 2 and Python 3, introduce substantial challenges that can potentially jeopardize the utility of these code snippets. This empirical study dives deep into the challenges of Python version inconsistencies on the interpretation and application of Python code snippets on Stack Overflow. Our empirical study exposes the prevalence of Python version compatibility issues on Stack Overflow. It further emphasizes an apparent deficiency in version-specific identification, a critical element that facilitates the identification and utilization of Python code snippets. These challenges, primarily arising from the lack of backward compatibility between Python's major versions, pose significant hurdles for developers relying on Stack Overflow for code references and learning. This study, therefore, signifies the importance of proactively addressing these compatibility issues in Python code snippets. It advocates for enhanced tools and strategies to assist developers in efficiently navigating through the Python version complexities on platforms like Stack Overflow. By highlighting these concerns and providing a potential remedy, we aim to contribute to a more efficient and effective programming experience on Stack Overflow and similar platforms.

关键词： Stack Overflow Python code snippets version compatibility

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Demystifying code snippets in code reviews: a study of the OpenStack and Qt communities and a practitioner survey

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EMPIRICAL SOFTWARE ENGINEERING 2024年第4期29卷 78-78页

作者： Zhang, Beiqi Fu, Liming Liang, Peng Yu, Jiaxin Wang, Chong Wuhan Univ Sch Comp Sci Wuhan Peoples R China Hubei Luojia Lab Wuhan Peoples R China

code review is widely known as one of the best practices for software quality assurance in software development. In a typical code review process, reviewers check the code committed by developers to ensure the quality of the code, during which reviewers and developers would communicate with each other in review comments to exchange necessary information. As a result, understanding the information in review comments is a prerequisite for reviewers and developers to conduct an effective code review. code snippet, as a special form of code, can be used to convey necessary information in code reviews. For example, reviewers can use code snippets to make suggestions or elaborate their ideas to meet developers' information needs in code reviews. However, little research has focused on the practices of providing code snippets in code reviews. To bridge this gap, we conduct a mixed-methods study to mine information and knowledge related to code snippets in code reviews, which can help practitioners and researchers get a better understanding about using code snippets in code review. Specifically, our study includes two phases: mining code review data and conducting practitioners' survey. In Phase 1, we conducted an exploratory study to mine code review data from two popular developer communities (i.e., OpenStack and Qt). We manually labelled 69,604 review comments and finally identified 3,213 review comments that contain code snippets. Based on the code review data collected, we analyzed the extent of using code snippets, the reviewers' purposes of providing code snippets, the developers' acceptance of code snippet suggestions, and the reasons that developers do not accept code snippet suggestions in code reviews. In Phase 2, we used an online questionnaire to survey practitioners from industry. By analyzing the 63 valid responses we received, we explored the scenarios reviewers provide code snippets, the developers' attitudes towards code snippets, and the characteristics o

关键词： Modern code review code snippets Mining software repositories Empirical study

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Usage and attribution of Stack Overflow code snippets in GitHub projects

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EMPIRICAL SOFTWARE ENGINEERING 2019年第3期24卷 1259-1295页

作者： Baltes, Sebastian Diehl, Stephan Univ Trier Software Engn Grp Trier Germany

Stack Overflow (SO) is the most popular question-and-answer website for software developers, providing a large amount of copyable code snippets. Using those snippets raises maintenance and legal issues. SO's license (CC BY-SA 3.0) requires attribution, i.e., referencing the original question or answer, and requires derived work to adopt a compatible license. While there is a heated debate on SO's license model for code snippets and the required attribution, little is known about the extent to which snippets are copied from SO without proper attribution. We present results of a large-scale empirical study analyzing the usage and attribution of non-trivial Java code snippets from SO answers in public GitHub (GH) projects. We followed three different approaches to triangulate an estimate for the ratio of unattributed usages and conducted two online surveys with software developers to complement our results. For the different sets of projects that we analyzed, the ratio of projects containing files with a reference to SO varied between 3.3% and 11.9%. We found that at most 1.8% of all analyzed repositories containing code from SO used the code in a way compatible with CC BY-SA 3.0. Moreover, we estimate that at most a quarter of the copied code snippets from SO are attributed as required. Of the surveyed developers, almost one half admitted copying code from SO without attribution and about two thirds were not aware of the license of SO code snippets and its implications.

关键词： code snippets Licensing Stack Overflow GitHub Online survey Mining software repositories

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Towards Finding code snippets on a Question and Answer Website Causing Mobile App Vulnerabilities

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2018年第11期E101D卷 2576-2583页

作者： Nakano, Hiroki Kanei, Fumihiro Takata, Yuta Akiyama, Mitsuaki Yoshioka, Katsunari Yokohama Natl Univ Grad Sch Environm & Informat Sci Yokohama Kanagawa 2408501 Japan NTT Secure Platform Labs Musashino Tokyo 1808585 Japan NTT Secure Platform Labs Cyber Secur Project Musashino Tokyo 1808585 Japan Yokohama Natl Univ Grad Sch Environm & Informat Sci Inst Adv Sci Yokohama Kanagawa 2408501 Japan

Android app developers sometimes copy code snippets posted on a question- and-answer (Q&A) website and use them in their apps. However, if a code snippet has vulnerabilities, Android apps containing the vulnerable snippet could also have the same vulnerabilities. Despite this, the effect of such vulnerable snippets on the Android apps has not been investigated in depth. In this paper, we investigate the correspondence between the vulnerable code snippets and vulnerable apps. we collect code snippets from a Q&A website, extract possibly vulnerable snippets, and calculate similarity between those snippets and bytecode on vulnerable apps. Our experimental results show that 15.8% of all evaluated apps that have SSL implementation vulnerabilities (Improper host name verification), 31.7% that have SSL certificate verification vulnerabilities, and 3.8% that have WEBVIEW remote code execution vulnerabilities contain possibly vulnerable code snippets from Stack Overflow. In the worst case, a single problematic snippet has caused 4,844 apps to contain a vulnerability, accounting for 31.2% of all collected apps with that vulnerability.

关键词： Mobile App Vulnerabilities Question and Answer Website code snippets

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SourceSnippet2Binary: A Method for Searching Vulnerable Source code snippets in Binaries 32

SourceSnippet2Binary: A Method for Searching Vulnerable Sour...

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32nd IEEE International Symposium on Software Reliability Engineering (ISSRE)

作者： Wu, Qinqin Huang, Hao Tang, Yi Gu, Zhenwei Jia, Ang Guangdong Power Grid Co Ltd Dept Cyber Secur Guangdong Power Dispatching & Controlling Ctr Guangzhou Guangdong Peoples R China Xi An Jiao Tong Univ Sch Cyber Sci & Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9781665426039

Vulnerability detection, known as research to detect whether a target code contains vulnerabilities, is often conducted at source code level or binary code level. However, in some cases, the vulnerable code is usually at source-level as they are found by software developers, and target code is usually in binary-level as they are released to be executed in different operating systems, which pose new requirements for vulnerability detection. In this paper, we illustrate a new method named SourceSnippet2Binary, trying to use vulnerable source code snippets to search whether a binary code contains the vulnerability. By proposing such a method, we hope further studies to spend more effort in source2binary vulnerability detection and make vulnerability detection more comprehensive.

关键词： vulnerability detection source2binary code snippets

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Attribution Required: Stack Overflow code snippets in GitHub Projects 17

Attribution Required: Stack Overflow Code Snippets in GitHub...

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IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C)

作者： Baltes, Sebastian Kiefer, Richard Diehl, Stephan Univ Trier Software Engn Grp Trier Germany

ISBN: (纸本)9781538615898

Stack Overflow (SO) is the largest Q&A website for developers, providing a huge amount of copyable code snippets. Using these snippets raises various maintenance and legal issues. The SO license requires attribution, i.e., referencing the original question or answer, and requires derived work to adopt a compatible license. While there is a heated debate on SO's license model for code snippets and the required attribution, little is known about the extent to which snippets are copied from SO without proper attribution. In this paper, we present the research design and summarized results of an empirical study analyzing attributed and unattributed usages of SO code snippets in GitHub projects. On average, 3.22% of all analyzed repositories and 7.33% of the popular ones contained a reference to SO. Further, we found that developers rather refer to the whole thread on SO than to a specific answer. For Java, at least two thirds of the copied snippets were not attributed.

关键词： empirical study code snippets copy-and-paste programming licensing stack overflow github survey

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Analyzing the Impact of Copying-and-Pasting Vulnerable Solidity code snippets from Question-and-Answer Websites 24

Analyzing the Impact of Copying-and-Pasting Vulnerable Solid...

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ACM Internet Measurement Conference (IMC)

作者： Weiss, Konrad Torres, Christof Ferreira Wendland, Florian Fraunhofer AISEC Garching Germany Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9798400705922

Ethereum smart contracts are executable programs deployed on a blockchain. Once deployed, they cannot be updated due to their inherent immutability. Moreover, they often manage valuable assets that are worth millions of dollars, making them attractive targets for attackers. The introduction of vulnerabilities in programs due to the reuse of vulnerable code posted on Q&A websites such as Stack Overflow is not a new issue. However, little effort has been made to analyze the extent of this issue on deployed smart contracts. In this paper, we conduct a study on the impact of vulnerable code reuse from Q&A websites during the development of smart contracts and provide tools uniquely fit to detect vulnerable code patterns in complete and incomplete Smart Contract code. This paper proposes a pattern-based vulnerability detection tool that is able to analyze code snippets (i.e., incomplete code) as well as full smart contracts based on the concept of code property graphs. We also propose a methodology that leverages fuzzy hashing to quickly detect code clones of vulnerable snippets among deployed smart contracts. Our results show that our vulnerability search, as well as our code clone detection, are comparable to state-of-the-art while being applicable to code snippets. Our large-scale study on 18,660 code snippets reveals that 4,596 of them are vulnerable, out of which 616 can be found in 17,852 deployed smart contracts. These results highlight that the reuse of vulnerable code snippets is indeed an issue in currently deployed smart contracts.

关键词： smart contracts code snippets code property graph fuzzy hashing

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APIzation: Generating Reusable APIs from StackOverflow code snippets 36

APIzation: Generating Reusable APIs from StackOverflow Code ...

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36th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Terragni, Valerio Salza, Pasquale Univ Auckland Auckland New Zealand Univ Zurich Zurich Switzerland

ISBN: (纸本)9781665403375

Developer forums like StackOverflow have become essential resources to modern software development practices. However, many code snippets lack a well-defined method declaration, and thus they are often incomplete for immediate reuse. Developers must adapt the retrieved code snippets by parameterizing the variables involved and identifying the return value. This activity, which we call APIzation of a code snippet, can be tedious and time-consuming. In this paper, we present APIzAToR to perform APIzations of JAVA code snippets automatically. APIzAToR is grounded by four common patterns that we extracted by studying real APIzations in GitHub. APIzAToR presents a static analysis algorithm that automatically extracts the method parameters and return statements. We evaluated APIzAToR with a ground-truth of 200 APIzations collected from 20 developers. For 113 (56.50 %) and 115 (57.50 %) APIzations, APIzAToR and the developers extracted identical parameters and return statements, respectively. For 163 (81.50 %) APIzations, either the parameters or the return statements were identical.

关键词： APIs software reuse code snippets StackOverflow GitHub program analysis program synthesis

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Employing Source code Quality Analytics for Enriching code snippets Data

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DATA 2023年第9期8卷

作者： Karanikiotis, Thomas Diamantopoulos, Themistoklis Symeonidis, Andreas Aristotle Univ Thessaloniki Elect & Comp Engn Dept Thessaloniki 54124 Greece

The availability of code snippets in online repositories like GitHub has led to an uptick in code reuse, this way further supporting an open-source component-based development paradigm. The likelihood of code reuse rises when the code components or snippets are of high quality, especially in terms of readability, making their integration and upkeep simpler. Toward this direction, we have developed a dataset of code snippets that takes into account both the functional and the quality characteristics of the snippets. The dataset is based on the codeSearchNet corpus and comprises additional information, including static analysis metrics, code violations, readability assessments, and source code similarity metrics. Thus, using this dataset, both software researchers and practitioners can conveniently find and employ code snippets that satisfy diverse functional needs while also demonstrating excellent readability and maintainability.

关键词： mining software repositories source code mining readability static analysis metrics code snippets

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Leveraging an Enhanced codeBERT-Based Model for Multiclass Software Defect Prediction via Defect Classification

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IEEE ACCESS 2025年 13卷 24383-24397页

作者： Hussain, Rida Ghafoor Yow, Kin-Choong Gori, Marco Univ Florence Dept Informat Engn I-53100 Florence Italy Univ Regina Fac Engn & Appl Sci Regina SK S4S 0A2 Canada Univ Siena Dept Informat Engn I-53100 Siena Italy

Ensuring software reliability through early-stage defect prevention and prediction is crucial, particularly as software systems become increasingly complex. Automated testing has emerged as the most practical approach to achieving bug-free and efficient code. In this context, machine learning-driven methods, especially those leveraging natural language models, have gained significant traction for developing effective techniques. This paper introduces a novel framework for automating software defect prediction, focusing on eight specific defects: SIGFPE, NZEC, LOGICAL, SYNTAX, SIGSEGV, SIGABRT, SEMANTIC, and LINKER. Our research involves a specialized dataset comprising nine classes, including eight common programming errors and one error-free class. The goal is to enhance software testing and development processes by identifying defects within code snippets. The proposed framework utilizes a codeBERT-based algorithm for defect prediction, optimizing model hyperparameters to achieve superior accuracy. Comparative analysis against established models such as RoBERTa, Microsoft codeBERT, and GPT-2 demonstrates that our approach yields significant improvements in prediction performance, with accuracy gains of up to 20% and 7% respectively in binary and multi class experimentation. Empirical studies validate the effectiveness of neural language models like codeBERT for software defect prediction, highlighting substantial advancements in software testing and development techniques. These findings underscore the potential benefits of incorporating advanced machine learning models into the software development lifecycle.

关键词： Software codes Predictive models Computer bugs Source coding Deep learning Data models Transformers Semantics Measurement Software defect prediction codeBERT defects GPT code snippets software reliability

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