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COMPUTER 2025年第4期58卷 115-120页

作者： Riehle, Dirk Friedrich Alexander Univ Erlangen Nurnberg Open Source Software D-91058 Erlangen Germany

Cybersecurity threats and software supply chain attacks are at an all-time high. Customers and agencies keep tightening the requirements for their software. An important recent development is the practical use of soft... 详细信息

关键词： Bill Of Materials Open Source Open Source Software Supply Chain Source code European Union Software Components Copy And Paste List Of Components code snippets Dependency Graph Non Functional Requirements Large Customers Hierarchical Structure Use Of Information Unique Identifier Basic Requirements Types Of Components Copyright Holder Source code Analysis Package Manager Legal Information Legal Separation Binary Files Erroneous Data

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A Large-Scale Empirical Study of Actionable Warning Distribution Within Projects

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2054-2071页

作者： Ge, Xiuting Fang, Chunrong Li, Xuanye Zheng, Qirui Liu, Jia Zhao, Zhihong Chen, Zhenyu Nanjing University State Key Laboratory for Novel Software Technology Jiangsu210093 China

Static Analysis Tools (SATs) show potential defect detection ability while their usability is severely hindered by massive unactionable warnings. To improve the usability of SATs, many machine learning-based Actionable Warning Identification (AWI) studies have been proposed, which mainly focus on mining warning features and improving identification models to identify actionable warnings. However, the underlying distribution of the warning dataset, which is closely related to feature mining and thereby affects AWI model performance, is not well-explored by these studies. Further, there is a lack of a well-prepared warning dataset to support the distribution analysis. In this article, we first propose a warning dataset construction approach, which incorporates manual inspection and verification latency into postprocess labels from an advanced closed-warning heuristic and thereby acquire credible labels. Based on 10 large-scale and real-world projects with 25K+ revisions and 2087K+ SpotBugs warnings, we construct a qualified warning dataset with 11975 distinct warnings. Subsequently, we thoroughly analyze the actionable warning distribution within projects against our constructed dataset from six warning characteristics (i.e., category, type, priority, rank, file, and method). Based on the analysis results, we present 16 findings. Finally, a preliminary study demonstrates that our findings can be practical and instructive in improving the usability of SATs. © 2024 IEEE.

关键词： Manuals Feature Extraction Usability Inspection Germanium Defect Detection Noise Labeling Testing Target Tracking Static Analysis Warnings Actionable Warning Distribution Empirical Study Heuristic Manual Inspection Static Analysis Manual Verification Feature Mining Lifespan Support Vector Machine Conjecture Source code Machine Learning Models Programming Language Original Features Closed And Open Software Quality code Changes Priority Level Automatic Labeling Latest Revision Warning Labels code snippets Automatic Construction Pareto Principle

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How AI Agents Are Transforming Software Engineering and the Future of Product Development

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COMPUTER 2025年第5期58卷 71-77页

作者： Panyam, Sriram Gujar, Praveen Omlet Inc Sunnyvale CA 94087 USA LinkedIn Saratoga CA 95070 USA

Artificial Intelligence (AI) agents are revolutionizing software engineering, boosting productivity while creating new demands for skilled engineers and raising critical ethical challenges.

关键词： Intelligence Agencies Software Technology Software Development Ethical Challenges Routine Tasks code Generation Machine Learning Accountability Profession Cloud Computing Processing Software Human Experts Repetitive Tasks Debugging Security Vulnerabilities Artificial Intelligence Systems Artificial Intelligence Models Use Of Artificial Intelligence Software Quality Artificial Intelligence Tools Integration Of Artificial Intelligence Deskilling code Review Ethical Oversight Human Creativity code snippets

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Protocol to predict time perception bias from cortical neurite microstructures in healthy older adults

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STAR PROTOCOLS 2025年第2期6卷 103756页

作者： Gozdas, Elveda Kim, Trudy Jounghani, Ali Rahimpour Hosseini, S. M. Hadi Stanford Univ Sch Med Dept Psychiat & Behav Sci Computat Brain Res & Intervent C BRAIN Lab Stanford CA 94305 USA

Understanding cortical microstructure related to timing is key to studying aging and neurodegenerative diseases. Here, we present a protocol for identifying brain regions involved in time perception using microstructural measures from multi-shell diffusion MRI (dMRI). We outline steps for processing data, applying NODDI (neurite orientation dispersion and density imaging) modeling to extract cortical neurite parameters, and quantifying neurite microstructure patterns. Finally, we detail procedures for predicting time perception bias in a Time-Wall Estimation task using support vector regression (SVR) with cortical neurite microstructures from dMRI. For complete details on the use and execution of this protocol, please refer to Kim et al.1

关键词： protocol illustrating cortex organizing pipeline code snippets provided using cortical neurite

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How Do Users Answer MATLAB Questions on Q&A Sites? A Case Study on Stack Overflow and MathWorks 28

How Do Users Answer MATLAB Questions on Q&A Sites? A Case St...

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28th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Naghashzadeh, Mahshid Haghshenas, Amir Sami, Ashkan Lo, David Shiraz Univ Dept Commun & Elect Engn Shiraz Iran Shiraz Univ Dept Comp Sci & Engn & IT Shiraz Iran Singapore Management Univ Sch Informat Syst Singapore Singapore

ISBN: (纸本)9781728196305

MAT LAB is an engineering programming language with various toolboxes that has a dedicated Question and Answer (Q&A) platform on the MathWorks website, which is similar to Stack Overflow (SO). Moreover, some MATLAB users ask their questions on SO. This paper aims to compare these two Q&A platforms to see what kind of questions are asked and how developers answer these questions in each platform. The result of our analysis on 80,382 MATLAB questions on SO and 266,367 questions on MathWorks show that MATLAB questions on topics ranging from the MATLAB software installation to questions related to programming received high votes and accepted answers on MathWorks. However, the questions about basics of programming such as plots, functions, and variables and questions on converting MATLAB code to other programming languages are very likely to receive answers on SO. Our detailed analysis on SO shows that users answer MATLAB questions with the same rate of the accepted answer as other popular programming languages like Java and Python, but the rate of unanswered questions and questions without an accepted answer for Simulink and the three most popular MATLAB toolboxes -- image processing, signal processing, and computer vision -- are very high. To analyze the evolution of MATLAB questions on SO, we studied 80,382 MATLAB questions using the SOTorrent dataset. The patterns in MATLAB questions' evolution are: 1) Most of the revisions to questions are text-related and not on code snippets. 2) Most of the code-related revisions were performed by the original poster (OP). 3) Non-original posters (Non-OPs) usually revise code snippets' appearance, while OPs usually revise code snippets' content and logic.

关键词： MATLAB Stack Overflow MathWorks code snippets evolution question revision

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SOTorrent: Reconstructing and Analyzing the Evolution of Stack Overflow Posts 15

SOTorrent: Reconstructing and Analyzing the Evolution of Sta...

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ACM/IEEE 15th International Conference on Mining Software Repositories (MSR)

作者： Baltes, Sebastian Dumani, Lorik Treude, Christoph Diehl, Stephan Univ Trier Trier Germany Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781450357166

Stack Overflow (SO) is the most popular question-and-answer website for software developers, providing a large amount of code snippets and free-form text on a wide variety of topics. Like other software artifacts, questions and answers on SO evolve over time, for example when bugs in code snippets are fixed, code is updated to work with a more recent library version, or text surrounding a code snippet is edited for clarity. To be able to analyze how content on SO evolves, we built SOTorrent, an open dataset based on the official SO data dump. SOTorrent provides access to the version history of SO content at the level of whole posts and individual text or code blocks. It connects SO posts to other platforms by aggregating URLs from text blocks and by collecting references from GitHub files to SO posts. In this paper, we describe how we built SOTorrent, and in particular how we evaluated 134 different string similarity metrics regarding their applicability for reconstructing the version history of text and code blocks. Based on a first analysis using the dataset, we present insights into the evolution of SO posts, e.g., that post edits are usually small, happen soon after the initial creation of the post, and that code is rarely changed without also updating the surrounding text. Further, our analysis revealed a close relationship between post edits and comments. Our vision is that researchers will use SOTorrent to investigate and understand the evolution of SO posts and their relation to other platforms such as GitHub.

关键词： stack overflow software evolution open dataset code snippets

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Studying the Change Histories of Stack Overflow and GitHub snippets 18

Studying the Change Histories of Stack Overflow and GitHub S...

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29th IEEE/ACM International Conference on Program Comprehension (ICPC) / 18th IEEE/ACM International Conference on Mining Software Repositories (MSR)

作者： Manes, Saraj Singh Baysal, Olga Carleton Univ Sch Comp Sci Ottawa ON Canada

ISBN: (纸本)9781728187105

Stack Overflow is a popular Q&A forum for software developers, providing a large number of copyable code snippets. While GitHub is a collaborative development platform, developers often reuse Stack Overflow code in their GitHub projects. These snippets get revised or edited on each platform. In this work, we study Stack Overflow posts and the code snippets that are reused from these posts in GitHub projects. We investigate and compare the change history of SO snippets with the change history of GitHub snippets. We have applied a stratified random sampling when mining 440,000 GitHub projects to create a dataset representing the change history of the reused snippets;this dataset contains 22,900 GitHub projects, 33,765 Stack Overflow references mapped to 4,634 Stack Overflow posts, and a total of 73,322 commits. We analyze the evolution patterns of snippets on each platform, compare key trends and explore the co-change of these snippets. Our results demonstrate that 76% of snippets evolve on Stack Overflow, while only 22% of the reused code snippets evolve in GitHub. Stack Overflow snippets undergo fewer and smaller changes compared to their evolving counterparts on GitHub. The evolution of snippets on both platforms is driven by the original author of the content. Finally, we found that a small percentage of snippets is co-changing across two platforms, while snippets in GitHub and Stack Overflow evolve independently of one another.

关键词： code snippets change history evolution Stack Overflow GitHub time series co-change code reuse

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Readability and Understandability of snippets Recommended by General-purposeWeb Search Engines: a Comparative Study 36

Readability and Understandability of Snippets Recommended by...

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

作者： Dantas, Carlos Eduardo C. Maia, Marcelo A. Univ Fed Uberlandia Uberlandia MG Brazil

ISBN: (纸本)9781665435833

Developers often search for reusable code snippets on general-purpose web search engines like Google, Yahoo! or Microsoft Bing. But some of these code snippets may have poor quality in terms of readability or understandability. In this paper, we propose an empirical analysis to analyze the readability and understandability score from snippets extracted from the web using three independent variables: ranking, general-purpose web search engine and recommended site. We collected the top-5 recommended sites and their respective code snippet recommendations using Google, Yahoo!, and Bing for 9,480 queries, and evaluate their readability and understandability scores. We found that some recommended sites have significantly better readability and understandability scores than others. The better-ranked code snippet is not necessarily more readable or understandable than a lower-ranked code snippet for all general-purpose web search engines. Moreover, considering the readability score, Google has better-ranked code snippets compared to Yahoo! or Microsoft Bing.

关键词： readability understandability code snippets web search engines

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Smart Programming Playgrounds 37

Smart Programming Playgrounds

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IEEE/ACM 2nd International Workshop on Software Architecture and Metrics (SAM)

作者： Padhye, Rohan Dhoolia, Pankaj Mani, Senthil Sinha, Vibha Singhal IBM Res Yorktown Hts NY 10598 USA

ISBN: (纸本)9781479919345

Modern IDEs contain sophisticated components for inferring missing types, correcting bad syntax and completing partial expressions in code, but they are limited to the context that is explicitly defined in a project's configuration. These tools are ill-suited for quick prototyping of incomplete code snippets, such as those found on the Web in Q&A forums or walk-through tutorials, since such code snippets often assume the availability of external dependencies and may even contain implicit references to an execution environment that provides data or compute services. We propose an architecture for smart programming playgrounds that can facilitate rapid prototyping of incomplete code snippets through a semi-automatic context resolution that involves identifying static dependencies, provisioning external resources on the cloud and injecting resource bindings to handles in the original code fragment. Such a system could be potentially useful in a range of different scenarios, from sharing code snippets on the Web to experimenting with new ideas during traditional software development.

关键词： Cloud computing code snippets Dependency injection Playgrounds

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Semantic Rule-based Automatic code conversion System

Semantic Rule-based Automatic Code conversion System

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2020 International Conference on Data Science and Engineering, ICDSE 2020

作者： Gollapudi, Sai Tarun Sasi, Sreela Gannon University Department of Computer and Information Science EriePA16541 United States

ISBN: (纸本)9781728189192

Most software employees are facing challenges on integrating programs written in different languages for implementing the techniques for their software development. This can be achieved by automating the conversion of programs using natural language programming techniques. This research presents a novel 'Semantic Rule-based Automatic code conversion System (SRACS)' that uses semantic layering, keyword identification, and a semantic rule-based constructor. The code snippets for 'Hello World', 'For Loop', 'While Loop', 'If else', 'Factorial' and 'Travelling Salesman Program' are converted from Java to Python and vice versa, and the accuracies are presented. An average accuracy of 71.57% is achieved for the conversion of the code snippets from Java to Python, and a 77.07% is achieved for Python to Java. The accuracy is based on the 'accuracy in the conversion of the variables', 'accuracy in the conversion of the attributes' and on the 'proper indentation of the code in the target code'. © 2020 IEEE.

关键词： code conversion code snippets Java NLTK Python semantic-analysis translator

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