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stack overflow's hidden nuances: How does zip code define user contribution?

JOURNAL OF SYSTEMS AND SOFTWARE 2025年 223卷

作者： Zolduoarrati, Elijah Licorish, Sherlock A. Stanger, Nigel Univ Otago Dept Informat Sci Dunedin New Zealand

Online communities like stack overflow rely on collective intelligence, with developers often incorporating its code snippets within their software repositories. Despite this relevance, concerns remain regarding issues surrounding user participation within the platform. While these phenomena have been studied in isolation, literature investigating them under a unified lens remain scarce. Our work aims to bridge this gap by operationalising metrics to represent user participation, behaviour, and community value across US states and cities. Our findings show that users from rural states tend to have higher daily posts, more votes, and produce more readable, positive-toned content with fewer typos. Those from urbanised states nonetheless obtain more question favourites, post scores, and accrue more views to both their questions and their profiles. At the city level, users from cities with prominent R&D sectors were found to curate more content and engage more actively, while cities without a strong tech presence show higher disengagements, increased likelihood of lurking, and a tendency to write longer code snippets. Qualitative content analysis triangulates our findings where users from tech hubs favour technical jargon and collaborative knowledge-sharing, their posts buzzing with coding documentations, debugging pointers, and personal anecdotes. In contrast, rural users weave a tapestry of emotions, expressing hope, frustration, and contentment alongside their many questions. Our research uncovers a dynamic interplay between factors influencing user participation, behaviour, and community values. Rather than static dichotomies, these elements exhibit multifaceted influences, suggesting varying impacts from diverse factors like tech access, educational initiatives, and inherent behavioural tendencies.

关键词： Behaviour Community value Contribution Online CQA Participation stack overflow

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What are the emotions of developers towards deep learning documentation? - An exploratory study on stack overflow posts

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INFORMATION AND SOFTWARE TECHNOLOGY 2025年 179卷

作者： Venigalla, Akhila Sri Manasa Chimalakonda, Sridhar Indian Inst Technol Tirupati India

Context: Non native machine learning and deep learning (DL) developers face several challenges in using DL frameworks owing to the issues persistent in DL documentation. However, there are no studies that explore the reasons for issues in documentation. Objective: Investigating the underlying emotions in developer discussions on documentation could help in identifying reasons for issues in documentation. Hence, in this study, we analyse emotions of stack overflow posts corresponding to documentation of DL frameworks. Methodology: We identify relevant deep-learning related tags using integrated snowballing approach and extract 159.2K posts related to DL. We then identify documentation related posts among these using keyword matching approach, which resulted in 13,572 DL documentation related posts. We use Random Forest Classifier to build six emotion classifier models based on Gold Label Dataset for emotions. We then classify the extracted posts into each of the six emotions - Anger, Fear, Love, Joy, Sadness and Surprise using the classifier models, and curate the results. Results: We observe a large expression of anger and sadness, with more than half of posts having 'yolo' and 'activation-function' tags exhibiting these emotions, while Love emotion is predominantly present in posts with 'theano' tag. During our analysis, we observed that 40% of 'Body' and 'Answer' posts exhibited anger and sadness emotions. Conclusion: Our study reveals the large presence of Anger, Fear and Sadness emphasizing the need to improve DL framework documentation. Specifically, maintainers of the 'yolo' and 'matcaffe' libraries could improve their documentation, as the corresponding posts exhibit more of Anger and Sadness.

关键词： Deep learning Documentation Emotions stack overflow

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A systematic mapping study of crowd knowledge enhanced software engineering research using stack overflow

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JOURNAL OF SYSTEMS AND SOFTWARE 2025年 226卷

作者： Tanzil, Minaoar Hossain Chowdhury, Shaiful Modaberi, Somayeh Uddin, Gias Hemmati, Hadi Univ Calgary Calgary AB Canada UNIV MANITOBA WINNIPEG MB Canada York Univ Toronto ON Canada

Developers continuously interact in crowd-sourced community-based question-answer (Q&A) sites. Reportedly, similar to 30% of all software professionals visit the most popular Q&A site stackoverflow (SO) every day. Software engineering (SE) research studies are also increasingly using SO data. To find out the trend, implication, impact, and future research potential utilizing SO data, a systematic mapping study needs to be conducted. Following a rigorous reproducible mapping study approach, from 18 reputed SE journals and conferences, we collected 384 SO-based research articles and categorized them into 10 facets (i.e., themes). We found that SO contributes to 85% of SE research compared with popular Q&A sites such as Quora, and Reddit. We found that 18 SE domains directly benefited from SO data whereas Recommender Systems, and API Design and Evolution domains use SO data the most (15% and 16% of all SO-based research studies, respectively). API Design and Evolution, and Machine Learning with/for SE domains have consistent upward publication. Deep Learning Bug Analysis and Code Cloning research areas have the highest potential research impact recently. With the insights, recommendations, and facet-based categorized paper list from this mapping study, SE researchers can find out potential research areas according to their interest to utilize large-scale SO data.

关键词： Crowdsourcing Community question answer stack overflow Mapping study

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A cross-continental analysis of how regional cues shape top stack overflow contributors

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JOURNAL OF SYSTEMS AND SOFTWARE 2025年 223卷

作者： Zolduoarrati, Elijah Licorish, Sherlock A. Grundy, John Univ Otago Otago New Zealand Monash Univ Monash Australia

stack overflow offers valuable knowledge for software developers, but studies suggest digital information tends to cluster geographically, limiting access to necessary knowledge for innovation. This study explores posts of top contributors on stack overflow across the United States, Brazil, India, Egypt, the United Kingdom, and Australia. We analyse platform activities, conduct social network analysis, employ topic modelling paired with thematic analysis, before dissecting their knowledge sharing patterns via directed content analysis. Results indicate that cultural factors, entrepreneurial activities, tech ecosystem maturity, as well as workforce diversity in a region were found to shape how top contributors contribute. For instance, individualistic users communicate directly whilst collectivistic users prefer subtle communication and socio-emotional cues. Moreover, top contributors in nascent technology ecosystems were more likely to discuss fundamental concepts, while those in mature ecosystems focus on specialised niches. This study sheds light on how diversity in human aspects may influence the dynamics of CQA settings, where future researchers can explicate the extent of which latent contextual factors affect user contributions and community structure.

关键词： Contribution Cross-regional behaviour Repository mining Social network analysis Topic modelling stack overflow User/contributor interactions

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PTM4Tag+: Tag recommendation of stack overflow posts with pre-trained models

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EMPIRICAL SOFTWARE ENGINEERING 2025年第1期30卷 1-41页

作者： He, Junda Xu, Bowen Yang, Zhou Han, Donggyun Yang, Chengran Liu, Jiakun Zhao, Zhipeng Lo, David Singapore Management Univ Sch Comp & Informat Syst Singapore Singapore North Carolina State Univ Raleigh NC USA Royal Holloway Univ London Dept Comp Sci Egham England Univ Copenhagen Dept Comp Sci Copenhagen Denmark

stack overflow is one of the most influential Software Question & Answer (SQA) websites, hosting millions of programming-related questions and answers. Tags play a critical role in efficiently organizing the contents on stack overflow and are vital to support various site operations, such as querying relevant content. Poorly chosen tags often lead to issues such as tag ambiguity and tag explosion. Therefore, a precise and accurate automated tag recommendation technique is needed. Inspired by the recent success of pre-trained models (PTMs) in natural language processing (NLP), we present PTM4Tag+, a tag recommendation framework for stack overflow posts that utilize PTMs in language modeling. PTM4Tag+ is implemented with a triplet architecture, which considers three key components of a post, i.e., Title, Description, and Code, with independent PTMs. We utilize a number of popular pre-trained models, including BERT-based models (e.g., BERT, RoBERTa, CodeBERT, BERToverflow, and ALBERT), and encoder-decoder models (e.g., PLBART, CoTexT, and CodeT5). Our results show that leveraging CodeT5 under the PTM4Tag+ framework achieves the best performance among the eight considered PTMs and outperforms the state-of-the-art Convolutional Neural Network-based approach by a substantial margin in terms of average Precision@k, Recall@k, and F1-score@k (k ranges from 1 to 5). Specifically, CodeT5 improves the performance of F1-score@1-5 by 8.8%, 12.4%, 15.3%, 16.4%, and 16.6%, respectively. Moreover, to address the concern with inference latency, we experimented PTM4Tag+ using smaller PTM models (i.e., DistilBERT, DistilRoBERTa, CodeBERT-small, and CodeT5-small). We find that although smaller PTMs cannot outperform larger PTMs, they still maintain over 93.96% of the performance on average while reducing the mean inference time by more than 47.2%.

关键词： Tag recommendation stack overflow Pre-trained models Transformer

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API comparison based on the non-functional information mined from stack overflow

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SCIENCE OF COMPUTER PROGRAMMING 2025年 241卷

作者： Chen, Zhiqi Liu, Yuzhou Liu, Lei Liu, Huaxiao Li, Ren Zhang, Peng Jilin Univ Coll Comp Sci & Technol Changchun Peoples R China Jilin Univ Coll Software Changchun Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun Peoples R China

When comparing similar APIs, developers tend to distinguish them from the aspects of functional details. At the same time, some important non-functional factors (such as performance, usability, and security) may be ignored or noticed after using the API in the project. This may result in unnecessary errors or extra costs. API-related questions are common on stack overflow, and they can give a well-rounded picture of the APIs. This provides us with a rich resource for API comparison. However, although many methods are offered for mining Questions and Answers (Q&As) automatically, they often suffer from two main problems: 1) they only focus on the functional information of APIs;2) they analyze each text in isolation but ignore the correlations among them. In this paper, we propose an approach based on the pre-training model BERT to mine the non-functional information of APIs from stack overflow: we first tease out the correlations among questions, answers as well as corresponding reviews, so that one Q&A can be analyzed as a whole;then, an information extraction model is constructed by fine-tuning BERT with three subtasks-entity identification, aspect classification, and sentiment analysis separately, and we use it to mine the texts in Q&As step by step;finally, we summarize and visualize the results in a user-friendly way, so that developers can understand the information intuitively at the beginning of API selection. We evaluate our approach on 4,456 Q&As collected from stack overflow. The results show our approach can identify the correlations among reviews with 90.1% precision, and such information can improve the performance of the data mining process. In addition, the survey on maturers and novices indicates the understandability and helpfulness of our method. Moreover, compared with language models, our method can provide more intuitive and brief information for API comparison in non-functional aspects.

关键词： API comparison Non-functional information stack overflow Review analysis

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Representation Learning for stack overflow Posts: How Far Are We?

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2024年第3期33卷 1-24页

作者： He, Junda Zhou, Xin Xu, Bowen Zhang, Ting Kim, Kisub Yang, Zhou Thung, Ferdian Irsan, Ivana Clairine Lo, David Singapore Management Univ Sch Comp & Informat Syst 80 Stamford Rd Singapore 178902 Singapore North Carolina State Univ Dept Comp Sci Coll Engn Raleigh NC 27695 USA

The tremendous success of stack overflow has accumulated an extensive corpus of software engineering knowledge, thus motivating researchers to propose various solutions for analyzing its content. The performance of such solutions hinges significantly on the selection of representation models for stack overflow posts. As the volume of literature on stack overflow continues to burgeon, it highlights the need for a powerful stack overflow post representation model and drives researchers' interest in developing specialized representation models that can adeptly capture the intricacies of stack overflow posts. The state-of-the-art (SOTA) stack overflow post representation models are Post2Vec and BERToverflow, which are built upon neural networks such as convolutional neural network and transformer architecture (e.g., BERT). Despite their promising results, these representation methods have not been evaluated in the same experimental setting. To fill the research gap, we first empirically compare the performance of the representation models designed specifically for stack overflow posts (Post2Vec and BERToverflow) in a wide range of related tasks (i.e., tag recommendation, relatedness prediction, and API recommendation). The results show that Post2Vec cannot further improve the SOTA techniques of the considered downstream tasks, and BERToverflow shows surprisingly poor performance. To find more suitable representation models for the posts, we further explore a diverse set of transformer-based models, including (1) general domain language models (RoBERTa, Longformer, and GPT2) and (2) language models built with software engineering related textual artifacts (CodeBERT, GraphCodeBERT, seBERT, CodeT5, PLBart, and CodeGen). This exploration shows that models like CodeBERT and RoBERTa are suitable for representing stack overflow posts. However, it also illustrates the "No Silver Bullet" concept, as none of the models consistently wins against all the others. Inspired by the fin

关键词： stack overflow transformers pre-trained models

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Understanding developer challenges and trends in web accessibility: a stack overflow analysis

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UNIVERSAL ACCESS IN THE INFORMATION SOCIETY 2024年 1-17页

作者： Alghamdi, Asmaa Mansour Aljedaani, Wajdi Jalali, Hamed Ludi, Stephanie Eler, Marcelo M. Univ North Texas Denton TX 76205 USA

Many standards and guidelines are available to promote the accessibility of digital products and services, yet developers may still struggle to comprehend how they can be applied. In this study, we investigate stack overflow posts in order to comprehend the challenges developers confront in the context of web accessibility. Our analysis involved manually labeling 5,092 posts associated with web accessibility to ensure the credibility and consistency of our results. In addition, a formal open-coding approach was employed to generate a taxonomy that delineates the various aspects developers discuss concerning accessibility in web applications. Our analysis revealed that accessibility posts had increased significantly through the years and that approximately 18.36% of the accessibility issues were associated with screen reader behavior. Our study also indicates that most discussions are a call to assistance to improve their application accessibility for visual impairments. The analysis of developers' queries on stack overflow reveals several distinct trends. These findings provide a more comprehensive understanding of the challenges that developers face when it comes to web accessibility.

关键词： Accessibility Empirical Study stack overflow Universal Design

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Reproducibility of issues reported in stack overflow questions: Challenges, impact & estimation

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JOURNAL OF SYSTEMS AND SOFTWARE 2024年 217卷

作者： Mondal, Saikat Roy, Banani Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada

Software developers often submit questions to technical Q&A sites like stack overflow (SO) to resolve codelevel problems. In practice, they include example code snippets with questions to explain the programming issues. Existing research suggests that users attempt to reproduce the reported issues using given code snippets when answering questions. Unfortunately, such code snippets could not always reproduce the issues due to several unmet challenges that prevent questions from receiving appropriate and prompt solutions. One previous study investigated reproducibility challenges discussed in 400 Java questions and produced a catalog of them (e.g., too short code snippets). However, it is unknown how the practitioners (i.e., SO users) perceive this challenge catalog. Practitioners' perspectives are inevitable in validating these challenges and estimating their severity. In this study, we first surveyed 53 practitioners to understand their perspectives on reproducibility challenges. We attempt to (a) see whether they agree with these challenges, (b) determine the impact of each challenge on answering questions, and (c) identify the need for tools to promote reproducibility. Survey results show that - (a) about 90% of the participants agree with the challenges, (b) "missing an important part of code"most severely hurt reproducibility, and (c) participants strongly recommend introducing automated tool support to promote reproducibility. Second, we extract nine code-based features (e.g., LOC, compilability) and build five Machine Learning (ML) models to predict issue reproducibility. Early detection might help users improve code snippets and their reproducibility. Our models achieve 84.5% precision, 83.0% recall, 82.8% F1-score, and 82.8% overall accuracy, which are highly promising. We also validate the effectiveness of our features by predicting the reproducibility status of C# code snippets. Third, we systematically interpret the ML model and explain how code snippets

关键词： stack overflow Issue reproducibility Code snippets Reproducibility challenges User study

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What is discussed about Flutter on stack overflow (SO) question-and-answer (Q&A) website: An empirical study*

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JOURNAL OF SYSTEMS AND SOFTWARE 2024年 215卷

作者： Alanazi, Afit Alfayez, Reem King Saud Univ Coll Comp & Informat Sci Riyadh Saudi Arabia

Context: Mobile applications are growing in prevalence. Cross-platform application is one of the main challenges within mobile application development. Aiming to facilitate the process of mobile application development, the software engineering community developed multiple solutions to support cross-platform application development. Flutter is a recent cross-platform software development kit (SDK) that has been rising in popularity. Objective: This study aims to gain a better understanding of Flutter stance on stack overflow (SO). Method: The study identified and analyzed 176,876 Flutter-related questions to understand the interest towards Flutter. The study utilized Latent Dirichlet Allocation (LDA) to identify Flutter-related topics discussed within the identified questions. Subsequently, a number of heuristics were utilized to gauge the popularity and difficulty of topics. Results: The study revealed that interest towards Flutter was steadily increasing on SO until it dropped in 2023 and SO users discuss 12 Flutter-related topics, with the topic of setup and build automation being the most popular and most difficult. Conclusion: This study highlights the interest towards Flutter on SO, identifies Flutter-related topics discussed on the website, and assess the topics popularity and difficulty. Software researchers, practitioners, educators, and Flutter contributors may utilize the results to steer their future Flutter-related efforts.

关键词： stack overflow Flutter Mobile development Topic modeling Empirical study

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