Bounties on technical Q&amp;A sites: a case study of Stack Overflow bounties

Zhou, Jiayuan; Wang, Shaowei; Bezemer, Cor‐Paul; Hassan, Ahmed E.

doi:10.1007/s10664-019-09744-3

Cited by 29 publications

(11 citation statements)

References 35 publications

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“…However, we note that many uncontrollable factors may be at play when it comes to what is causing the communities’ decline and Q&A website administrators should also act towards improving the websites and providing a better environment for nurturing a community. While we cannot infer causality and provide fail-proof advice, our findings also hint at actions that can help in lifting these communities, such as maintaining an active staff of moderators and investing in gamification features to motivate users’ participation (Anderson et al 2012 ; Cavusoglu et al 2015 ; Zhou et al 2020 ; Santos et al 2020 ; Wang 2020 ; Deng et al 2019 ).…”

Section: Implications Of Our Findingsmentioning

confidence: 82%

“…Studies have also investigated methods of improving Stack Overflow, such as helping users find the information (Nadi and Treude 2020 ; Zhang et al 2019 ), identifying expert users and using their knowledge to help the community (Ford et al 2018 ), and increasing user contribution using symbolic rewards, such badges and bounties (Cavusoglu et al 2015 ; Zhou et al 2020 ).…”

Section: Background and Related Workmentioning

confidence: 99%

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An empirical study of Q&A websites for game developers

Kamienski

Bezemer

2021

Empir Software Eng

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The game development industry is growing, and training new developers in game development-specific abilities is essential to satisfying its need for skilled game developers. These developers require effective learning resources to acquire the information they need and improve their game development skills. Question and Answer (Q&A) websites stand out as some of the most used online learning resources in software development. Many studies have investigated how Q&A websites help software developers become more experienced. However, no studies have explored Q&A websites aimed at game development, and there is little information about how game developers use and interact with these websites. In this paper, we study four Q&A communities by analyzing game development data we collected from their websites and the 347 responses received on a survey we ran with game developers. We observe that the communities have declined over the past few years and identify factors that correlate to these changes. Using a Latent Dirichlet Allocation (LDA) model, we characterize the topics discussed in the communities. We also analyze how topics differ across communities and identify the most discussed topics. Furthermore, we find that survey respondents have a mostly negative view of the communities and tended to stop using the websites once they became more experienced. Finally, we provide recommendations on where game developers should post their questions, which can help mitigate the websites’ declines and improve their effectiveness.

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Section: Implications Of Our Findingsmentioning

confidence: 82%

Section: Background and Related Workmentioning

confidence: 99%

An empirical study of Q&A websites for game developers

Kamienski

Bezemer

2021

Empir Software Eng

Self Cite

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“…Zhou et al found that questions with bounties were answered in most cases [16]. Profir-Petru et al proposed the POSIT method, which used abstract syntax tree tags from the CLANG compiler and part-of-speech tags from the Standard Stanford part-of-speech tagger, to simultaneously tag natural and programming languages in questions [17].…”

Section: Other Research On Stack Overflowmentioning

confidence: 99%

Hot question prediction in Stack Overflow

2021

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Stack Overflow is a very popular programming question and answer community. Some questions become hot, and receive high views, which are of widespread concern to developers. Finding hot questions early can give priority to recommend potential hot questions to answers, thereby shortening the response time. Besides, the hot question prediction is also helpful for making advertising plan, planning advertising campaigns and estimating costs. Therefore, it is important to predict hot questions. The authors propose the VSAF method which analyses the View amount changes, Answer amount changes and Score changes soon after questions' creation based on Fully convolutional neural network. The performance of the VSAF method based on a training set and two different test sets has been evaluated. The training set has 1600 hot questions and 1600 cold questions. The random test set has 381 hot questions and 2819 cold questions, while the balanced test set has 400 hot questions and 400 cold questions. The experimental results show that using the balanced test set, VSAF achieves Accuracy, F1 hot and F1 cold of 80%, 77.77% and 81.81%, which outperforms the baseline approach by 25.59%, 21.52% and 29.04%, respectively. Using the random test set for evaluation, VSAF achieves Accuracy, F1 hot and F1 cold of 84.91%, 53.96% and 90.97%, which outperforms the baseline approach by 31.83%, 84.16% and 19.35%, respectively. The VSAF method significantly outperforms the state-of-the-art approach on hot question prediction. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Prior research investigated how developers leverage Stack Overflow and studied different mechanisms aiming to improve the design of Stack Overflow [8,[31][32][33][34]. Treude et al [7] categorized the types of questions on Stack Overflow, and observed that Stack Overflow can be useful for code review and learning the concepts of programming.…”

Section: Understanding and Improving Stack Overflowmentioning

confidence: 99%

“…An automated system to identify the quality of posts and filter low-quality content was proposed by Ponzanelli et al [18]. To improve the quality of the crowdsourced knowledge on Stack Overflow, prior studies aimed to identify artifacts with different properties [24,33,[36][37][38][39][40]. For example, Nasehi et al [41] examined code examples on Stack Overflow and identified characteristics of effective code examples.…”

Section: Understanding and Improving Stack Overflowmentioning

confidence: 99%

An empirical study of question discussions on Stack Overflow

Zhu¹,

Zhang²,

Hassan³

et al. 2021

Preprint

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Stack Overflow provides a means for developers to exchange knowledge. While much previous research on Stack Overflow has focused on questions and answers (Q&A), recent work has shown that discussions in comments also contain rich information. On Stack Overflow, discussions through comments and chat rooms can be tied to questions or answers. In this paper, we conduct an empirical study that focuses on the nature of question discussions. We observe that: (1) Question discussions occur at all phases of the Q&A process, with most beginning before the first answer is received.(2) Both askers and answerers actively participate in question discussions; the likelihood of their participation increases as the number of comments increases.(3) There is a strong correlation between the number of question comments and the question answering time (i.e., more discussed questions receive answers more slowly); also, questions with a small number of comments are likely to be answered more quickly than questions with no discussion. Our findings suggest that question discussions contain a rich trove of data that is integral to the Q&A processes on Stack Overflow. We further suggest how future research

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Bounties on technical Q&A sites: a case study of Stack Overflow bounties

Cited by 29 publications

References 35 publications

An empirical study of Q&A websites for game developers

An empirical study of Q&A websites for game developers

Hot question prediction in Stack Overflow

An empirical study of question discussions on Stack Overflow

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