Understanding triggers for clarification requests in community-based software help forums

Hudson, Nathaniel; Chilana, Parmit K.; Guo, Xiaoyu; Day, Jason Allan; Liu, Edmund

doi:10.1109/vlhcc.2015.7357216

Cited by 6 publications

(1 citation statement)

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“…Hudson et al investigated the characteristics of questions for which clarification are requested (e.g., missing information, unclear goals, non-standard terminology) from helpers on Q&A websites. Hudson et al observed a large difference of such characteristics across different websites [37]. Ponzanelli et al performed an empirical study to understand the relationship between a set of proposed factors and the quality of a post on Stack Overflow [17].…”

Section: Understanding and Improving The Quality On Qanda Websitesmentioning

confidence: 99%

How Do Users Revise Answers on Technical Q&A Websites? A Case Study on Stack Overflow

Chen

Hassan

2020

IIEEE Trans. Software Eng.

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To ensure the quality of its shared knowledge, Stack Overflow encourages users to revise answers through a badge system, which is based on quantitative measures (e.g., a badge is awarded after revising more than 500 answers). Prior studies show that badges can positively steer the user behavior on Stack Overflow (e.g., increasing user participation). However, little is known whether revision-related badges have a negative impact on the quality of revisions since some studies show that certain users may game incentive systems to gain rewards. In this study, we analyze 3,871,966 revision records that are collected from 2,377,692 Stack Overflow answers. We find that: 1) Users performed a much larger than usual revisions on the badge-awarding days compared to normal days; 25% of the users did not make any more revisions once they received their first revision-related badge. 2) Performing more revisions than usual in a single day increased the likelihood of such revisions being rolled back (e.g., due to undesired or incorrect revisions). 3) Users were more likely to perform text and small revisions if they performed many revisions in a single day. Our findings are concurred by the Stack Overflow community, and they highlight the need for changes to the current badge system in order to provide a better balance between the quality and quantity of revisions.

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Section: Understanding and Improving The Quality On Qanda Websitesmentioning

confidence: 99%

How Do Users Revise Answers on Technical Q&A Websites? A Case Study on Stack Overflow

Chen

Hassan

2020

IIEEE Trans. Software Eng.

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Automatic prediction of rejected edits in Stack Overflow

2022

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The content quality of shared knowledge in Stack Overflow (SO) is crucial in supporting software developers with their programming problems. Thus, SO allows its users to suggest edits to improve the quality of a post (i.e., question and answer). However, existing research shows that many suggested edits in SO are rejected due to undesired contents/formats or violating edit guidelines. Such a scenario frustrates or demotivates users who would like to conduct good-quality edits. Therefore, our research focuses on assisting SO users by offering them suggestions on how to improve their editing of posts. First, we manually investigate 764 (382 questions + 382 answers) rejected edits by rollbacks and produce a catalog of 19 rejection reasons. Second, we extract 15 texts and user-based features to capture those rejection reasons. Third, we develop four machine learning models using those features. Our best-performing model can predict rejected edits with 69.1% precision, 71.2% recall, 70.1% F1-score, and 69.8% overall accuracy. Fourth, we introduce an online tool named EditEx that works with the SO edit system. EditEx can assist users while editing posts by suggesting the potential causes of rejections. We recruit 20 participants to assess the effectiveness of EditEx. Half of the participants (i.e., treatment group) use EditEx and another half (i.e., control group) use the SO standard edit system to edit posts. According to our experiment, EditEx can support SO standard edit system to prevent 49% of rejected edits, including the commonly rejected ones. However, it can prevent 12% rejections even in free-form regular edits. The treatment group finds the potential rejection reasons identified by EditEx influential . Furthermore, the median workload suggesting edits using EditEx is half compared to the SO edit system.

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