Improving Low Quality Stack Overflow Post Detection

Ponzanelli, Luca; Mocci, Andrea; Bacchelli, Alberto; Lanza, Michele; Fullerton, David A.

doi:10.1109/icsme.2014.90

Cited by 120 publications

(49 citation statements)

References 7 publications

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“…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]. Ponzanelli et al also built a classification model to identify high-quality and low-quality questions as soon as questions are posted [38]. Yao et al found that the quality of an answer is highly associated with that of its question [39].…”

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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“…Most previous work in mining community Q&A sites has focused on: (i) assessing the quality of questions and answers (Ponzanelli et al 2014;Xia et al 2016;Roy et al 2017); (ii) understanding how software developers interact with each other on Q&A sites (Treude et al 2011); (iii) providing empirical evidence on how to write good questions and answers (Bosu et al 2013;Calefato et al 2018); the impact of sentiment on getting an answer accepted (Calefato et al 2015); (iv) the role played by social cues on the perceived quality of an answer (Hart and Sarma 2014); (v) the topics discussed by developers (Bajaj et al 2014;Barua et al 2012); (vi) retrieving semantically linked questions (Xu et al 2016a;Xu et al 2016b); and (vii) summarizing answers (Xu et al 2017). Table 15 summarizes the prior work reviewed next, which is strictly related to best-answer prediction for technical help requests.…”

Section: Related Workmentioning

confidence: 99%

An empirical assessment of best-answer prediction models in technical Q&A sites

2018

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Technical Q&A sites have become essential for software engineers as they constantly seek help from other experts to solve their work problems. Despite their success, many questions remain unresolved, sometimes because the asker does not acknowledge any helpful answer. In these cases, an information seeker can only browse all the answers within a question thread to assess their quality as potential solutions. We approach this time-consuming problem as a binary-classification task where a best-answer prediction model is built to identify the accepted answer among those within a resolved question thread, and the candidate solutions to those questions that have received answers but are still unresolved. In this paper, we report on a study aimed at assessing 26 best-answer prediction models in two steps. First, we study how models perform when predicting best answers in Stack Overflow, the most popular Q&A site for software engineers. Then, we assess performance in a cross-platform setting where the prediction models are trained on Stack Overflow and tested on other technical Q&A sites. Our findings show that the choice of the classifier and automatied parameter tuning have a large impact on the prediction of the best answer. We also demonstrate that our approach to the bestanswer prediction problem is generalizable across technical Q&A sites. Finally, we provide practical recommendations to Q&A platform designers to curate and preserve the crowdsourced knowledge shared through these sites.

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“…Sridhara et al [30] generate descriptive summary comments for Java methods. Besides those work, some studies analyze the project source code and apply its abstract syntax tree information to code or document search [31][32][33][34][35][36][37]. In this paper, we use 7 kinds of features to represent our learning example and train our classifier.…”

Section: Related Workmentioning

confidence: 99%

Toward accurate link between code and software documentation

Cao

Zou

Luo

et al. 2018

Sci. China Inf. Sci.

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Recovering traceability links between source code and software documentation is an important research topic in software maintenance and software reuse. There have been a lot of research efforts in recovering traceability between documentation and code elements (class, interface, method, etc.), mostly based on program analysis. However, there are still a lot of noise links being established in existing work. In this paper, we propose a novel approach to classifying code elements, occurring in a document, into contextual code elements and salient code elements. As a result, we can filter the noise traceability links between a software document and its contextual code elements and get a higher quality link set. Our classifier is trained based on open source project Lucene's source code and 1899 StackOverflow answer documents about Lucene. We extract code elements from these documents and represent each of these code elements with a 7-dimension feature vector, then we use a decision-tree-based learning model to classify them as salient or not. In the experiments, we get a precision of 70.7% in recognizing the salient code elements of these documents and get 12% improvement compared with Rigby's work. We can filter out 56.5%∼69.3% noise traceability links with different thresholds in our classifier. It can improve the quality of traceability links between source code and their related software documents in application.

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Improving Low Quality Stack Overflow Post Detection

Cited by 120 publications

References 7 publications

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

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

An empirical assessment of best-answer prediction models in technical Q&A sites

Toward accurate link between code and software documentation

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