Identifying Textual Features of High-Quality Questions: An Empirical Study on Stack Overflow

Qing Mi; Yujin Gao; Jacky Keung; Yan Xiao; Solomon Mensah

doi:10.1109/APSEC.2017.77

Identifying Textual Features of High-Quality Questions: An Empirical Study on Stack Overflow

Qing Mi, Yujin Gao^*, Jacky Keung, Yan Xiao, Solomon Mensah

^*Corresponding author for this work

School of Computer Science and Technology

City University of Hong Kong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

Background: Stack Overflow (SO) is a programming-specific Q&A website that serves as a valuable repository of software engineering knowledge. For SO members, formulating a good question is the first step towards eliciting satisfactory responses. Aims: To guide SO members on how to make a good question, we conduct an empirical study using the publicly available Stack Overflow Data Dump for the period of 2008-2016. Method: We first choose 25 features along 5 dimensions to represent the textual characteristics that we are interested in. Making use of the Boruta algorithm, we then capture all features that are either strongly or weakly relevant to the question quality. Results: The results show that the number of tags and code snippets are the most discriminative features, whereas there is only a weak correlation between the question quality and the sentiment-related factors. Based on the empirical evidence, we provide useful and usable suggestions to SO members on how to optimize their questions. Conclusions: We consider that our findings will provide SO members with a better understanding of the patterns behind high-quality questions, this is to support effective and efficient utilization of Q&A websites as the ultimate goal.

Original language	English
Title of host publication	Proceedings - 24th Asia-Pacific Software Engineering Conference, APSEC 2017
Editors	Jian Lv, He Zhang, Mike Hinchey, Xiao Liu
Publisher	IEEE Computer Society
Pages	636-641
Number of pages	6
ISBN (Electronic)	9781538636817
DOIs	https://doi.org/10.1109/APSEC.2017.77
Publication status	Published - 2 Jul 2017
Event	24th Asia-Pacific Software Engineering Conference, APSEC 2017 - Nanjing, Jiangsu, China Duration: 4 Dec 2017 → 8 Dec 2017

Publication series

Name	Proceedings - Asia-Pacific Software Engineering Conference, APSEC
Volume	2017-December
ISSN (Print)	1530-1362

Conference

Conference	24th Asia-Pacific Software Engineering Conference, APSEC 2017
Country/Territory	China
City	Nanjing, Jiangsu
Period	4/12/17 → 8/12/17

Keywords

Boruta algorithm
Q&A website
Stack Overflow
empirical software engineering
textual feature

Access to Document

10.1109/APSEC.2017.77

Cite this

Mi, Q., Gao, Y., Keung, J., Xiao, Y., & Mensah, S. (2017). Identifying Textual Features of High-Quality Questions: An Empirical Study on Stack Overflow. In J. Lv, H. Zhang, M. Hinchey, & X. Liu (Eds.), Proceedings - 24th Asia-Pacific Software Engineering Conference, APSEC 2017 (pp. 636-641). (Proceedings - Asia-Pacific Software Engineering Conference, APSEC; Vol. 2017-December). IEEE Computer Society. https://doi.org/10.1109/APSEC.2017.77

Mi, Qing ; Gao, Yujin ; Keung, Jacky et al. / Identifying Textual Features of High-Quality Questions : An Empirical Study on Stack Overflow. Proceedings - 24th Asia-Pacific Software Engineering Conference, APSEC 2017. editor / Jian Lv ; He Zhang ; Mike Hinchey ; Xiao Liu. IEEE Computer Society, 2017. pp. 636-641 (Proceedings - Asia-Pacific Software Engineering Conference, APSEC).

@inproceedings{7e687a4583ba4952a66c135e4fe0f447,

title = "Identifying Textual Features of High-Quality Questions: An Empirical Study on Stack Overflow",

abstract = "Background: Stack Overflow (SO) is a programming-specific Q&A website that serves as a valuable repository of software engineering knowledge. For SO members, formulating a good question is the first step towards eliciting satisfactory responses. Aims: To guide SO members on how to make a good question, we conduct an empirical study using the publicly available Stack Overflow Data Dump for the period of 2008-2016. Method: We first choose 25 features along 5 dimensions to represent the textual characteristics that we are interested in. Making use of the Boruta algorithm, we then capture all features that are either strongly or weakly relevant to the question quality. Results: The results show that the number of tags and code snippets are the most discriminative features, whereas there is only a weak correlation between the question quality and the sentiment-related factors. Based on the empirical evidence, we provide useful and usable suggestions to SO members on how to optimize their questions. Conclusions: We consider that our findings will provide SO members with a better understanding of the patterns behind high-quality questions, this is to support effective and efficient utilization of Q&A websites as the ultimate goal.",

keywords = "Boruta algorithm, Q&A website, Stack Overflow, empirical software engineering, textual feature",

author = "Qing Mi and Yujin Gao and Jacky Keung and Yan Xiao and Solomon Mensah",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 24th Asia-Pacific Software Engineering Conference, APSEC 2017 ; Conference date: 04-12-2017 Through 08-12-2017",

year = "2017",

month = jul,

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doi = "10.1109/APSEC.2017.77",

language = "English",

series = "Proceedings - Asia-Pacific Software Engineering Conference, APSEC",

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Mi, Q, Gao, Y, Keung, J, Xiao, Y & Mensah, S 2017, Identifying Textual Features of High-Quality Questions: An Empirical Study on Stack Overflow. in J Lv, H Zhang, M Hinchey & X Liu (eds), Proceedings - 24th Asia-Pacific Software Engineering Conference, APSEC 2017. Proceedings - Asia-Pacific Software Engineering Conference, APSEC, vol. 2017-December, IEEE Computer Society, pp. 636-641, 24th Asia-Pacific Software Engineering Conference, APSEC 2017, Nanjing, Jiangsu, China, 4/12/17. https://doi.org/10.1109/APSEC.2017.77

Identifying Textual Features of High-Quality Questions: An Empirical Study on Stack Overflow. / Mi, Qing; Gao, Yujin; Keung, Jacky et al.
Proceedings - 24th Asia-Pacific Software Engineering Conference, APSEC 2017. ed. / Jian Lv; He Zhang; Mike Hinchey; Xiao Liu. IEEE Computer Society, 2017. p. 636-641 (Proceedings - Asia-Pacific Software Engineering Conference, APSEC; Vol. 2017-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Identifying Textual Features of High-Quality Questions

T2 - 24th Asia-Pacific Software Engineering Conference, APSEC 2017

AU - Mi, Qing

AU - Gao, Yujin

AU - Keung, Jacky

AU - Xiao, Yan

AU - Mensah, Solomon

PY - 2017/7/2

Y1 - 2017/7/2

N2 - Background: Stack Overflow (SO) is a programming-specific Q&A website that serves as a valuable repository of software engineering knowledge. For SO members, formulating a good question is the first step towards eliciting satisfactory responses. Aims: To guide SO members on how to make a good question, we conduct an empirical study using the publicly available Stack Overflow Data Dump for the period of 2008-2016. Method: We first choose 25 features along 5 dimensions to represent the textual characteristics that we are interested in. Making use of the Boruta algorithm, we then capture all features that are either strongly or weakly relevant to the question quality. Results: The results show that the number of tags and code snippets are the most discriminative features, whereas there is only a weak correlation between the question quality and the sentiment-related factors. Based on the empirical evidence, we provide useful and usable suggestions to SO members on how to optimize their questions. Conclusions: We consider that our findings will provide SO members with a better understanding of the patterns behind high-quality questions, this is to support effective and efficient utilization of Q&A websites as the ultimate goal.

AB - Background: Stack Overflow (SO) is a programming-specific Q&A website that serves as a valuable repository of software engineering knowledge. For SO members, formulating a good question is the first step towards eliciting satisfactory responses. Aims: To guide SO members on how to make a good question, we conduct an empirical study using the publicly available Stack Overflow Data Dump for the period of 2008-2016. Method: We first choose 25 features along 5 dimensions to represent the textual characteristics that we are interested in. Making use of the Boruta algorithm, we then capture all features that are either strongly or weakly relevant to the question quality. Results: The results show that the number of tags and code snippets are the most discriminative features, whereas there is only a weak correlation between the question quality and the sentiment-related factors. Based on the empirical evidence, we provide useful and usable suggestions to SO members on how to optimize their questions. Conclusions: We consider that our findings will provide SO members with a better understanding of the patterns behind high-quality questions, this is to support effective and efficient utilization of Q&A websites as the ultimate goal.

KW - Boruta algorithm

KW - Q&A website

KW - Stack Overflow

KW - empirical software engineering

KW - textual feature

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DO - 10.1109/APSEC.2017.77

M3 - Conference contribution

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EP - 641

BT - Proceedings - 24th Asia-Pacific Software Engineering Conference, APSEC 2017

A2 - Lv, Jian

A2 - Zhang, He

A2 - Hinchey, Mike

A2 - Liu, Xiao

PB - IEEE Computer Society

Y2 - 4 December 2017 through 8 December 2017

ER -

Mi Q, Gao Y, Keung J, Xiao Y, Mensah S. Identifying Textual Features of High-Quality Questions: An Empirical Study on Stack Overflow. In Lv J, Zhang H, Hinchey M, Liu X, editors, Proceedings - 24th Asia-Pacific Software Engineering Conference, APSEC 2017. IEEE Computer Society. 2017. p. 636-641. (Proceedings - Asia-Pacific Software Engineering Conference, APSEC). doi: 10.1109/APSEC.2017.77

Identifying Textual Features of High-Quality Questions: An Empirical Study on Stack Overflow

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