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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Bug characteristics in open source software
Lin Tan, Chen Liu, LI Zhen-min, et al.
Empirical Software Engineering (2013) Vol. 19, Iss. 6, pp. 1665-1705
Closed Access | Times Cited: 212

Showing 1-25 of 212 citing articles:

A large scale study of programming languages and code quality in github
Baishakhi Ray, Daryl Posnett, Vladimir Filkov, et al.
(2014)
Closed Access | Times Cited: 356
Detecting missing information in bug descriptions
Oscar Chaparro, Jing Lü, Fiorella Zampetti, et al.
(2017), pp. 396-407
Closed Access | Times Cited: 130
An empirical study of bugs in test code
Arash Vahabzadeh, Amin Milani Fard, Ali Mesbah
(2015), pp. 101-110
Closed Access | Times Cited: 100
Not all bugs are the same: Understanding, characterizing, and classifying bug types
Gemma Catolino, Fabio Palomba, Andy Zaidman, et al.
Journal of Systems and Software (2019) Vol. 152, pp. 165-181
Open Access | Times Cited: 97
Bug Characteristics in Blockchain Systems: A Large-Scale Empirical Study
Zhiyuan Wan, David Lo, Xin Xia, et al.
(2017), pp. 413-424
Open Access | Times Cited: 88
IoT Bugs and Development Challenges
Amir Makhshari, Ali Mesbah
(2021), pp. 460-472
Closed Access | Times Cited: 60
Toward Understanding Deep Learning Framework Bugs
Junjie Chen, Yihua Liang, Qingchao Shen, et al.
ACM Transactions on Software Engineering and Methodology (2023) Vol. 32, Iss. 6, pp. 1-31
Open Access | Times Cited: 32
Nuances are the Key: Unlocking ChatGPT to Find Failure-Inducing Tests with Differential Prompting
Tsz-On Li, Wenxi Zong, Yibo Wang, et al.
2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) (2023), pp. 14-26
Open Access | Times Cited: 23
Bugs in large language models generated code: an empirical study
Florian Tambon, Arghavan Moradidakhel, Amin Nikanjam, et al.
Empirical Software Engineering (2025) Vol. 30, Iss. 3
Open Access | Times Cited: 1
How Practitioners Perceive Automated Bug Report Management Techniques
Weiqin Zou, David Lo, Zhenyu Chen, et al.
IEEE Transactions on Software Engineering (2018) Vol. 46, Iss. 8, pp. 836-862
Open Access | Times Cited: 82
Discovering bug patterns in JavaScript
Quinn Hanam, Fernando Santos de Mattos Brito, Ali Mesbah
(2016), pp. 144-156
Closed Access | Times Cited: 78
A large-scale study of programming languages and code quality in GitHub
Baishakhi Ray, Daryl Posnett, Prémkumar Dévanbu, et al.
Communications of the ACM (2017) Vol. 60, Iss. 10, pp. 91-100
Closed Access | Times Cited: 67
How bugs are born: a model to identify how bugs are introduced in software components
Gema Rodríguez-Pérez, Gregório Robles, Alexander Serebrenik, et al.
Empirical Software Engineering (2020) Vol. 25, Iss. 2, pp. 1294-1340
Open Access | Times Cited: 55
The symptoms, causes, and repairs of bugs inside a deep learning library
Li Jia, Hao Zhong, Xiaoyin Wang, et al.
Journal of Systems and Software (2021) Vol. 177, pp. 110935-110935
Closed Access | Times Cited: 42
Predictive Models in Software Engineering: Challenges and Opportunities
Yanming Yang, Xin Xia, David Lo, et al.
ACM Transactions on Software Engineering and Methodology (2022) Vol. 31, Iss. 3, pp. 1-72
Open Access | Times Cited: 35
Gang of eight
Akond Rahman, Effat Farhana, Chris Parnin, et al.
(2020), pp. 752-764
Closed Access | Times Cited: 46
Committed to Trust: A Qualitative Study on Security & Trust in Open Source Software Projects
Dominik Wermke, Noah Wöhler, Jan H. Klemmer, et al.
2022 IEEE Symposium on Security and Privacy (SP) (2022), pp. 1880-1896
Closed Access | Times Cited: 25
An Empirical Study of Functional Bugs in Android Apps
Yiheng Xiong, Mengqian Xu, Ting Su, et al.
(2023), pp. 1319-1331
Closed Access | Times Cited: 15
How do bugs surface? A comprehensive study on the characteristics of software bugs manifestation
Domenico Cotroneo, Roberto Pietrantuono, Stefano Russo, et al.
Journal of Systems and Software (2015) Vol. 113, pp. 27-43
Closed Access | Times Cited: 48
Is spreadsheet ambiguity harmful? detecting and repairing spreadsheet smells due to ambiguous computation
Wensheng Dou, Shing-Chi Cheung, Jun Wei
Proceedings of the 44th International Conference on Software Engineering (2014), pp. 848-858
Closed Access | Times Cited: 47
Improving software bug-specific named entity recognition with deep neural network
Cheng Zhou, Bin Li, Xiaobing Sun
Journal of Systems and Software (2020) Vol. 165, pp. 110572-110572
Closed Access | Times Cited: 38
An Empirical Study on Bugs Inside TensorFlow
Jia Li, Hao Zhong, Xiaoyin Wang, et al.
Lecture notes in computer science (2020), pp. 604-620
Closed Access | Times Cited: 38
An exploratory study of autopilot software bugs in unmanned aerial vehicles
Dinghua Wang, Shuqing Li, Guanping Xiao, et al.
(2021)
Closed Access | Times Cited: 32
Context-Sensitive and Directional Concurrency Fuzzing for Data-Race Detection
Zuming Jiang, Jia-Ju Bai, Kangjie Lu, et al.
(2022)
Open Access | Times Cited: 22
A comprehensive empirical study on bug characteristics of deep learning frameworks
Yilin Yang, Tianxing He, Zhilong Xia, et al.
Information and Software Technology (2022) Vol. 151, pp. 107004-107004
Closed Access | Times Cited: 20
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