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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:

An extensive evaluation of ensemble techniques for software change prediction
Gemma Catolino, Filomena Ferrucci
Journal of Software Evolution and Process (2019) Vol. 31, Iss. 9
Closed Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

Towards Pattern-Based Change Verification Framework for Cloud-Enabled Healthcare Component-Based
Sadia Ali, Yaser Hafeez, N. Z. Jhanjhi, et al.
IEEE Access (2020) Vol. 8, pp. 148007-148020
Open Access | Times Cited: 71
Machine learning-based test smell detection
Valeria Pontillo, Dario Amoroso d’Aragona, Fabiano Pecorelli, et al.
Empirical Software Engineering (2024) Vol. 29, Iss. 2
Open Access | Times Cited: 7
Improving change prediction models with code smell-related information
Gemma Catolino, Fabio Palomba, Francesca Arcelli Fontana, et al.
Empirical Software Engineering (2019) Vol. 25, Iss. 1, pp. 49-95
Closed Access | Times Cited: 44
A critical review on blockchain assessment initiatives: A technology evolution viewpoint
Ricardo Colomo‐Palacios, Mary Sánchez‐Gordón, Daniel Arias Aranda
Journal of Software Evolution and Process (2020) Vol. 32, Iss. 11
Open Access | Times Cited: 40
An exploratory study on confusion in code reviews
Felipe Ebert, Fernando Castor, Nicole Novielli, et al.
Empirical Software Engineering (2021) Vol. 26, Iss. 1
Open Access | Times Cited: 35
A critical comparison on six static analysis tools: Detection, agreement, and precision
Valentina Lenarduzzi, Fabiano Pecorelli, Nyyti Saarimäki, et al.
Journal of Systems and Software (2022) Vol. 198, pp. 111575-111575
Open Access | Times Cited: 19
Image-based insect species and gender classification by trained supervised machine learning algorithms
Midori Tuda, Alejandro Isabel Luna-Maldonado
Ecological Informatics (2020) Vol. 60, pp. 101135-101135
Closed Access | Times Cited: 28
Competencies for Code Review
Pavlína Wurzel Gonçalves, Gül Çalıklı, Alexander Serebrenik, et al.
Proceedings of the ACM on Human-Computer Interaction (2023) Vol. 7, Iss. CSCW1, pp. 1-33
Open Access | Times Cited: 9
IoT Framework for Smart Irrigation using Machine Learning Technique
S. Ramya, A.M. Swetha, Manivannan Doraipandian
Journal of Computer Science (2020) Vol. 16, Iss. 3, pp. 355-363
Open Access | Times Cited: 19
An Empirical Study of Quick Remedy Commits
Fengcai Wen, Csaba Nagy, Michele Lanza, et al.
(2020), pp. 60-71
Closed Access | Times Cited: 16
A Critical Comparison on Six Static Analysis Tools: Detection Agreement and Precision
Valentina Lenarduzzi, Fabiano Pecorelli, Nyyti Saarimäki, et al.
SSRN Electronic Journal (2022)
Open Access | Times Cited: 8
Simplified abstract syntax tree based semantic features learning for software change prediction
Xinyue Yang, Xiaofang Zhang, Yao Tong
Journal of Software Evolution and Process (2022) Vol. 34, Iss. 4
Closed Access | Times Cited: 5
The Impact of Ensemble Techniques on Software Maintenance Change Prediction: An Empirical Study
Hadeel Alsolai, Marc Roper
Applied Sciences (2022) Vol. 12, Iss. 10, pp. 5234-5234
Open Access | Times Cited: 5
Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques
Rasmita Panigrahi, Sanjay Kumar Kuanar, Sanjay Misra, et al.
Applied Sciences (2022) Vol. 12, Iss. 23, pp. 12217-12217
Open Access | Times Cited: 5
Quick remedy commits and their impact on mining software repositories
Fengcai Wen, Csaba Nagy, Michele Lanza, et al.
Empirical Software Engineering (2021) Vol. 27, Iss. 1
Open Access | Times Cited: 6
Software change‐proneness prediction based on deep learning
Xiaoyan Zhu, Nan Li, Yong Wang
Journal of Software Evolution and Process (2022) Vol. 34, Iss. 4
Closed Access | Times Cited: 3
Machine Learning for Change-Prone Class Prediction: A History-Based Approach
Rogério de Carvalho Silva, Paulo Roberto Farah, Sílvia Regina Vergílio
(2022), pp. 289-298
Closed Access | Times Cited: 3
An assessment of heterogenous ensemble classifiers for analyzing change‐proneness in open‐source software systems
Megha Khanna, Ankita Bansal
Journal of Software Evolution and Process (2024) Vol. 36, Iss. 8
Closed Access
Predicting Change-Proneness with a Deep Learning Model: Incorporating Structural Dependencies
Yamin Hu, Hao Jiang
2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) (2024), pp. 1218-1229
Closed Access
On the use of Change History Data to Enhance Class Change-Proneness Prediction Models
Rogério de Carvalho Silva, Paulo Roberto Farah, Sílvia Regina Vergílio
Journal of the Brazilian Computer Society (2024) Vol. 30, Iss. 1, pp. 421-443
Open Access
Comparative Study of Feature Reduction Techniques in Software Change Prediction
Ruchika Malhotra, Ritvik Kapoor, Deepti Aggarwal, et al.
(2021), pp. 18-28
Closed Access | Times Cited: 4
Enhancing Information Retrieval System Using Change-Prone Classes
Deepa Bura, Amit Choudhary
Advances in library and information science (ALIS) book series (2019), pp. 40-68
Closed Access | Times Cited: 3
Cross-Project Change-Proneness Prediction with Selected Source Project
Ankita Bansal, Viraj Madaan, Rahul Gaur, et al.
(2022), pp. 1-5
Closed Access | Times Cited: 2
Quantitative Software Change Prediction in Open Source Web Projects Using Time Series Forecasting
Wasiur Rhmann
International Journal of Open Source Software and Processes (2021) Vol. 12, Iss. 2, pp. 36-51
Closed Access | Times Cited: 1
An Assessment of Machine Learning Algorithms and Models for Prediction of Change-Prone Java Methods
Paulo Roberto Farah, Rogério de Carvalho Silva, Sílvia Regina Vergílio
(2023), pp. 322-331
Closed Access
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