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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Abidi, M., Rahman, M.S., Openja, M., & Khomh, F. (2024). Design smells in multi-language systems and bug-proneness: a survival analysis. Empirical Software Engineering, 29, 106 (42 pages). External link
Abidi, M., Rahman, M. S., Openja, M., & Khomh, F. (2022). Multi-language design smells: a backstage perspective. Empirical Software Engineering, 27(5), 52 pages. External link
Abidi, M., Rahman, M. S., Openja, M., & Khomh, F. (2021). Are Multi-Language Design Smells Fault-Prone? An Empirical Study. ACM Transactions on Software Engineering and Methodology, 30(3), 1-56. External link
Abidi, M., Openja, M., & Khomh, F. (2020, June). Multi-language design smells : a backstage perspective [Paper]. 17th International Conference on Mining Software Repositories (MSR 2020), Seoul, Republic of Korea. External link
Businge, J., Openja, M., Nadi, S., & Berger, T. (2022). Reuse and maintenance practices among divergent forks in three software ecosystems. Empirical Software Engineering, 27(2), 54 (47 pages). External link
Businge, J., Openja, M., Kavaler, D., Bainomugisha, E., Khomh, F., & Filkov, V. (2019, February). Studying Android App Popularity by Cross-Linking GitHub and Google Play Store [Paper]. 26th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2019), Hangzhou, China. External link
El aoun, M. R., Li, H., Khomh, F., & Openja, M. (2021, September). Understanding Quantum Software Engineering Challenges An Empirical Study on Stack Exchange Forums and GitHub Issues [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2021), Luxembourg, Netherlands. External link
Majidi, F., Openja, M., Khomh, F., & Li, H. (2022, October). An Empirical Study on the Usage of Automated Machine Learning Tools [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. External link
Openja, M., Khomh, F., Foundjem, A., Jiang, Z. M., Abidi, M., & Hassan, A. E. (2024). An empirical study of testing machine learning in the wild. ACM Transactions on Software Engineering and Methodology. External link
Openja, M., Laberge, G., & Khomh, F. (2024). Detection and evaluation of bias-inducing features in machine learning. Empirical Software Engineering, 29(1), 71 pages. External link
Openja, M., Nikanjam, A., Yahmed, A. H., Khomh, F., & Jiang, Z. M. J. (2022, October). An Empirical Study of Challenges in Converting Deep Learning Models [Paper]. 39th IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. External link
Openja, M., Majidi, F., Khomh, F., Chembakottu, B., & Li, H. (2022, June). Studying the Practices of Deploying Machine Learning Projects on Docker [Paper]. 26th ACM International Conference on Evaluation and Assessment in Software Engineering (EASE 2022), Gothenburg, Sweden. External link
Openja, M. (2022). Studying the Practices of Deploying Machine Learning Projects on Docker [Dataset]. External link
Openja, M., Morovati, M. M., An, L., Khomh, F., & Abidi, M. (2022). Technical debts and faults in open-source quantum software systems: An empirical study. Journal of Systems and Software, 193, 28 pages. External link
Openja, M. (2021). An Empirical Study of Testing and Release Practices for Machine Learning Software Systems [Master's thesis, Polytechnique Montréal]. Available
Openja, M., Adams, B., & Khomh, F. (2020, September). Analysis of Modern Release Engineering Topics : – A Large-Scale Study using StackOverflow – [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2020), Adelaide, Australia. External link
Openja, M. (2020). Release Engineering Posts [Dataset]. External link
