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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.
Bosco, M., Cavoto, P., Ungolo, A., Muse, B. A., Khomh, F., Nardone, V., & Di Penta, M. UnityLint: A Bad Smell Detector for Unity [Paper]. 2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC 2023), Melbourne, Australia. External link
Ikama, A., Du, V., Belias, P., Muse, B. A., Khomh, F., & Hamdaqa, M. (2022, October). Revisiting the Impact of Anti-patterns on Fault-Proneness: A Differentiated Replication [Paper]. 22nd IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2022), Limassol, Cyprus. External link
Jebnoun, H., Rahman, M. S., Khomh, F., & Muse, B. A. (2022). Clones in deep learning code: what, where, and why? Empirical Software Engineering, 27(4). External link
Muse, B. A., Nafi, K. W., Khomh, F., & Antoniol, G. (2024). Data-access performance anti-patterns in data-intensive systems. Empirical Software Engineering, 29, 144 (35 pages). External link
Muse, B. A., Khomh, F., & Antoniol, G. (2023). Refactoring practices in the context of data-intensive systems. Empirical Software Engineering, 28(2), 46 (66 pages). External link
Muse, B. A. (2022). Data-Access Technical Debt: Specification, Refactoring, and Impact Analysis [Ph.D. thesis, Polytechnique Montréal]. Available
Muse, B. A., Khomh, F., & Antoniol, G. (2022, March). Do developers refactor data access code? An empirical study [Paper]. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2022), Honolulu, HI, USA. External link
Muse, B. A., Nagy, C., Cleve, A., Khomh, F., & Antoniol, G. (2022). FIXME: synchronize with database! An empirical study of data access self-admitted technical debt. Empirical Software Engineering, 27(6), 42 pages. External link
Muse, B. A., Rahman, M. M., Nagy, C., Cleve, A., Khomh, F., & Antoniol, G. (2020, June). On the prevalence, impact and evolution of SQL Code smells in data-intensive systems [Paper]. 17th International Conference on Mining Software Repositories (MSR 2020), Seoul, Republic of Korea. External link
Nardone, V., Muse, B. A., Abidi, M., Khomh, F., & Di Penta, M. (2023). Video Game Bad Smells: What They Are and How Developers Perceive Them. ACM Transactions on Software Engineering and Methodology, 32(4), 1-35. External link
