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This graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
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A 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.
Jebnoun, H., Ben Braiek, H., Rahman, M. M., & Khomh, F. (2020, June). The scent of deep learning code : an empirical study [Paper]. 17th International Conference on Mining Software Repositories (MSR 2020), Seoul, Republic of Korea. External link
Khomh, F., Rahman, M. M., & Barbez, A. (2023). Intelligent Software Maintenance. In Romero, J. R., Medina-Bulo, I., & Chicano, F. (eds.), Optimising the Software Development Process with Artificial Intelligence (pp. 241-275). 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
Rahman, M. M., Khomh, F., & Castelluccio, M. (2022). Works for me! Cannot reproduce: A large scale empirical study of non-reproducible bugs. Empirical Software Engineering, 27(5), 111 (45 pages). External link
Rahman, M. M., Khomh, F., Yeasmin, S., & Roy, C. K. (2021). The forgotten role of search queries in IR-based bug localization: an empirical study. Empirical Software Engineering, 26(6), 116 (56 pages). External link
Rahman, M. M., Khomh, F., & Castelluccio, M. (2020, September). Why are Some Bugs Non-Reproducible? : An Empirical Investigation using Data Fusion [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2020). External link
Shah, M. B., Rahman, M. M., & Khomh, F. (2024). Towards enhancing the reproducibility of deep learning bugs: an empirical study. Empirical Software Engineering, 30(1), -. External link
Silva, R. F., Rahman, M. M., Dantas, C. E., Roy, C. K., Khomh, F., & Maia, M. A. (2021). "A Replication Package For The Paper ""Improved Retrieval of Programming Solutions with Code Examples Using a Multi-featured Score""" [Dataset]. External link
Silva, R. F., Rahman, M. M., Dantas, C. E., Roy, C., Khomh, F., & Maia, M. A. (2021). Improved retrieval of programming solutions with code examples using a multi-featured score. Journal of Systems and Software, 181, 14 pages. External link
Vahedi, M., Rahman, M. M., Khomh, F., Uddin, G., & Antoniol, G. (2021, March). Summarizing Relevant Parts from Technical Videos [Paper]. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2021), Honolulu, HI, USA. External link