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Collaboration networkThis 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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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.
Badran, K., Cote, P.-O., Kolopanis, A., Bouchoucha, R., Collante, A., Costa, D. E., Shihab, E., & Khomh, F. (2023). Can Ensembling Preprocessing Algorithms Lead to Better Machine Learning Fairness? Computer, 56(4), 71-79. External link
Badran, K., Côté, P.-O., Kolopanis, A., Bouchoucha, R., Collante, A., Costa, D. E., Shihab, E., & Khomh, F. (2022). Can Ensembling Pre-processing Algorithms Lead to Better Machine Learning Fairness? [Dataset]. External link
Chowdhury, M. A. R., Abdalkareem, R., Shihab, E., & Adams, B. (2022). On the Untriviality of Trivial Packages: An Empirical Study of npm JavaScript Packages. IEEE Transactions on Software Engineering, 48(8), 2695-2708. External link
Kamei, Y., Shihab, E., Adams, B., Hassan, A. E., Mockus, A., Sinha, A., & Ubayashi, N. (2013). A large-scale empirical study of just-in-time quality assurance. IEEE Transactions on Software Engineering, 39(6), 757-773. External link
Shihab, E., Kamei, Y., Adams, B., & Hassan, A. E. (2013). Is Lines of Code a Good Measure of Effort in Effort-Aware Models? Information and Software Technology, 55(11), 1981-1993. External link
Shihab, E., Ihara, A., Kamei, Y., Ibrahim, W. M., Ohira, M., Adams, B., Hassan, A. E., & Matsumoto, K.-I. (2013). Studying re-opened bugs in open source software. Empirical Software Engineering, 18(5), 1005-1042. External link
Shihab, E., Hassan, A. E., Adams, B., & Jiang, Z. M. (2012, November). An industrial study on the risk of software changes [Paper]. 20th ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2012), Cary, NC, United states. External link
Yin Ho, S. C., Majdinasab, V., Islam, M., Costa, D. E., Shihab, E., Khomh, F., Nadi, S., & Raza, M. (2023, October). An Empirical Study on Bugs Inside PyTorch: A Replication Study [Paper]. 39th IEEE International Conference on Software Maintenance and Evolution (ICSME 2023), Bogota, Colombia. External link
