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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.
Qin, Q., & Merlo, E. (2025). Prune bias from the root: Bias removal and fairness estimation by pruning sensitive attributes in pre-trained DNN models. Information and Software Technology, 189, 107906 (12 pages). External link
Qin, Q., Djian, B. P. P., Merlo, E., Li, H., & Gambs, S. (2025). Representation-based fairness evaluation and bias correction robustness assessment in neural networks. Information and Software Technology, 107876 (21 pages). External link
Caumartin, G., Qin, Q., Chatragadda, S., Panjrolia, J., Li, H., & Elias Costa, D. (2025, March). Exploring the Potential of Llama Models in Automated Code Refinement: A Replication Study [Paper]. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2025), Montreal, QC, Canada. External link
Qin, Q., Aghili, R., Li, H., & Merlo, E. (2025, March). Preprocessing is All You Need: Boosting the Performance of Log Parsers with a General Preprocessing Framework [Paper]. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2025), Montreal, QC, Canada. External link
Qin, Q., Li, H., Merlo, E., & Lamothe, M. (2025, April). Automated, Unsupervised, and Auto-Parameterized Inference of Data Patterns and Anomaly Detection [Paper]. 47th International Conference on Software Engineering (ICSE 2025), Ottawa, ON, Canada. External link
Aghili, R., Qin, Q., Li, H., & Khomh, F. (2024, October). Understanding Web Application Workloads and Their Applications: Systematic Literature Review and Characterization [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2024), Flagstaff, AZ, USA. External link
Qin, Q. (2025). Prune the Bias From the Root: Bias Removal and Fairness Estimation by Muting Sensitive Attributes in Pre-trained DNN Models [Dataset]. External link
Qin, Q., Li, H., Merlo, E., & Lamothe, M. (2025). Automated, Unsupervised, and Auto-parameterized Inference of Data Patterns and Anomaly Detection [Dataset]. External link
