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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.
Kouemo Ngassom, S., Moradidakhel, A., Tambon, F., & Khomh, F. (2024, July). Chain of Targeted Verification Questions to Improve the Reliability of Code Generated by LLMs [Paper]. 1st ACM International Conference on AI-Powered Software (ALWARE 2024), Porto de Galinhas, Brazil. External link
Majdinasab, V., Bishop, M. J., Rasheed, S., Moradidakhel, A., Tahir, A., & Khomh, F. (2024, March). Assessing the Security of GitHub Copilot's Generated Code - A Targeted Replication Study [Paper]. 2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2024), Rovaniemi, Finland. External link
Moradidakhel, A., Nikanjam, A., Majdinasab, V., Khomh, F., & Desmarais, M. C. (2024). Effective test generation using pre-trained Large Language Models and mutation testing. Information and Software Technology, 171, 107468 (17 pages). External link
Moradidakhel, A., Nikanjam, A., Khomh, F., Desmarais, M. C., & Washizaki, H. (2024). Generative AI for Software Development: A Family of Studies on Code Generation. In Generative AI for Effective Software Development (pp. 151-172). External link
Moradidakhel, A., Nikanjam, A., Khomh, F., Desmarais, M. C., & Washizaki, H. (2024). An Overview on Large Language Models. In Generative AI for Effective Software Development (pp. 3-21). External link
Moradidakhel, A. (2023). Enhancing Software Engineering Tasks with Intelligent Assistant Tools [Ph.D. thesis, Polytechnique Montréal]. Available
Moradidakhel, A., Desmarais, M. C., & Khomh, F. (2023). Dev2vec: Representing domain expertise of developers in an embedding space. Information and Software Technology, 159, 12 pages. External link
Moradidakhel, A., Majdinasab, V., Nikanjam, A., Khomh, F., Desmarais, M. C., & Jiang, Z. M. (2023). GitHub Copilot AI pair programmer: Asset or Liability? Journal of Systems and Software, 203, 111734 (23 pages). External link
Moradidakhel, A., Desmarais, M. C., & Khomh, F. (2022). Dev2vec: Representing Domain Expertise of Developers in an Embedding Space [Dataset]. External link
Moradidakhel, A., Desmarais, M. C., & Khomh, F. (2021, June). Assessing developer expertise from the statistical distribution of programming syntax patterns [Paper]. 25th Evaluation and Assessment in Software Engineering Conference (EASE 2021). External link
Tambon, F., Moradidakhel, A., Nikanjam, A., Khomh, F., Desmarais, M. C., & Antoniol, G. (2025). Bugs in large language models generated code: an empirical study. Empirical Software Engineering, 30(3), 48 pages. External link
