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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.
An, L. (2019). Understanding the Impact of Release Processes and Practices on Software Quality [Ph.D. thesis, Polytechnique Montréal]. Available
An, L., Castelluccio, M., & Khomh, F. (2019). An empirical study of DLL injection bugs in the Firefox ecosystem. Empirical Software Engineering, 24(4), 1799-1822. External link
An, L., Khomh, F., & Guéhéneuc, Y.-G. (2018). An empirical study of crash-inducing commits in Mozilla Firefox. Software Quality Journal, 26(2), 553-584. External link
An, L., Mlouki, O., Khomh, F., & Antoniol, G. (2017, February). Stack Overflow: A code laundering platform? [Paper]. 24th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2017), Klagenfurt, Austria. External link
An, L. (2015). Improving Bug Triaging Using Software Analytics [Master's thesis, École Polytechnique de Montréal]. Available
An, L., & Khomh, F. (2015, October). An empirical study of crash-inducing commits in Mozilla Firefox [Paper]. 11th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2015), Beijing, China. External link
An, L., & Khomh, F. (2015, August). An Empirical Study of Highly Impactful Bugs in Mozilla Projects [Paper]. IEEE International Conference on Software Quality, Reliability and Security (QRS 2015), Vancouver, BC, Canada. External link
An, L., Khomh, F., & Adams, B. (2014, September). Supplementary Bug Fixes vs. Re-opened Bugs [Paper]. 14th IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2014), Victoria, BC. External link
Barbour, L., An, L., Khomh, F., Zou, Y., & Wang, S. (2018). An investigation of the fault-proneness of clone evolutionary patterns. Software Quality Journal, 26(4), 1187-1222. External link
Barrak, A., Laverdière, M.-A., Khomh, F., An, L., & Merlo, E. (2018, October). Just-in-time Detection of Protection-Impacting Changes on WordPress and MediaWiki [Paper]. 28th Annual International Conference on Computer Science and Software Engineering (CASCON 2018), Toronto, Canada. Unavailable
Castelluccio, M., An, L., & Khomh, F. (2018). An empirical study of patch uplift in rapid release development pipelines. Empirical Software Engineering, 24(5), 3008-3044. External link
Castelluccio, M., An, L., & Khomh, F. (2017, September). Is it safe to uplift this patch? : An empirical study on Mozilla Firefox [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2017), Shanghai, China. External link
Openja, M., Morovati, M. M., An, L., Khomh, F., & Abidi, M. (2022). Technical debts and faults in open-source quantum software systems: An empirical study. Journal of Systems and Software, 193, 28 pages. External link
Palma, F., An, L., Khomh, F., Moha, N., & Guéhéneuc, Y.-G. (2014, November). Investigating the change-proneness of service patterns and antipatterns [Paper]. 7th IEEE International Conference on Service-Oriented Computing and Applications (SOCA 2014), Matsue, Japan (8 pages). External link
Tambon, F., Nikanjam, A., An, L., Khomh, F., & Antoniol, G. (2024). Silent bugs in deep learning frameworks: an empirical study of Keras and TensorFlow. Empirical Software Engineering, 29(1), 10 (34 pages). External link
Tambon, F., Laberge, G., An, L., Nikanjam, A., Mindom, P. S. N., Pequignot, Y., Khomh, F., Antoniol, G., Merlo, E., & Laviolette, F. (2022). How to certify machine learning based safety-critical systems? A systematic literature review. Automated Software Engineering, 29(2). External link
Xu, B., An, L., Thung, F., Khomh, F., & Lo, D. (2020). Why reinventing the wheels? An empirical study on library reuse and re-implementation. Empirical Software Engineering, 25(1), 755-789. External link
