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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.
Coviello, C., Romano, S., Scanniello, G., & Antoniol, G. (2022). GASSER: A Multi-Objective Evolutionary Approach for Test Suite Reduction. International Journal of Software Engineering and Knowledge Engineering, 32(2), 193-225. External link
Coviello, C., Romano, S., Scanniello, G., & Antoniol, G. (2021, March). Gasser [Paper]. 28th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2021), Honolulu, HI, USA. External link
Coviello, C., Romano, S., Scanniello, G., Marchetto, A., Corazza, A., & Antoniol, G. (2020). Adequate vs. inadequate test suite reduction approaches. Information and Software Technology, 119, 19 pages. External link
Coviello, C., Romano, S., Scanniello, G., & Antoniol, G. (2020, October). GASSER: Genetic algorithm for teSt suite reduction [Paper]. 14th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2020) (6 pages). External link
Coviello, C., Romano, S., Scanniello, G., Marchetto, A., Corazza, A., & Antoniol, G. (2019). Adequate vs. Inadequate Test Suite Reduction Approaches. Raw Data [Dataset]. External link
Coviello, C., Romano, S., Scanniello, G., Marchetto, A., Antoniol, G., & Corazza, A. (2018, March). Clustering support for inadequate test suite reduction [Paper]. 25th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2018), Campobasso, Italy. External link
Politowski, C., Khomh, F., Romano, S., Scanniello, G., Petrillo, F., Guéhéneuc, Y.-G., & Maïga, A. (2020). A large scale empirical study of the impact of Spaghetti Code and Blob anti-patterns on program comprehension. Information and Software Technology, 122, 14 pages. External link
Politowski, C., Khomh, F., Romano, S., Scanniello, G., Petrillo, F., Guéhéneuc, Y.-G., & Maiga, A. (2020). A Large Scale Empirical Study of the Impact of Spaghetti Code and Blob Anti-patterns on Program Comprehension [Dataset]. External link
Romano, S., Scanniello, G., Antoniol, G., & Marchetto, A. (2018). SPIRITuS: a SimPle Information Retrieval regressIon Test Selection approach. Information & Software Technology, 99, 62-80. External link
