no code implementations • NAACL (GeBNLP) 2022 • Emeralda Sesari, Max Hort, Federica Sarro
Pre-trained word embedding models are easily distributed and applied, as they alleviate users from the effort to train models themselves.
no code implementations • 8 Nov 2023 • Mar Zamorano, Carlos Cetina, Federica Sarro
Video games demand is constantly increasing, which requires the costly production of large amounts of content.
no code implementations • 18 Oct 2023 • Alexander E. I. Brownlee, James Callan, Karine Even-Mendoza, Alina Geiger, Carol Hanna, Justyna Petke, Federica Sarro, Dominik Sobania
We find that the number of patches passing unit tests is up to 75% higher with LLM-based edits than with standard Insert edits.
no code implementations • 23 Aug 2023 • Saurabhsingh Rajput, Tim Widmayer, Ziyuan Shang, Maria Kechagia, Federica Sarro, Tushar Sharma
This work will facilitate further advances in DL energy measurement and the development of energy-aware practices for DL systems.
no code implementations • 5 Aug 2023 • Xinyue Li, Zhenpeng Chen, Jie M. Zhang, Federica Sarro, Ying Zhang, Xuanzhe Liu
This paper analyzes fairness in automated pedestrian detection, a crucial but under-explored issue in autonomous driving systems.
1 code implementation • 25 Jul 2023 • Zhenpeng Chen, Jie M. Zhang, Federica Sarro, Mark Harman
Existing research mostly improves the fairness of Machine Learning (ML) software regarding a single protected attribute at a time, but this is unrealistic given that many users have multiple protected attributes.
no code implementations • 17 Sep 2022 • Minghua Ma, Zhao Tian, Max Hort, Federica Sarro, Hongyu Zhang, QIngwei Lin, Dongmei Zhang
In this paper, we propose an approach for the selection of the initial seeds to generate IDIs for fairness testing.
no code implementations • 14 Jul 2022 • Max Hort, Zhenpeng Chen, Jie M. Zhang, Mark Harman, Federica Sarro
How many datasets are used for evaluating bias mitigation methods?
2 code implementations • 7 Jul 2022 • Zhenpeng Chen, Jie M. Zhang, Federica Sarro, Mark Harman
We find that (1) the bias mitigation methods significantly decrease ML performance in 53% of the studied scenarios (ranging between 42%~66% according to different ML performance metrics); (2) the bias mitigation methods significantly improve fairness measured by the 4 used metrics in 46% of all the scenarios (ranging between 24%~59% according to different fairness metrics); (3) the bias mitigation methods even lead to decrease in both fairness and ML performance in 25% of the scenarios; (4) the effectiveness of the bias mitigation methods depends on tasks, models, the choice of protected attributes, and the set of metrics used to assess fairness and ML performance; (5) there is no bias mitigation method that can achieve the best trade-off in all the scenarios.
no code implementations • 4 Jul 2022 • Rebecca Moussa, Federica Sarro
We carry out a thorough empirical study comparing the performance of the machine learners on 5 SEE datasets in the two most common SEE scenarios (i. e., out-of-the-box-ml and tuned-ml) as well as an in-depth analysis of the documentation and code of their APIs.
no code implementations • 24 Feb 2022 • Rebecca Moussa, Danielle Azar, Federica Sarro
While, we cannot conclude that OCSVM is the best classifier, our results still show interesting findings.
1 code implementation • 14 Jan 2022 • Vali Tawosi, Rebecca Moussa, Federica Sarro
In the last decade, several studies have explored automated techniques to estimate the effort of agile software development.
no code implementations • 18 Oct 2021 • Tushar Sharma, Maria Kechagia, Stefanos Georgiou, Rohit Tiwari, Indira Vats, Hadi Moazen, Federica Sarro
This paper aims to summarize the current knowledge in applied machine learning for source code analysis.
1 code implementation • 12 Mar 2021 • Giovani Guizzo, Federica Sarro, Jens Krinke, Silvia Regina Vergilio
The results show that strategies generated by Sentinel outperform the baseline strategies in 95% of the cases always with large effect sizes.
1 code implementation • 7 Oct 2020 • Paul Ralph, Nauman bin Ali, Sebastian Baltes, Domenico Bianculli, Jessica Diaz, Yvonne Dittrich, Neil Ernst, Michael Felderer, Robert Feldt, Antonio Filieri, Breno Bernard Nicolau de França, Carlo Alberto Furia, Greg Gay, Nicolas Gold, Daniel Graziotin, Pinjia He, Rashina Hoda, Natalia Juristo, Barbara Kitchenham, Valentina Lenarduzzi, Jorge Martínez, Jorge Melegati, Daniel Mendez, Tim Menzies, Jefferson Molleri, Dietmar Pfahl, Romain Robbes, Daniel Russo, Nyyti Saarimäki, Federica Sarro, Janet Siegmund, Diomidis Spinellis, Miroslaw Staron, Klaas Stol, Margaret-Anne Storey, Davide Taibi, Damian Tamburri, Marco Torchiano, Christoph Treude, Burak Turhan, XiaoFeng Wang, Sira Vegas
Empirical Standards are natural-language models of a scientific community's expectations for a specific kind of study (e. g. a questionnaire survey).
Software Engineering General Literature
no code implementations • 8 Aug 2020 • Yixue Zhao, Justin Chen, Adriana Sejfia, Marcelo Schmitt Laser, Jie Zhang, Federica Sarro, Mark Harman, Nenad Medvidovic
UI testing is tedious and time-consuming due to the manual effort required.
Software Engineering
no code implementations • 30 Nov 2013 • Filomena Ferrucci, M-Tahar Kechadi, Pasquale Salza, Federica Sarro
The main purpose of this framework is to allow the user to focus on the aspects of GA that are specific to the problem to be addressed, being sure that this task is going to be correctly executed on the Cloud with a good performance.