However, we experienced that such replays can produce a large amount of false positive alerts that make the results unreliable or time consuming to analyze.
Thus, the goal of this study is to investigate the software quality assurance strategies adopted during the development, integration, and maintenance of AI/ML components and code.
Collaborative AI systems (CAISs) aim at working together with humans in a shared space to achieve a common goal.
Thus, building such systems with strong assurances of compliance with requirements domain specific standards and regulations is of greatest importance.
Regulatory compliance is a well-studied area, including research on how to model, check, analyse, enact, and verify compliance of software.
Finally, we were interested in the challenges and benefits reported of continuous experimentation.
no code implementations • 29 Jan 2021 • Jil Klünder, Regina Hebig, Paolo Tell, Marco Kuhrmann, Joyce Nakatumba-Nabende, Rogardt Heldal, Stephan Krusche, Masud Fazal-Baqaie, Michael Felderer, Marcela Fabiana Genero Bocco, Steffen Küpper, Sherlock A. Licorish, Gustavo Lòpez, Fergal McCaffery, Özden Özcan Top, Christian R. Prause, Rafael Prikladnicki, Eray Tüzün, Dietmar Pfahl, Kurt Schneider, Stephen G. MacDonell
Our results show that 76. 8% of the companies implement hybrid methods.
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
Aims: The goal is to explore indicators of potential Technical Debt and Waste in NFRs documentation.
Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks.