There are three main areas of focus within this topic: using natural language processing to improve qualitative data analysis and requirements engineering, examining qualitative data analysis in software engineering research, and software engineering education.
Research in this area is concerned with improving the process of analyzing textual data, and with improving the quality of the results. Important work in this area has dealt with exploring natural language processing to improve the consistency of qualitative data analysis (Kaufmann, Barcomb and Riehle, 2020) and using qualitative data analysis in requirements engineering (Kaufmann, Krause, Harutyunyan, Barcomb and Riehle, 2021). Current research in this area is supported by an NSERC Discovery grant. Collaborators associated with this research area include Dr. Andreas Kaufmann, Dr. Guenther Ruhe, and Dr. Feng Chen.
This research area focuses on examining how researchers in software engineering area performing qualitative research, and how it might be improved. Initial work involved technical reports on using qualitative data analysis techniques to add rigor to the development of patterns (Riehle, Harutyunyan and Barcomb, 2020) and on using a distributed team for inter-rater agreement in qualitative data analysis (Kaufmann, Barcomb and Riehle, 2016). External collaborators associated with this research area include Dr. Dirk Riehle, Dr. Andreas Kaufmann, Dr. Nikolay Harutyunyan, and Elçin Yenişen Yavuz.
This topic concerns how to conduct and improve software engineering education. A key paper in this area concerned industry collaboration to improve the content of courses (Marasco, Barcomb, Dwomoh, Eguia, Jaffary, Johnson, Leonard and Shupe, 2022). Our collaborators include Dr. Emily Marasco.
This line of research focuses on the people and processes involved in the creation of software. There are three main areas of research under this theme, in addition to single papers exploring other ideas. The main areas are: open source software contributors and communities, diversity and inclusion, and component and tool selection.
Much of the work in this area has focused on episodic participation, including why people participate episodically, how their episodic participation relates to their participation in other projects, and how communities or organizations can support and make use of episodic participants. The most important work in this area consists of three papers which were part of Dr. Barcomb's doctoral dissertation (Barcomb, 2019); the papers examined the presence of episodic participation in free/libre and open source software development (Barcomb, Kaufmann, Riehle, Stol and Fitzgerald, 2018), retention of episodic participants in free/libre and open source software (Barcomb, Stol, Riehle and Fitzgerald, 2019), and practices for managing episodic participation in free/libre and open source software (Barcomb, Stol, Fitzgerald and Riehle, 2020). Recent work in this area has also considered company participation in free/libre and open source software (Schwab, Riehle, Barcomb and Harutyunyan, 2020; Weikert, Riehle and Barcomb, 2019; Yenişen Yavuz, Barcomb and Riehle, 2022). Current collaborators in this area of research include Elçin Yenişen Yavuz, Mehmet Ali Erol, Dr. Roisin Lyons, and Dr. Klaas-Jan Stol.
Other work related to human factors has included examining how software developers acquire skills (Barcomb, Grottke, Stauffert, Riehle and Jahn, 2015) and ordering managerial preferences for developers' skills (Barcomb, Jullien, Meyer and Olteanu, 2019).
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