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Soft theory: a pragmatic alternative to conduct quantitative empirical studies
Russo, Daniel; Stol, Klaas-Jan
Practitioners and scholars often face new software engineering phenomena which lack sufficient theoretical grounding. When studying such nascent and emerging topics, it is important to establish an initial and rudimentary understanding, leaving a more precise understanding of underpinning mechanisms till later. Controlled experiments, for example, might lead to insights into the specific mechanisms underpinning a certain practice, such as distributed development, pair programming, and test-driven development. However, at an initial stage of research, such highly controlled studies may not be feasible. In other domains, it may not be clear what the key constructs are, so that effective measurement cannot be done. Instead, researchers might opt for pragmatic alternative research approaches that do not require experimental control or active intervention in a study’s setting. In this paper we advocate the use of soft theory (based on soft modeling techniques) for quantitative studies in software engineering research. We discuss the use of soft theory and position it within an existing taxonomy of quantitative data analysis techniques. Soft modeling and soft theory affords us a pragmatic approach to developing inferential and predictive research models, rather than aiming to develop a causal understanding. Soft theory approaches are grounded in robust quantitative data analysis techniques. We argue that these techniques can be effectively used in industry settings which are not amenable to highly controlled studies.
Keyword(s): software engineering; experiments
Publication Date:
2019
Type: Conference item
Peer-Reviewed: Yes
Language(s): English
Institution: University of Limerick
Funder(s): Science Foundation Ireland
Citation(s): 15SIRG3293
2019 IEEE/ACM Joint 7th International Workshop on Conducting Empirical Studies in Industry (CESI) and 6th International Workshop on Software Engineering Research and Industrial Practice (SER&IP);
http://dx.doi.org/10.1109/CESSER-IP.2019.00013
15/SIRG/3293
13/RC/2094
Publisher(s): IEEE Computer Society
First Indexed: 2020-01-25 06:25:47 Last Updated: 2020-01-25 06:25:47