Requirements Elicitation via Fit-Gap Analysis: A View through the Grounded Theory Lens
Publication date
2021
Editors
La Rosa, Marcello
Sadiq, Shazia
Teniente, Ernest
Advisors
Supervisors
Document Type
Part of book
Metadata
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License
taverne
Abstract
While requirements elicitation remains a key success factor for software projects, there is little empirical research on the elicitation methods. We focus on fit-gap analysis, a requirements elicitation technique that is common in practice, but hardly studied in requirements engineering research. Fit-gap analysis is a method for matching software products with the needs of customers, with the aim to identify needs that are supported as fits, and needs that are not as gaps. Through a grounded theory investigation of recording transcripts from fit-gap analysis sessions, we provide empirical knowledge about this elicitation technique. We determine and discuss the different categories of the topics contained in a fit-gap analysis. Additionally, as a first step toward assisting analysts in processing and exploring their analyses, we build and share a set of keywords and phrases that can help automatically identify those categories within the transcripts. We conduct an experiment for early validation, involving both students and practitioners, that determines the relative perceived importance of the identified fit-gap categories. Finally, we derive implications for research in the field that include our perspective on how tooling can assist analysts in fit-gap analysis.
Keywords
Elicitation techniques, Fit-gap analysis, Grounded theory, Requirements elicitation, Requirements engineering, Taverne, Theoretical Computer Science, General Computer Science
Citation
Spijkman, T, Dalpiaz, F & Brinkkemper, S 2021, Requirements Elicitation via Fit-Gap Analysis: A View through the Grounded Theory Lens. in M La Rosa, S Sadiq & E Teniente (eds), Advanced Information Systems Engineering. CAiSE 2021 : 33rd International Conference, CAiSE 2021, Melbourne, VIC, Australia, June 28 – July 2, 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12751 LNCS, Springer, pp. 363-380. https://doi.org/10.1007/978-3-030-79382-1_22