Architectural assumptions and their management in industry – An exploratory study
Paper i proceeding, 2017

As an important type of architectural knowledge, architectural assumptions should be well managed in projects. However, little empirical research has been conducted regarding architectural assumptions and their management in software development. In this paper, we conducted an exploratory case study with twenty-four architects to analyze architectural assumptions and their management in industry. In this study, we confirmed certain findings from our previous survey on architectural assumptions (e.g., neither the term nor the concept of architectural assumption is commonly used in industry, and stakeholders may have different understandings of the architectural assumption concept). We also got five new findings: (1) architects frequently make architectural assumptions in their work; (2) the architectural assumption concept is subjective; (3) architectural assumptions are context-dependent and have a dynamic nature (e.g., turning out to be invalid or vice versa during their lifecycle); (4) there is a connection between architectural assumptions and certain types of software artifacts (e.g., requirements and design decisions); (5) twelve architectural assumptions management activities and four benefits of managing architectural assumptions were identified. © 2017, Springer International Publishing AG.

Case study

Architectural assumption

Architectural assumptions management

Författare

Chen Yang

Rijksuniversiteit Groningen

Wuhan University

Peng Liang

Wuhan University

Paris Avgeriou

Rijksuniversiteit Groningen

Ulf Eliasson

Volvo Cars

Chalmers, Data- och informationsteknik

Rogardt Heldal

Chalmers, Data- och informationsteknik, Software Engineering

Patrizio Pelliccione

Göteborgs universitet

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 10475 LNCS 191-207

Ämneskategorier

Data- och informationsvetenskap

DOI

10.1007/978-3-319-65831-5_14

Mer information

Senast uppdaterat

2019-01-17