Soft Competencies and Satisfaction Levels for Software Engineers : A Unified Framework
Abstract
The importance of software engineers’ competency has long been established as a key pillar for the development of robust software in order to achieve quality software. Software engineering competency research is not necessarily lacking. Nevertheless, the satisfaction derived from using software competency needs more investigation. The aim of this study is to identify soft competencies from empirical data and create satisfaction levels for software engineers’ soft competencies. The result shows 63 soft competencies with three different satisfaction levels consisting of basic, performance and delighters. The paper contributes to the SEC research by highlighting the satisfaction levels of soft competency for the benefit of the educators (academia), software engineers (possessor) and users of software competency (practitioner).
Main Authors
Format
Conferences
Conference paper
Published
2020
Series
Subjects
Publication in research information system
Publisher
Springer
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202001301905Käytä tätä linkitykseen.
Parent publication ISBN
978-3-030-35509-8
Review status
Peer reviewed
ISSN
1865-1348
DOI
https://doi.org/10.1007/978-3-030-35510-4_5
Conference
International Conference on Software Quality
Language
English
Published in
Lecture Notes in Business Information Processing
Is part of publication
Software Quality : Quality Intelligence in Software and Systems Engineering. Proceedings of the 12th International Conference, SWQD 2020, Vienna, Austria, January 14–17, 2020
Citation
- Assyne, N. (2020). Soft Competencies and Satisfaction Levels for Software Engineers : A Unified Framework. In D. Winkler, S. Biffl, D. Mendez, & J. Bergsmann (Eds.), Software Quality : Quality Intelligence in Software and Systems Engineering. Proceedings of the 12th International Conference, SWQD 2020, Vienna, Austria, January 14–17, 2020 (371, pp. 69-83). Springer. Lecture Notes in Business Information Processing. https://doi.org/10.1007/978-3-030-35510-4_5
License
Copyright© Springer Nature Switzerland AG 2020