Comparison of model size predictors in practice
Paper in proceedings, 2017

The amount of software in modern vehicles is constantly growing. However, the risk for functional and quality deficiencies increases simultaneously with size. This results in industry for example in inevitable and unexpected refactorings of software models, which is slowing down development processes in turn. In this industrial case study, we evaluate model growth predictors applied to foresee critical model size developments. We present five approaches and systematically compare them regarding prediction accuracy.

Author

Jan Schröder

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)

Christian Berger

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)

Alessia Knauss

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)

H. Preenja

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)

Mohammad Ali

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)

Miroslaw Staron

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)

T. Herpel

Automotive Safety Technologies GmbH

Proceedings - 2017 IEEE/ACM 39th International Conference on Software Engineering Companion, ICSE-C 2017

186-188

Subject Categories

Software Engineering

DOI

10.1109/ICSE-C.2017.66

ISBN

978-1-5386-1589-8

More information

Latest update

7/23/2018