Header

UZH-Logo

Maintenance Infos

An industrial case study on quality impact prediction for evolving service-oriented software


Koziolek, Heiko; Schlich, Bastian; Bilich, Carlos; Weiss, Roland; Becker, Steffen; Krogmann, Klaus; Trifu, Mircea; Mirandola, Raffaela; Koziolek, Anne (2011). An industrial case study on quality impact prediction for evolving service-oriented software. In: 33rd International Conference on Software Engineering, Waikiki, Honolulu, HI, USA, 21 May 2011 - 28 May 2011, 776-785.

Abstract

Systematic decision support for architectural design decisions is a major concern for software architects of evolving service-oriented systems. In practice, architects often analyse the expected performance and reliability of design alternatives based on prototypes or former experience. Model-driven prediction methods claim to uncover the tradeoffs between different alternatives quantitatively while being more cost-effective and less error-prone. However, they often suffer from weak tool support and focus on single quality attributes. Furthermore, there is limited evidence on their effectiveness based on documented industrial case studies. Thus, we have applied a novel, model-driven prediction method called Q-ImPrESS on a large-scale process control system consisting of several million lines of code from the automation domain to evaluate its evolution scenarios. This paper reports our experiences with the method and lessons learned. Benefits of Q-ImPrESS are the good architectural decision support and comprehensive tool framework, while one drawback is the time-consuming data collection.

Abstract

Systematic decision support for architectural design decisions is a major concern for software architects of evolving service-oriented systems. In practice, architects often analyse the expected performance and reliability of design alternatives based on prototypes or former experience. Model-driven prediction methods claim to uncover the tradeoffs between different alternatives quantitatively while being more cost-effective and less error-prone. However, they often suffer from weak tool support and focus on single quality attributes. Furthermore, there is limited evidence on their effectiveness based on documented industrial case studies. Thus, we have applied a novel, model-driven prediction method called Q-ImPrESS on a large-scale process control system consisting of several million lines of code from the automation domain to evaluate its evolution scenarios. This paper reports our experiences with the method and lessons learned. Benefits of Q-ImPrESS are the good architectural decision support and comprehensive tool framework, while one drawback is the time-consuming data collection.

Statistics

Citations

3 citations in Web of Science®
14 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

0 downloads since deposited on 13 Feb 2012
0 downloads since 12 months

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:28 May 2011
Deposited On:13 Feb 2012 08:46
Last Modified:07 Dec 2017 11:29
Publisher:Association for Computing Machinery
Series Name:Proceedings of the 33rd International Conference on Software Engineering
ISBN:978-1-4503-0445-0
Publisher DOI:https://doi.org/10.1145/1985793.1985902
Related URLs:http://2011.icse-conferences.org/
Other Identification Number:merlin-id:3890

Download