%0 Conference Paper %1 koziolek2011a %A Koziolek, Heiko %A Schlich, Bastian %A Bilich, Carlos %A Weiss, Roland %A Becker, Steffen %A Krogmann, Klaus %A Trifu, Mircea %A Mirandola, Raffaela %A Koziolek, Anne %B Proceeding of the 33rd international conference on Software engineering (ICSE 2011), Software Engineering in Practice Track %D 2011 %E Taylor, Richard N. %E Gall, Harald %E Medvidovic, Nenad %I ACM, New York, NY, USA %K case_study dtmc industrial_software lqn palladio performance_prediction reliability_prediction reverse_engineering service-oriented_software trade-off_analysis %P 776--785 %R 10.1145/1985793.1985902 %T An Industrial Case Study on Quality Impact Prediction for Evolving Service-Oriented Software %U http://doi.acm.org/10.1145/1985793.1985902 %X 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. Modeldriven 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. %@ 978-1-4503-0445-0

@inproceedings{koziolek2011a, 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. Modeldriven 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.}, acmid = {1985902}, added-at = {2017-08-08T09:12:33.000+0200}, author = {Koziolek, Heiko and Schlich, Bastian and Bilich, Carlos and Weiss, Roland and Becker, Steffen and Krogmann, Klaus and Trifu, Mircea and Mirandola, Raffaela and Koziolek, Anne}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/29e69311e27a17d9204b2aac959c9a5e9/snowball}, booktitle = {Proceeding of the 33rd international conference on Software engineering (ICSE 2011), Software Engineering in Practice Track}, doi = {10.1145/1985793.1985902}, editor = {Taylor, Richard N. and Gall, Harald and Medvidovic, Nenad}, interhash = {67cf27e784a23935c632c5e2c7044aed}, intrahash = {9e69311e27a17d9204b2aac959c9a5e9}, isbn = {978-1-4503-0445-0}, keywords = {case_study dtmc industrial_software lqn palladio performance_prediction reliability_prediction reverse_engineering service-oriented_software trade-off_analysis}, location = {Waikiki, Honolulu, HI, USA}, note = {Acceptance Rate: 18\% (18/100)}, numpages = {10}, pages = {776--785}, publisher = {ACM, New York, NY, USA}, timestamp = {2018-02-15T08:08:34.000+0100}, title = {An Industrial Case Study on Quality Impact Prediction for Evolving Service-Oriented Software}, url = {http://doi.acm.org/10.1145/1985793.1985902}, year = 2011 }