An industrial case study on quality impact prediction for evolving service-oriented software
| Autor: | Raffaela Mirandola, Klaus Krogmann, Heiko Koziolek, Roland Weiss, Mircea Trifu, Anne Koziolek, Bastian Schlich, Steffen Becker, Carlos G. Bilich |
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| Přispěvatelé: | University of Zurich, Koziolek, Heiko |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Reverse engineering
Decision support system Source lines of code computer.internet_protocol Computer science business.industry 10009 Department of Informatics media_common.quotation_subject Service-oriented architecture 000 Computer science knowledge & systems computer.software_genre Automation 1712 Software Software Unified Modeling Language Quality (business) Architectural decision Software engineering business INF computer media_common computer.programming_language |
| Zdroj: | ICSE |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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