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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.

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Integrating 3rd party components in software systems provides promising advantages but also risks due to disconnected evolution cycles. Deciding whether to migrate to a newer version of a 3rd party component integrated into selfimplemented code or to switch to a different one is challenging. Dedicated evolution support for 3rd party component scenarios is hence required. Existing approaches do not account for open source components which allow accessing and analyzing their source code and project information. The approach presented in this paper combines analyses for code dependency, code quality, and bug tracker information for a holistic view on the evolution with 3rd party components. We applied the approach in a case study on a communication middleware component for industrial devices used at ABB. We identified 7 methods potentially impacted by changes of 3rd party components despite the absence of interface changes. We further identified selfimplemented code that does not need any manual investigation after the 3rd party component evolution as well as a positive trend of code and bug tracker issues.

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Managing the cost-effective evolution of industrial software systems is a challenging task because of their complexity and long lifetimes. Limited pro-active evolution planning and software architecture erosion often lead to huge maintenance costs in such systems. However, formerly researched approaches for evolution scenario analysis and architecture enforcement are only reluctantly applied by practitioners due to their perceived overhead and high costs. We have applied several recent sustainability evaluation and improvement approaches in a case study to the software architecture of a large industrial software system currently under development at ABB. We combined our selection of approaches in a lightweight method called MORPHOSIS, for which this paper presents experiences and lessons learned. We found that reasonable sustainability evaluation and improvement is possible already with limited efforts.

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