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

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.

Performance and reliability prediction for evolving service-oriented software systems

“…This paper extends two former publications [31,32] with a survey of related studies, more detail about the Q-ImPrESS method, a much more detailed description of the data collection, extended performance predictions, effort estimations for both performance and reliability predictions as well as a detailed discussion of the modeling, implementation, and process issues of Q-ImPrESS.…”

An industrial case study of performance and cost design space exploration

“…The ability to re-use models and components was another useful feature [23]. Moreover, the Palladio workbench has been used in industrial case studies before [27,33], thus we assume that it is mature and sufficiently stable. Palladio's drawbacks lie in its more laborious model creation due to the complex meta model and its weaker user documentation.…”

Constructing performance model of JMS middleware platform

“…Such models are used in early stages of the software development process to guide important design decisions, or in software maintenance activities when a change impact analysis can be conducted to choose among multiple modification directions [21]. On the input side, the modeler can usually collect information about the timing (or more generally resource demands) of the operations used as atomic elements of the model.…”