Beyond ATAM: Architecture Analysis in the Development of Large Scale Software Systems

  • Andrzej Zalewski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4758)


Large scale software developments require substantial investment and are exposed to high level of risk. Architectural decisions taken at early stages of the development can substantially influence the entire level of technical risk. In this paper architectural decisions are divided into two basic groups: early – top level system organization decision establishing system organization patterns (the notion introduced in this paper) and detailed ones. However as it was shown on notable examples of large scale developments carried out in recent ten years in Poland, wrong decisions concerning system organization pattern can trigger severe risks that can lead to the development crisis. These risks are frequently connected with the complexity explosion syndrome – sudden, undetected growth of design complexity that exceeds the capabiblity of the development team and time budget. To manage these risks properly appropriate architecture analysis method has been introduced. On the contrary to the traditional scenario-based architecture analysis methods, like ATAM, it was based on GQM approach. A complete assessement framework have been defined comprising three goals: complexity control, organizational adequacy and satisfactory perforormance and reliability; a set of questions related to these goals, as well as metrics for the qualities expressed by these questions. The conclusion contains ex post analysis of exemplary large scale systems showing that the proposed framework provides adequate assessement of design risk. It has also been indicated that the critical risks identified during the evalution of the system organization pattern should be carefully managed.


Software Architecture Distribution Center Transaction Management Architecture Analysis Architecture Evaluation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Andrzej Zalewski
    • 1
  1. 1.Warsaw University of Technology, Institute of Automatic Control and Computational Engineering, WarsawPoland

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