Abstract
Automated analysis of object-oriented design models can provide insight into the quality of a given software design. Data obtained from automated analysis, however, is often too complex to be easily understood by a designer. This paper examines the use of an automated analysis tool on industrial software UML class models, where one set of models was created as part of the design process and the other was obtained from reverse engineering code. The analysis was performed by DesignAdvisor, a tool developed by Siemens Corporate Research, that supports metrics-based analysis and detection of design guideline violations. The paper describes the lessons learned from using the automated analysis techniques to assess the quality of these models. We also assess the impact of design pattern use in the overall quality of the models. Based on our lessons learned, identify design guidelines that would minimize the occurrence of these errors.
This work is supported in part by Siemens Corporate Research, NSF grants EIA-0000433, CDA-9700732, CCR-9901017, Department of the Navy, and Office of Naval Research under Grant No. N00014-01-1-0744, and in cooperation with Siemens Transportation and Detroit Diesel Corporation.
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© 2005 Springer-Verlag Berlin Heidelberg
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Cheng, B.H.C., Stephenson, R., Berenbach, B. (2005). Lessons Learned from Automated Analysis of Industrial UML Class Models (An Experience Report) . In: Briand, L., Williams, C. (eds) Model Driven Engineering Languages and Systems. MODELS 2005. Lecture Notes in Computer Science, vol 3713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11557432_24
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DOI: https://doi.org/10.1007/11557432_24
Publisher Name: Springer, Berlin, Heidelberg
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