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Productivity Analysis of the Distributed QoS Modeling Language

Productivity Analysis of the Distributed QoS Modeling Language

Joe Hoffert, Douglas C. Schmidt, Aniruddha Gokhale
Copyright: © 2011 |Pages: 21
ISBN13: 9781616928742|ISBN10: 1616928743|EISBN13: 9781616928766
DOI: 10.4018/978-1-61692-874-2.ch008
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MLA

Hoffert, Joe, et al. "Productivity Analysis of the Distributed QoS Modeling Language." Model-Driven Domain Analysis and Software Development: Architectures and Functions, edited by Janis Osis and Erika Asnina, IGI Global, 2011, pp. 156-176. https://doi.org/10.4018/978-1-61692-874-2.ch008

APA

Hoffert, J., Schmidt, D. C., & Gokhale, A. (2011). Productivity Analysis of the Distributed QoS Modeling Language. In J. Osis & E. Asnina (Eds.), Model-Driven Domain Analysis and Software Development: Architectures and Functions (pp. 156-176). IGI Global. https://doi.org/10.4018/978-1-61692-874-2.ch008

Chicago

Hoffert, Joe, Douglas C. Schmidt, and Aniruddha Gokhale. "Productivity Analysis of the Distributed QoS Modeling Language." In Model-Driven Domain Analysis and Software Development: Architectures and Functions, edited by Janis Osis and Erika Asnina, 156-176. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-61692-874-2.ch008

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Abstract

Model-driven engineering (MDE), in general, and Domain-Specific Modeling Languages (DSMLs), in particular, are increasingly used to manage the complexity of developing applications in various domains. Although many DSML benefits are qualitative (e.g., ease of use, familiarity of domain concepts), there is a need to quantitatively demonstrate the benefits of DSMLs (e.g., quantify when DSMLs provide savings in development time) to simplify comparison and evaluation. This chapter describes how the authors conducted productivity analysis for the Distributed Quality-of-Service (QoS) Modeling Language (DQML). Their analysis shows (1) the significant productivity gain using DQML compared with alternative methods when configuring application entities and (2) the viability of quantitative productivity metrics for DSMLs.

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