Metrics for software architecture: a case study in the telecommunication domain

Kalyanasundaram, S.; Ponnambalam, K.; Singh, Amar Pal; Stacey, B.J.; Munikoti, R.

doi:10.1109/ccece.1998.685597

Cited by 8 publications

(2 citation statements)

References 2 publications

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“…Downstream SEPA tools can then extend this evaluation against the more detailed Application Architecture and Implementation Architecture, incorporating scenariobased evaluation when applicable. RARE improves upon [25] by establishing a stronger correlation between high-level goals and low-level metrics, managing the many-to-many relationships through heuristics and strategies. RARE research has identified static metrics from a number of sources, although many do not apply since they focus on implementationlevel concerns.…”

Section: Illustration Of Derivation and Conflict Managementmentioning

confidence: 98%

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Tool support for systematic class identification in object-oriented software architectures

Barber

Graser

Proceedings 37th International Conference on Technology of Object-Oriented Languages and Systems. TOOLS-Pacific 2000

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Sofrware architectures have received considerable attention in both research and practice for representing system stakeholder concerns, and many researchers have leveraged objectoriented models and methods for sofiare architecture representation and evaluation. While the benefits associated with object-oriented approaches are closely aligned with desirable qualities for sofrware architectures (e.g., reusability, extensibility, comprehensibility, performance), these beneJits are only realized through rational decision-making by the analyst when identihing object-oriented classes from a requirements specification. Traditionally, the class identification process has been somewhat subjective, with dvferent analysts often arriving at completely different class models from the same requirements specijkation. Since this subjectivity does not lend itself to automation, little tool support is available for identgfying object-oriented classes and their relationships. This paper describes a tool under development, Reference Architecture Representation Environment (RARE,), designed to systematically guide the analyst through class identification by applying heuristics associated with quality attributes and evaluating the resulting architecture based on relevant static metrics. RARE helps address a number of challenges typically faced during the class identification process, including (I) developing an architecture that reflects the quality attributes prioritized by the analyst, (2) managing inherent conflicts between selected attributes, and (3) capturing analyst expertise and rationale for use by others.

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Section: Illustration Of Derivation and Conflict Managementmentioning

confidence: 98%

“…RARE research has identified static metrics from a number of sources, although many do not apply since they focus on implementationlevel concerns. Sources include architectural analysis efforts such as [25] and more traditional object-oriented analysis metrics [16-191 and heuristics [ 151.…”

Section: Illustration Of Derivation and Conflict Managementmentioning

confidence: 99%