A heuristic for software evaluation and selection
Software—Practice & Experience
Appraisal and evaluation of software products
Journal of Information Science
C4.5: programs for machine learning
C4.5: programs for machine learning
Fuzzy clustering in software reusability
Software—Practice & Experience
Component software: beyond object-oriented programming
Component software: beyond object-oriented programming
Automatic thesaurus construction supporting fuzzy retrieval of reusable components
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Lessons learned through six years of component-based development
Communications of the ACM
Art of Software Testing
Challenges in COTS decision-making: a goal-driven requirements engineering perspective
SEKE '02 Proceedings of the 14th international conference on Software engineering and knowledge engineering
Acquiring COTS Software Selection Requirements
IEEE Software
Testing Component-Based Software: A Cautionary Tale
IEEE Software
COTS Characterization Model in a COTS-Based Development Environment
APSEC '03 Proceedings of the Tenth Asia-Pacific Software Engineering Conference Software Engineering Conference
Selecting Components: a Process for Context-Driven Evaluation
APSEC '03 Proceedings of the Tenth Asia-Pacific Software Engineering Conference Software Engineering Conference
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Component-based Software Engineering (CBSE) provides solutions to the development of complex and evolving systems. As these systems are created and maintained, the task of selecting components is repeated. The Context-driven Component Evaluation (CdCE) project is developing strategies and techniques for automating a repeatable process for assessing software components. This paper describes our work using Artificial Intelligence (AI) techniques to classify components based on an ideal component specification. Using AI we are able to represent dependencies between attributes, overcoming some of the limitations of existing aggregation-based approaches to component selection.