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We have proposed and implemented AgentMatcher, an architecture for match‐making in e‐Business applications. It uses arc‐labeled and arc‐weighted trees to match buyers and sellers via our novel similarity algorithm. This paper adapts the architecture for match‐making between learners and learning objects (LOs). It uses the Canadian Learning Object Metadata (CanLOM) repository of the eduSource e‐Learning project. Through AgentMatcher’s new indexing component, known as Learning Object Metadata Generator (LOMGen), metadata is extracted from HTML LOs for use in CanLOM. LOMGen semi‐automatically generates the LO metadata by combining a word frequency count and dictionary lookup. A subset of these metadata terms can be selected from a query interface, which permits adjustment of weights that express user preferences. Web‐based pre‐filtering is then performed over the CanLOM metadata kept in a relational database. Using an XSLT (Extensible Stylesheet Language Transformations) translator, the pre‐filtered result is transformed into an XML representation, called Weighted Object‐Oriented (WOO) RuleML (Rule Markup Language). This is compared to the WOO RuleML representation obtained from the query interface by AgentMatcher’s core Similarity Engine. The final result is presented as a ranked LO list with a user‐specified threshold.

Presents the results of a study aimed at investigating the antecedent software development factors that affect quality of final products. By monitoring those causal factors from the early phases of development, one can have a final product of enhanced quality and reduced costs.

The study considered an unprecedentedly large number of 30 C++ object‐oriented systems of varied size and application domains, a comprehensive suite of large number of predictive software design or code measures in one study, and compared their results on a common platform.

It was found that many of the software design or code measures have a significant positive or negative relationship with quality.

The value of the paper lies in the fact that it addresses some of the major problems from which most of the studies conducted in this research domain suffer. The objective and justification of this paper are to address these deficiencies, in addition to validating some of the results obtained in earlier studies. Another important value of the paper lies in the fact that, based on the results of the study, the paper enlists useful lessons learned that can provide some practical insight for practitioners and quality managers.