Algorithms for clustering data
Algorithms for clustering data
Implementing distribution and persistence aspects with aspectJ
OOPSLA '02 Proceedings of the 17th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
AbstFinder, A Prototype Natural Language Text Abstraction Finder for Use in Requirements Elicitation
Automated Software Engineering
Recovering Traceability Links between Code and Documentation
IEEE Transactions on Software Engineering
Recovering documentation-to-source-code traceability links using latent semantic indexing
Proceedings of the 25th International Conference on Software Engineering
Utilizing Supporting Evidence to Improve Dynamic Requirements Traceability
RE '05 Proceedings of the 13th IEEE International Conference on Requirements Engineering
IEEE Software
Advancing Candidate Link Generation for Requirements Tracing: The Study of Methods
IEEE Transactions on Software Engineering
Isolating and relating concerns in requirements using latent semantic analysis
Proceedings of the 21st annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications
Poirot: A Distributed Tool Supporting Enterprise-Wide Automated Traceability
RE '06 Proceedings of the 14th IEEE International Requirements Engineering Conference
The Detection and Classification of Non-Functional Requirements with Application to Early Aspects
RE '06 Proceedings of the 14th IEEE International Requirements Engineering Conference
Automated classification of non-functional requirements
Requirements Engineering
Using data mining and recommender systems to scale up the requirements process
Proceedings of the 2nd international workshop on Ultra-large-scale software-intensive systems
A theory of aspects as latent topics
Proceedings of the 23rd ACM SIGPLAN conference on Object-oriented programming systems languages and applications
An early aspect for model-driven transformers engineering
Proceedings of the 2011 international workshop on Early aspects
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This paper describes an approach for automating the detection of early aspects. Based on hierarchical clustering and an underlying probabilistic algorithm, the technique generates initial requirements clusters representing relatively homogenous feature sets, use cases and potential cross-cutting concerns. A second clustering phase is then applied in which dominant terms are identified and removed from each of the initial clusters, allowing new clusters to form around less dominant terms. This second phase enables previously inter-tangled aspects to be detected. Three metrics are introduced to differentiate potential cross-cutting concerns from other types of clusters. The approach is illustrated through an example based on the Public Health Watcher case study.