Title | ||
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EA-Analyzer: a tool for identifying conflicting dependencies in requirements documents |
Abstract | ||
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Aspect-oriented requirements engineering is a promising approach to early aspects, and its main goal is to address the composability and subsequent analysis of crosscutting concerns during requirements engineering. The goal is to reveal aspect influences and mutual tradeoffs among aspects before the architecture is derived. An important step towards the effective analysis of early aspects is to provide tools for mining aspects in requirements document, and inferring dependencies and trade-offs among broadly-scoped concerns. However, to date, current techniques for the analysis of early aspects have mainly been limited to techniques for mining aspects using natural language processing or information retrieval. The analysis of inferences and trade-offs are still being done manually with visual inspection. EA-Analyzer is a tool for indentifying conflicting dependencies that are contained within a requirements document. The tool is an application of a Bayesian learning method, and offers early insights into architectural trade-offs. |
Year | DOI | Venue |
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2009 | 10.1145/1509825.1509835 | Proceedings of the 15th workshop on Early aspects |
Keywords | DocType | Citations |
mining aspect,effective analysis,requirements engineering,subsequent analysis,conflicting dependency,requirements document,aspect-oriented requirements engineering,requirements composition,aspect-oriented software development,early insight,architectural trade-offs,main goal,conflicting dependencies,requirements analysis,early aspect,requirement analysis,visual inspection,natural language processing,bayesian learning,requirement engineering,information retrieval | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Alberto Sardinha | 1 | 36 | 8.27 |
Awais Rashid | 2 | 2041 | 149.78 |
Ruzanna Chitchyan | 3 | 531 | 42.59 |
Nathan Weston | 4 | 100 | 6.23 |
Phil Greenwood | 5 | 348 | 20.93 |