2010
DOI: 10.4236/jsea.2010.311118
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Case-Based Reasoning for Reducing Software Development Effort

Abstract: How can we best find project changes that most improve project estimates? Prior solutions to this problem required the use of standard software process models that may not be relevant to some new project. Also, those prior solutions suffered from limited verification (the only way to assess the results of those studies was to run the recommendations back through the standard process models). Combining case-based reasoning and contrast set learning, the W system requires no underlying model. Hence, it is widely… Show more

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Cited by 4 publications
(6 citation statements)
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“…W extends standard CBR by learning an adaption of q, called q , that retrieves better quality examples. Based on the analysis of [8] and this paper, we recommend W on several grounds:…”
Section: Resultsmentioning
confidence: 99%
See 3 more Smart Citations
“…W extends standard CBR by learning an adaption of q, called q , that retrieves better quality examples. Based on the analysis of [8] and this paper, we recommend W on several grounds:…”
Section: Resultsmentioning
confidence: 99%
“…SEESAW can only handle models in the COCOMO format. Hence, we restrict ourselves to data in that format (see [8] for examples of W running on a much broader set of inputs).…”
Section: Comparing W To Seesawmentioning
confidence: 99%
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“…Case-based reasoning life cycle is comprised of 4 primary stages [16] i.e. 1) Retrieve searches for stored cases and determines whether the case is applicable to solving the problem at hand 2) Reuse applies the case from stage 1 to solve the problem at hand 3) Revise amends or adapts the problem-solving methods for optimal application to the problem at hand and 4) Retain keeps and records the solution methods applied to the new problem for additional case-based purposes.…”
Section: Case-based Reasoning (Cbr) Of the Tqf Advisory Systemmentioning
confidence: 99%