{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T18:19:31Z","timestamp":1761675571545,"version":"3.34.0"},"reference-count":16,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2008,2]]},"abstract":"<jats:p>Estimation by analogy (EBA) predicts effort for a new project by learning from the performance of former projects. This is done by aggregating effort information of similar projects from a given historical data set that contains projects, or objects in general, and attributes describing the objects. While this has been successful in general, existing research results have shown that a carefully selected subset, as well as weighting, of the attributes may improve the performance of the estimation methods.<\/jats:p><jats:p>In order to improve the estimation accuracy of our former proposed EBA method AQUA, which supports data sets that have non-quantitative and missing values, an attribute weighting method using rough set analysis is proposed in this paper. AQUA is thus extended to AQUA<jats:sup>+<\/jats:sup>by incorporating the proposed attribute weighting and selection method. Better prediction accuracy was obtained by AQUA<jats:sup>+<\/jats:sup>compared to AQUA for five data sets. The proposed method for attribute weighting and selection is effective in that (1) it supports data sets that have non-quantitative and missing values; (2) it supports attribute selection as well as weighting, which are not supported simultaneously by other attribute selection methods; and (3) it helps AQUA<jats:sup>+<\/jats:sup>to produce better performance.<\/jats:p>","DOI":"10.1142\/s0218194008003532","type":"journal-article","created":{"date-parts":[[2008,4,21]],"date-time":"2008-04-21T09:18:43Z","timestamp":1208769523000},"page":"1-23","source":"Crossref","is-referenced-by-count":15,"title":["SOFTWARE EFFORT ESTIMATION BY ANALOGY USING ATTRIBUTE SELECTION BASED ON ROUGH SET ANALYSIS"],"prefix":"10.1142","volume":"18","author":[{"given":"JINGZHOU","family":"LI","sequence":"first","affiliation":[{"name":"Software Engineering Decision Support Laboratory, University of Calgary, Calgary AB, T2N1N4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"GUENTHER","family":"RUHE","sequence":"additional","affiliation":[{"name":"Software Engineering Decision Support Laboratory, University of Calgary, Calgary AB, T2N1N4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.2307\/249573"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1109\/32.637387"},{"journal-title":"Empirical Software Engineering","author":"Li J.","key":"rf3"},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1023\/A:1023062629183"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1023\/A:1015202115651"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(97)00043-X"},{"key":"rf9","doi-asserted-by":"publisher","DOI":"10.1109\/MS.2005.151"},{"key":"rf10","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-011-3534-4"},{"key":"rf13","doi-asserted-by":"publisher","DOI":"10.1023\/A:1011219601502"},{"key":"rf14","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1080\/00031305.1983.10483087","volume":"37","author":"Efron B.","journal-title":"The American Statistician"},{"key":"rf16","doi-asserted-by":"publisher","DOI":"10.1007\/s11334-005-0009-x"},{"key":"rf17","doi-asserted-by":"publisher","DOI":"10.1109\/32.962560"},{"key":"rf19","doi-asserted-by":"publisher","DOI":"10.1109\/32.965340"},{"volume-title":"Software Engineering Metrics and Models","year":"1986","author":"Conte S. 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