Evaluating logistic regression models to estimate software project outcomes

Information and Software Technology

Volumn 52, Issue 9, 2010, Pages 934-944

  Cerpa, Narciso ^a   Bardeen, Matthew ^a   Kitchenham, Barbara ^b   Verner, June ^c  

^a UNIVERSIDAD DE TALCA   (Chile)

^b KEELE UNIVERSITY   (United Kingdom)

^c UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Classifier evaluation; Cross company data; Project outcome; ROC analysis; Single company data; Tailored cut off

Indexed keywords

CLASSIFIER EVALUATION; CUT-OFF; DATA SETS; EMPIRICAL INVESTIGATION; FACTOR-BASED MODEL; HETEROGENEOUS DATA; HIGHLY-CORRELATED; LOGISTIC REGRESSION MODELS; MULTIPLE ORGANIZATIONS; PREDICTION ACCURACY; PREDICTION MODEL; PREDICTIVE CAPABILITIES; PRINCIPAL COMPONENTS ANALYSIS; PROBABILITY OF SUCCESS; PROJECT OUTCOMES; PROJECT SUCCESS; RECEIVER OPERATING CHARACTERISTIC ANALYSIS; ROC ANALYSIS; SOFTWARE DEVELOPMENT PROJECTS; SOFTWARE PROJECT;

CLASSIFIERS; COMPUTER SOFTWARE SELECTION AND EVALUATION; FORECASTING; MATHEMATICAL MODELS; PROJECT MANAGEMENT; REGRESSION ANALYSIS; SOFTWARE DESIGN;

PRINCIPAL COMPONENT ANALYSIS;

EID: 77953683193     PISSN: 09505849     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.infsof.2010.03.011     Document Type: Article

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