Developing Interpretable Models with Optimized Set Reduction for Identifying High-Risk Software Components

By Lionel Claude Briand, Victor R. Basili, Christopher J. Hetmanski. 1993

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Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, background): “Research on interpretable models has a long history [8, 32, 7, 33], but with the availability of ever larger datasets and the adoption of computer-based decision making in all parts of society the need for interpretable machine learning models has only increased.”
The prediction of faulty classes using object-oriented design metrics (2001, background): “V7-02/11/99 11 rate [9][14][12][13] (also called consistency in [57][58]), the Kappa coefficient [12][14], Type I and Type II misclassifications [41][42][44][43] (Type I misclassification rate is f − 1 , and the Type II misclassification rate is s − 1 ), and some authors report sensitivity and speci”
Robust prediction of fault-proneness by random forests (2004, methodology): “These include, logistic regression [7], discriminant analysis [19, 28], the discriminative power techniques [29], Optimized Set Reduction [9], artiﬁcial neural network [18], fuzzy classiﬁcation [10], Bayesian Belief Networks [12] , genetic algorithms [6], classiﬁcation trees [13, 17, 30, 31], and,…”
Transfer learning for cross-company software defect prediction (2012, methodology): “With the software metric research advanced [24] [25] [26], there are a lot of paper using different methods on defect prediction, such as interpretable models [1], decision tree [2], Bayesian networks [3], and neural networks [4].”
A Simulation Study of the Model Evaluation Criterion MMRE (2003, methodology): “The latter methods include CART (Classification and Regression Trees) [4][5][7][35][50][48], OSR (Optimized Set Reduction) [2][3][29], Stepwise ANOVA (Analysis of Variance) [35], OLS (Ordinary Least Squares) regression (more than 30 studies, see [10] for an account), Robust regression [23] [24]…”
Reliability and validity in comparative studies of software prediction models (2005, methodology): “The latter include CART models [3], [5], [20], [35], OSR—Optimized Set Reduction, a subtype of CART [1], [2], [19], EBA models [17], [29], [33], [38], [41], [44], and, finally, ANN models [32], [35] [44].”
A large-scale empirical study of just-in-time quality assurance (2013, background): “To address this challenge, a plethora of software engineering research focuses on prioritizing software quality assurance activities [4], [6], [49].”
Software fault prediction: A literature review and current trends (2011, methodology): “Briand, Basili, and Hetmanski (1993) applied logistic regression, classification trees, optimized set reduction (OSR) methods on 146 components of an ADA system which consists of 260,000 lines of code.”
Predicting defect-prone software modules using support vector machines (2008, methodology): “, 1996), the discriminative power techniques (Schneidewind, 1992), optimized set reduction (Briand et al., 1993), genetic algorithms (Azar et al.”
Machine Learning and Software Engineering (2002, unknown): “, 2000), DT (Briand et al., 1993; Porter and Silbey, 1990), CL (de Almeida and Matwin, 1999), ILP (Cohen and Devanbu, 1997) Software size {NN, GP} (Dolado, 2000) Software development cost DT (Briand et al.”