## 3 DePaaS: Architecture and Design
### SDP Models Provided by DePaaS  
The literature of software defect prediction includes more than eighty (80) SDP models grouped into five classes: statistical, traditional machine learning, novel machine learning, search based and hybridised techniques. * _Statistical SDP models_ are built on techniques such as linear regression, logistic regression, naive Bayes classifier, K-nearest neighbour, Bayesian networks, Discriminant Analysis, Correlation Analysis, Multivariate Adaptive Regression Splines (MARS), and Negative Binomial Regression (NBR), etc. Figure 1: Functional diagram of DePaaS. * _Traditional machine learning SDP models_ are built on machine learning techniques such as decision trees, Bayesian Learning, Ensemble Learning, Evolutionary Learning, Neural Networks, Support Vector Machines, and Rule Based Learning, etc. * _Novel machine learning SDP models_ include Batch Nested Generalized Exemplar (BNGE), Exemplar-Aided Constructor of Hyperrectangles (EACH) and Rule Induction from a Set of Exemplars (RISE), Partial Decision Trees (PART), PrUning and BuiLding Integrated in Classification (PUBLIC), Rule Induction with Optimal Neighborhood Algorithm (Riona) and Simple Learner with Iterative Pruning to Produce Error Reduction (SLIPPER), etc. * _Search-based (SBT) SDP models_ include Bioinformatics-oriented hierarchical evolutionary learning (BioHEL), CO-Evolutionary Rule Extractor (CORE), CHC Adaptative Search for Instance Selection (CHC), Generational Genetic Algorithm for Instance Selection (GGA), Population-Based Incremental Learning (PBIL), Steady-State Genetic Algorithm for Instance Selection (SGA), Constricted Particle Swarm Optimization (CPSO), Linear Decreasing Weight PSO (LDWPSO), Real Encoding PSO (REPSO), Bojarczuk GP, Falco GP, Tan-GP, GA based Classifier System with Adaptive Discretization Intervals (GA-ADI), GA based Classifier System with Interval Rules (GA-Int), Incremental Learning with GA (ILGA), GA for solving problem MAX-F (OlexGA), Supervised Inductive Algorithm (SIA), Supervised Classifier System (UCS), X-Classifier System (XCS), and Memetic Pittsburgh Learning Classifier System (MPLCS), etc. * _Hybridised (HBT) SDP models_ Decision Tree- Genetic Algorithm (DT-GA), Oblique Decision Tree with Evolutionary Learning (DT-Oblique), Tree Analysis with Randomly Generated and Evolved Trees (TARGET), Genetic Algorithm with MSE estimation (GA_MSE), Intelligent Genetic Algorithm for Edition (IGA), Genetic based Fuzzy Adaboost (GFS-AB), Genetic Fuzzy Learning with Logitboost (GFS-LB), Logitboost with Single Winner Inference (GFS-MaxLB), Fuzzy rule approach based on a genetic cooperative-competitive learning (GFS-GCCL), Fuzzy Learning based on GP (GFS-GP), Fuzzy Learning based on GP Grammar Operators (GFS-GPG), Grammar Operators and Simulated Annealing (GFS-SP), Hierarchical Decision Rules (HIDER), Neural Network Evolutionary Programming (NNEP), Steady-State GA for Extracting Fuzzy Classification Rules from Data (SGERD), and Structural Learning Algorithm in a Vague Environment with Feature Selection (SLAVE), etc. DePaaS, by design, can provide multiple SDP model describing its usage details and performance details. To start with, DePaaS aims to provide two best performing SDP models from three classes-machine learning, search-based and hybridised techniques. [PERSON] has compared the performance of such novel machine learning, search-based and hybridised SDP models [21]. Performance parameters (G-Mean and Balance) of the top performing SDP models intended to be included in DePaaS is provided in Table 1 along with the Friedman score as determined by [PERSON].