A novel search-based approach to software quality modeling with multiple software project repositories is presented. Training a software quality model with only one software measurement and defect data set may not effectively encapsulate quality trends of the development organization. The inclusion of additional software projects during the training process can provide a cross-project perspective on software quality modeling and prediction. The genetic-programming-based approach includes three strategies for modeling with multiple software projects: Baseline Classifier, Validation Classifier, and Validation-and-Voting Classifier. The latter is shown to provide better generalization and more robust software quality models. This is based on a case study of software metrics and defect data from seven real-world systems. A second case study considers 17 different (nonevolutionary) machine learners for modeling with multiple software data sets. Both case studies use a similar majority-voting approach for predicting fault-proneness class of program modules. It is shown that the total cost of misclassification of the search-based software quality models is consistently lower than those of the non-search-based models. This study provides clear guidance to practitioners interested in exploiting their organization's software measurement data repositories for improved software quality modeling.

A key factor in the success of a software project is achieving the best-possible software reliability within the allotted time & budget. Classification models which provide a risk-based software quality prediction, such as fault-prone & not fault-prone, are effective in providing a focused software quality assurance endeavor. However, their usefulness largely depends on whether all the predicted fault-prone modules can be inspected or improved by the allocated software quality-improvement resources, and on the project-specific costs of misclassifications. Therefore, a practical goal of calibrating classification models is to lower the expected cost of misclassification while providing a cost-effective use of the available software quality-improvement resources. This paper presents a genetic programming-based decision tree model which facilitates a multi-objective optimization in the context of the software quality classification problem. The first objective is to minimize the "Modified Expected Cost of Misclassification", which is our recently proposed goal-oriented measure for selecting & evaluating classification models. The second objective is to optimize the number of predicted fault-prone modules such that it is equal to the number of modules which can be inspected by the allocated resources. Some commonly used classification techniques, such as logistic regression, decision trees, and analogy-based reasoning, are not suited for directly optimizing multi-objective criteria. In contrast, genetic programming is particularly suited for the multi-objective optimization problem. An empirical case study of a real-world industrial software system demonstrates the promising results, and the usefulness of the proposed model

The knowledge, prior to system operations, of which program modules are problematic is valuable to a software quality assurance team, especially when there is a constraint on software quality enhancement resources. A cost-effective approach for allocating such resources is to obtain a prediction in the form of a quality-based ranking of program modules. Subsequently, a module-order model (MOM) is used to gauge the performance of the predicted rankings. From a practical software engineering point of view, multiple software quality objectives may be desired by a MOM for the system under consideration: e.g., the desired rankings may be such that 100% of the faults should be detected if the top 50% of modules with highest number of faults are subjected to quality improvements. Moreover, the management team for the same system may also desire that 80% of the faults should be accounted if the top 20% of the modules are targeted for improvement. Existing work related to MOM(s) use a quantitative prediction model to obtain the predicted rankings of program modules, implying that only the fault prediction error measures such as the average, relative, or mean square errors are minimized. Such an approach does not provide a direct insight into the performance behavior of a MOM. For a given percentage of modules enhanced, the performance of a MOM is gauged by how many faults are accounted for by the predicted ranking as compared with the perfect ranking. We propose an approach for calibrating a multiobjective MOM using genetic programming. Other estimation techniques, e.g., multiple linear regression and neural networks cannot achieve multiobjective optimization for MOM(s). The proposed methodology facilitates the simultaneous optimization of multiple performance objectives for a MOM. Case studies of two industrial software systems are presented, the empirical results of which demonstrate a new promise for goal-oriented software quality modeling.

The knowledge of the likely problematic areas of a software system is very useful for improving its overall quality. Based on such information, a more focused software testing and inspection plan can be devised. Decision trees are attractive for a software quality classification problem which predicts the quality of program modules in terms of risk-based classes. They provide a comprehensible classification model which can be directly interpreted by observing the tree-structure. A simultaneous optimization of the classification accuracy and the size of the decision tree is a difficult problem, and very few studies have addressed the issue. This paper presents an automated and simplified genetic programming (gp) based decision tree modeling technique for the software quality classification problem. Genetic programming is ideally suited for problems that require optimization of multiple criteria. The proposed technique is based on multi-objective optimization using strongly typed GP. In the context of an industrial high-assurance software system, two fitness functions are used for the optimization problem: one for minimizing the average weighted cost of misclassification, and one for controlling the size of the decision tree. The classification performances of the GP-based decision trees are compared with those based on standard GP, i.e., S-expression tree. It is shown that the GP-based decision tree technique yielded better classification models. As compared to other decision tree-based methods, such as C4.5, GP-based decision trees are more flexible and can allow optimization of performance objectives other than accuracy. Moreover, it provides a practical solution for building models in the presence of conflicting objectives, which is commonly observed in software development practice.