Empirical assessment of machine learning based software defect prediction techniques

The wide-variety of real-time software systems, including telecontrol/telepresence systems, robotic systems, and mission planning systems, can entail dynamic code synthesis based on runtime mission-specific requirements and operating conditions. This necessitates the need for dynamic dependability assessment to ensure that these systems will perform as specified and will not fail in catastrophic ways. One approach in achieving this is to dynamically assess the modules in the synthesized code using software defect prediction techniques. Statistical models, such as Stepwise Multi-linear Regression models and multivariate models, and machine learning approaches, such as Artificial Neural Networks, Instance-based Reasoning, Bayesian-Belief Networks, Decision Trees, and Rul