Using Machine Learning for Risky Module Estimation of Safety-Critical Software

With the rapid development of digital computer and information processing technologies, nuclear I and C (Instrument and Control) system which needs safety critical function has adopted digital technologies. Software used in safety-critical system must have high dependability. Highly dependable software needs strict software testing and V and V activities. These days, regulatory demands for nuclear power plants are more and more increasing. But, human resources and time for regulation are limited. So, early software risky module prediction is very useful for software testing and regulation activities. Early estimation can be built from a collection of internal metrics during early development phase. Internal metrics are measures of a product derived from assessment of the product itself, and external metrics are measures of a product derived from assessment of the behavior of the systems. Internal metrics can be collected more easily and early than external metrics. In addition, internal metrics can be useful for estimating fault-prone software modules using machine learning. In this paper, we introduce current research status and techniques related to estimating risky software module using machine learning techniques. Section 2 describes the overview of the estimation model using machine learning and section 3 describes processes of the estimation model. Section 4 describes several estimation models using machine leanings. Section 5 concludes the paper