A Machine Learning Approach for Quantifying the Design Error Propagation in Safety Critical Software System

Abstract

In general, the safety critical systems are zero error tolerance systems, designed with the high precision approach and with maximum perfection. Hence the authors attempted to create a flawless design by analyzing the various components including error occurrence at low-level design of making software. In view of this, the migration of design defects is quantified from origin to multiple states through hidden markov model approach. Here the probabilistic natures of selected defects by observing the operation of anti-lock braking system in various scenarios are modeled. It is observed that this model supports in identifying and quantifying the behavioral properties of selected errors while interacting with subsystems. The behavior of software is determined in terms of hidden state sequence. The sensitivity and precision quotient are measured for goodness-of-fit. This approach of early analysis of software hidden design errors will enhance the precision in producing any of the safety critical systems in practice.

Keywords:

• Critical
• Safety
• HMM
• ABS
• Error

Additional information

Notes on contributors

R. Bharathi

R Bharathi did postgraduate degree in computer science and engineering. She has a progressive teaching experience of 18 years. Her current research work focuses on safety critical software systems, machine learning, computational intelligence, software design quality estimation. Corresponding author. Email: [email protected]

R. Selvarani

R Selvarani is currently working as professor having a progressive teaching experience of 27 years and program director for doctoral program at Alliance College of Engineering and Design, Alliance University. She did her PhD in Jawaharlal Nehru Technological University, Hyderabad in the year 2009. She has published her research work in several peer-reviewed journals such as Elsevier, Info comp, Inderscience, ACM SIGSOFT, IEEE Conf. etc. and has a patent in software architecture and design domain. Her publication in Elsevier Journal was selected in terms of having “the best content” in the area of Information Technology for the year 2012 by VERTICAL NEWS, USA. Her name is listed in Who’s Who for Science and Technology, USA. Email: [email protected]