Redefining software reliability modeling: embracing fault-dependency, imperfect removal, and maximum fault considerations

Abstract

Software reliability is a critical aspect of ensuring the quality and dependability of software systems. However, existing software reliability models often make assumptions that do not align with real-world scenarios, such as perfect fault removal and independent faults. In this paper, we address this gap by developing a software reliability model that considers fault-dependent detection, imperfect fault removal, and the maximum number of faults that may present in the system. By accounting for these factors, our proposed model aims to provide a more accurate representation of software reliability. We evaluate the effectiveness of our model by comparing it to existing models using three commonly used goodness-of-fit criteria. The results demonstrate the importance of incorporating these considerations in software reliability modeling and highlight the superiority of our approach in capturing the complexities associated with software faults. Additionally, this paper conducts an analysis of optimal release planning, which yields highly encouraging results for software managers and engineers. This analysis adds significant value to the existing literature, further emphasizing the practical relevance of our proposed model.

Keywords:

• imperfect fault removal
• mean value function
• non-homogeneous Poisson process
• release planning
• software reliability growth model

Disclosure statement

The authors declare no conflicts of interest or disclosures to report.

Additional information

Funding

The authors declare that this research was conducted without any specific grant or funding support.

Notes on contributors

Umashankar Samal

Umashankar Samal graduated with a master’s degree in Mathematics in 2019 from Sant Longowal Institute of Engineering &Technology, India. Currently, he is a research scholar at Atal Bihari Vajpayee-Indian Institute of Information Technology and Management, Gwalior, India. His research interests include safety, quality, and reliability engineering.

Ajay Kumar

Ajay Kumar joined ABV-IIITM, Gwalior in July 2009 and now he is an associate professor at Department of Applied Sciences, ABV-IIITM, Gwalior. He obtained his MSc degree in Industrial Mathematics and Informatics from the Department of Mathematics, IIT Roorkee in 2003, and his Ph.D. in Reliability of Industrial Systems from the Department of Mathematics, IIT Roorkee, in 2009. His primary areas of interest are Reliability, Statistics, Fuzzy Sets, Fuzzy Logic, Optimization, Machine Learning, and Modeling & Simulation. He has published over 45 research papers in reputed journals and conferences.