Empowering software reliability: Leveraging efficient fault detection and removal efficiency

Abstract

Advancements in science and technology have led to the widespread use of computer systems in various applications, emphasizing the importance of software reliability. Software failures can have severe consequences, making thorough testing crucial. Software reliability growth models (SRGMs) play a significant role in enhancing reliability by predicting improvement over time. This article introduces a comprehensive approach to software reliability that incorporates a dynamic fault detection rate, along with fault removal efficiency. The fault detection rate measures the rate at which faults are identified during testing, reflecting the effectiveness of the testing process. By incorporating this dynamic component, the model provides a more accurate estimation of software reliability and enables adaptive testing strategies and resource allocation. Achieving a high fault detection rate is desirable, but organizations must consider the cost implications and strike a balance between reliability and time-to-market constraints. This article extends the analysis to calculate the optimal release time and optimal warranty period that minimize development costs, subject to the desired reliability. By considering these factors, development teams can make informed decisions regarding the timing of software release and the duration of the warranty period, optimizing both reliability and cost.

Keywords:

• Fault detection rate
• fault removal efficiency
• mean value function
• non-homogeneous Poisson process
• optimal release policy
• software reliability growth model

Authors’ contributions

Umashankar Samal conducted all the research activities and calculations for this study. Additionally, Umashankar Samal took primary responsibility for drafting the manuscript and incorporating the research findings into a coherent narrative. Ajay Kumar provided valuable guidance and oversight throughout the research process. This included reviewing and proofreading the manuscript for clarity, accuracy, and scientific rigor.

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 50 research papers in reputed journals and conferences.