Empowering software reliability: Leveraging efficient fault detection and removal efficiency

• Published in: Quality engineering Vol. 37; no. 1; pp. 118 - 129
• Main Authors: Samal, Umashankar, Kumar, Ajay
• Format: Journal Article
• Language: English
• Published: Milwaukee Taylor & Francis 02.01.2025; Taylor & Francis Ltd
• Subjects: Fault detection; Fault detection rate; fault removal efficiency; Growth models; mean value function; non-homogeneous Poisson process; optimal release policy; Optimization; Resource allocation; Software reliability; software reliability growth model; System reliability; Warranties
• ISSN: 0898-2112; 1532-4222
• DOI: 10.1080/08982112.2024.2358889

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.