Integrating Human Learning Factors and Bayesian Analysis Into Software Reliability Growth Models for Optimal Release Strategies

This study presents a Software Reliability Growth Model (SRGM) that incorporates imperfect debugging and employs Bayesian analysis to optimize the timing of software releases. The primary objective is to reduce software testing costs while enhancing the model's practical applicability. Asignificant limitation of traditional estimation techniques, such as MLE and LSE, is their challenge in accurately estimating model parameters when historical data is limited. To overcome this issue, the proposed Bayesian approach utilizes prior knowledge from domain experts and integrates available software testing data to predict both the software's reliability and associated costs. This method facilitates both prior and posterior analyses, making it effective even in scenarios with limited data. The model also considers the efficiency of the debugging process, which can be influenced by factors such as the testing team's learning curve and human error. By integrating these human elements and the intrinsic characteristics of the debugging process, the model becomes more comprehensive and realistic. This results in parameter estimates that more accurately represent real-world scenarios, making the model more intuitive for experts to apply. Additionally, the study incorporates numerical examples and sensitivity analyses that provide essential insights for management. These examples offer strategic guidance for software release decisions, assisting stakeholders in balancing the trade-offs between testing costs, reliability, and release timing. To further enhance decision-making, a computerized application system is proposed to help determine the optimal software release point. This tool streamlines the process, ensuring a more efficient approach to addressing this critical challenge in software development.