SOFTWARE RELIABILITY GROWTH MODELS WITH LEARNING BASED FAULT DETECTION RATE FUNCTION

In light of the growing importance of software dependability, researchers have researched the process of reliability growth in great detail to better forecast and analyze software reliability throughout the testing and debugging phases. Different fault detection rates are useful in different contexts. This research proposes a unified reliability model for analyzing SRGM across several failure datasets, with a particular focus on the effect of the fault detection rate. Using a power function with an S-type fault detection rate produces the greatest results in software reliability models, while a constant fault detection rate produces satisfactory results, and the models using an exponential fault detection rate perform poorly. Two models using a learning factor-based defect detection rate function are presented, keeping the constraints of debugging in mind. Our models are reliable. The second suggested LB-FDR model is the LB-FDR Model with Linear Fault Content (LFC), which was created since linear fault content debugging was found to be insufficient