Genetic Algorithm Based Optimal Testing Effort Allocation Problem for Modular Software

Software reliability growth models (SRGM) are used to assess modular software quantitatively and predict the reliability of each of the modules during module testing phase. In the last few decades various SRGM’s have been proposed in literature. However, it is difficult to select the best model from a plethora of models available. To reduce this difficulty, unified modeling approaches have been proposed by many researchers. In this paper we present a generalized framework for software reliability growth modeling with respect to testing effort expenditure and incorporate the faults of different severity. We have used different standard probability distribution functions for representing failure observation and fault detection/ correction times. The faults in the software are labeled as simple, hard and complex faults. Developing reliable modular software is necessary. But, at the same time the testing effort available during the testing time is limited. Consequently, it is important for the project manager to allocate these limited resources among the modules optimally during the testing process. In this paper we have formulated an optimization problem in which the total number of faults removed from modular software is (which include simple, hard and complex faults) maximized subject to budgetary and reliability constraints. To solve the optimization problem we have used genetic algorithm. One numerical example has been discussed to illustrate the solution of the formulated optimal effort allocation problem. Index Terms Non-homogenous Poisson process, software reliability growth model, Probability Distribution Functions, Fault Severity, Genetic Algorithm