Over the last few decades, various software reliability growth models (SRGM) have been proposed, and in recent years, a gradual but marked shift has led to a focus on the balance between acceptable software reliability and affordable software testing costs. Chiu et al.(2008) proposed an SRGM from the perspective of learning effects that is more flexible in terms of fitting various software error data although it has a restrictive assumption of a constant number of potential errors. In this paper, we consider a software reliability growth model in which the number of potential errors varies over the debugging period since wrongly fixing an error may cause more errors, while correctly debugging one error may resolve others. However, such variations will gradually converge as the testing staff becomes more familiar with the software system. In order to describe this phenomenon, a sine function is introduced in this study to describe the time-dependent behavior of the number of potential errors with imperfect debugging, and the expected testing cost is thus evaluated to assist in the determination of an optimal software release policy. A numerical example is illustrated to verify the effectiveness of the proposed approach.
|Number of pages||18|
|Journal||International Journal of Industrial Engineering : Theory Applications and Practice|
|Publication status||Published - 2019 Jan 1|
All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering