Omar Shatnawi

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Most non-homogenous Poisson process (NHPP) based software reliability growth models (SRGMs) presented in the literature assume that the faults in the software are of the same type. This assumption implies that the fault removal rate per remaining faults is independent of the testing time. However, this assumption is not truly representative of reality. It(More)
Several software reliability growth models (SRGMs) have been presented in the literature in the last three decades. These SRGMs take into account different testing environment depending on size and efficiency of testing team, type of components and faults, design of test cases, software architecture etc. The plethora of models makes the model selection an(More)
Nonhomogeneous poisson process based software reliability growth models are generally classified into two groups. The first group contains models, which use the machine execution time or calendar time as a unit of fault detection/removal period. Such models are called continuous time models. The second group contains models, which use the number of test(More)
In the software reliability engineering literature, few attempts have been made to measure software reliability using discrete time modeling. One of the reasons can be attributed to the mathematical complexity involved in constructing such models. The proposed unified modelling approach provides a broad framework for developing NHPP type of discrete SRGMs.(More)
Software reliability is defined as the probability of failure–free software operation for a specified period of time (American National Standards Institute – ANSI). It quantifies the failures of software systems and is the key factor in software quality [19]. It is also a major subject of Software Reliability Engineering (SRE) – a discipline which(More)
There exist various Software Reliability Growth models (SRGMs) in the software reliability engineering literature which assume diverse testing environments like distinction between failure and removal processes, learning of the testing personnel, possibility of imperfect debugging and error generation etc. But most of them are based upon constant or(More)