A new modeling approach for the non-homogeneous Poisson processes (NHPPs) based software reliability modeling is proposed to describe the stochastic behavior of software fault-detection processes, of which the failure rate is not monotonic. The fundamental idea is to apply the Marshall-Olkin distribution to the software fault-detection time distribution. The applicability of Marshall-Olkin distribution in software reliability modeling is studied. The data fitting abilities of the proposed NHPP-based software reliability model is compared with the existing typical ones through real software project data analysis.

In recent years, considerable attention has been devoted to continuously running software systems whose performance characteristics are smoothly degrading in time. Software aging often affects the performance of a software system and eventually causes it to fail. A novel approach to handle transient software failures due to software aging is called software rejuvenation, which can be regarded as a preventive and proactive solution that is particularly useful for counteracting the aging phenomenon. In this paper, we focus on a high assurance software system with fault-tolerance and preventive rejuvenation, and analyze the stochastic behavior of such a highly critical software system. More precisely, we consider a fault-tolerant software system with two-version redundant structure and random rejuvenation schedule, and evaluate quantitatively some dependability measures like the steady-state system availability and MTTF based on the familiar Markovian analysis. In numerical examples, we examine the dependence of two fault tolerant techniques; design and environment diversity techniques, on the system dependability measures.

This paper presents the results of a continuing research work on the practical characterization of operating systems (OS) behavior in the presence of software faults in OS components, such as faulty device drivers. The methodology used is based on the emulation of software faults in device drivers and observation of the behavior of the overall system regarding a comprehensive set of failure modes, analyzed according to different dimensions related to multiple user perspectives. The emulation of the software faults is done through the injection of specific mutations at machine-code level that reproduce the code generated by compilers when typical programming errors occur in the high level language code. Two important aspects of this methodology are the independence of source code availability and the use of simple and established practices to evaluate operating systems failure modes, thus allowing its use as a dependability benchmarking technique. The generalization of the methodology to any software system built of discrete and identifiable components is also discussed.

The application of fault injection in the context of dependability benchmarking is far from being straightforward. One decisive issue to be addressed is to what extent injected faults are representative of the considered faults. This paper proposes an approach to analyze the effects of real and injected faults.

In this paper, we consider a simple development process consisting of design and debug phases, which is derived from actual concurrent development process for embedded software at a certain company. Then we propose two-phase project control that examines the initial development plan at the end of design phase, updates it to the current status of the development process and executes the debug phase under the new plan. In order to show the usefulness, we define three imaginary projects based on actually executed projects in a certain company: the project that executes debug phase under initial plan, the project that applies the proposed approach, and the project that follows a uniform plan. Moreover, to execute these projects, we use the project simulator, which has already been developed based on GSPN model. Judging from the number of residual faults in all products, we found that project B is the best among them.

In this paper, we propose a plausible software reliability growth model by applying a mathematical technique of stochastic differential equations. First, we extend a basic differential equation describing the average behavior of software fault-detection processes during the testing phase to a stochastic differential equation of ItÔ type, and derive a probability distribution of its solution processes. Second, we obtain several software reliability measures from the probability distribution. Finally, applying a method of maximum-likelihood we estimate unknown parameters in our model by using available data in the actual software testing procedures, and numerically show the stochastic behavior of the number of faults remaining in the software system. Further, the model is compared among the existing software reliability growth models in terms of goodness-of-fit.