Modeling and Analysis of Software Fault Detection and Correction Process by Considering Time Dependency
ABSTRACT Software reliability modeling & estimation plays a critical role in software development, particularly during the software testing stage. Although there are many research papers on this subject, few of them address the realistic time delays between fault detection and fault correction processes. This paper investigates an approach to incorporate the time dependencies between the fault detection, and fault correction processes, focusing on the parameter estimations of the combined model. Maximum likelihood estimates of combined models are derived from an explicit likelihood formula under various time delay assumptions. Various characteristics of the combined model, like the predictive capability, are also analyzed, and compared with the traditional least squares estimation method. Furthermore, we study a direct, useful application of the proposed model & estimation method to the classical optimal release time problem faced by software decision makers. The results illustrate the effect of time delay on the optimal release policy, and the overall software development cost.
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ABSTRACT: Software is currently a key part of many safety-critical and life-critical application systems. People always need easy- and instinctive-to-use software, but the biggest challenge for software engineers is how to develop software with high reliability in a timely manner. To assure quality, and to assess the reliability of software products, many software reliability growth models (SRGMs) have been proposed in the past three decades. The practical problem is that sometimes these selected SRGMs by companies or software practitioners disagree in their reliability predictions, while no single model can be trusted to provide consistently accurate results across various applications. Consequently, some researchers have proposed to use combinational models for improving the prediction capability of software reliability. In this paper, three enhanced weighted-combinations, namely weighted arithmetic, weighted geometric, and weighted harmonic combinations, are proposed. To solve the problem of determining proper weights for model combinations, we further study how to incorporate enhanced genetic algorithms (EGAs) with several efficient operators into weighted assignments. Experiments are performed based on real software failure data, and numerical results show that our proposed models are flexible enough to depict various software development environments. Finally, some management metrics are presented to both assure software quality and determine the optimal release strategy of software products under development.IEEE Transactions on Reliability 09/2014; 63(3):731-749. · 1.66 Impact Factor
Conference Paper: Software Relialibility Markovian Model Based on Phase-Type Distribution[Show abstract] [Hide abstract]
ABSTRACT: The paper focuses on creating of a software reliability model based on phase type distribution. Usually, the length of intervals between the moments of fault detection and correction have unknown distributions. In this paper, a new approach how to approximate any distribution of positive random variable by mixture and convolution of exponential phases, known as the general type of phase-type distribution, is proposed. The optimization algorithm of Local Unimodal Sampling (LUS) is applied to estimate parameters of phase-type distribution. After such procedure, the dynamics of a software reliability model can be described by a continuous time absorbing Markov chain. The probabilities of the resulting absorbing Markov chain are used to compute performance measures of the software reliability model.ICSEA 2014 : The Ninth International Conference on Software Engineering Advances; 10/2014
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ABSTRACT: Several software reliability growth models based on nonhomogeneous Poisson process (NHPP) have been presented in the literature in the last three decades. However, existing software reliability growth models have the problem that the considerations of imperfect debugging phenomenon are incompletely. Based on the two important aspects that new faults introduced by fault debugging and incompletely fault debugging, in this paper, we proposed a software reliability growth model in which the total number of fault and fault removal efficient will change over time. Examined by a group of public failure data set, the proposed model is considered to be a model with goodness-of-fit and the predictive power.Software Engineering and Service Science (ICSESS), 2013 4th IEEE International Conference on; 01/2013