Research on data-driven software reliability models

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Research on data-driven software reliability models

Journal Title: Science Paper Online - Year 2007, Vol 2, Issue 10

Abstract

Traditional software reliability growth models (SRGMs) are generally based on several impractical assumptions, which to a large extend limits their applicability and accuracy. In recent years, data-driven approach to software reliability modeling has attracted a lot of attention, and several artificial neural network (ANN) based and support vector machine (SVM) based software reliability models (SRMs) have been proposed in the literature. Data-driven SRMs require no assumptions on the properties of software faults and software failure process, thus they appear to have wider applicability compared with SRGMs. For data-driven SRMs, software failure data used have great impact on model prediction accuracy; however, to the best of our knowledge, this issue has not been studied in the literature. In this paper, an SVM-based SRM is proposed. It is also demonstrated that for data-driven SRMs accumulative software failure data rather than inter-failure data should be used, and recent failure data rather than all historical failure data should be used. A genetic algorithm (GA) based algorithm for optimizing model parameters is proposed. Based on three failure data sets published in the literature which are taken from real-life software projects, comparative studies of the proposed SRM and existing data-driven SRMs are conducted. Results show that the proposed SRM seems to have the highest prediction accuracy.

Authors and Affiliations

Bo YANG, Hongzhong HUANG, Suchang GUO

Keywords

EP ID EP96781
DOI -
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