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Software reliability prediction based on support vector regression with binary particle swarm optimization for model mining

机译：基于支持向量回归的软件可靠性预测模型挖掘的二元粒子群优化

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Data-Driven Software Reliability Modeling (DDSRM) is an approach in software reliability prediction problem which only relies on software failure data. There are two kinds of model architecture in this modeling, which are Single-Input Single-Output (SISO) and Multiple-Delayed-Input Single-Output (MDISO). In MDISO architecture, the prediction process involves having multiple inputs from the failure data to predict single output in the future. Most MDISO literatures use underlying assumption that a failure is correlated with a number of most recent failures. In more “generic” model of MDISO, a failure can be correlated with some of the previous failures. The process of searching which time lags to use as inputs in this model is sometimes referred to as a model mining process. This paper proposes to apply Binary Particle Swarm Optimization (BPSO) algorithm as model mining in software reliability prediction problem in terms of failure count number with Support Vector Regression (SVR) as predictor. Initial experiment shows that the proposed SVR-BPSO method yields more accurate prediction result than a prediction without model mining.
机译：数据驱动的软件可靠性建模（DDSRM）是软件可靠性预测问题的方法，其依赖于软件故障数据。此建模中有两种模型架构，其是单输入单输出（SISO）和多延迟输入的单输出（MDISO）。在MDISO架构中，预测过程涉及具有从故障数据的多个输入来预测未来的单个输出。大多数MDISO文献使用潜在的假设，其中失败与许多最新故障相关。在MDISO的更多“通用”模型中，失败可以与以前的一些故障相关。搜索使用作为该模型中的输入的时间滞后的过程有时被称为模型挖掘过程。本文提出将二进制粒子群优化（BPSO）算法应用于软件可靠性预测问题中的模型挖掘，其中失效计数数量与支持向量回归（SVR）作为预测器。初始实验表明，所提出的SVR-BPSO方法比没有模型挖掘的预测产生更精确的预测结果。

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