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Software reliability prediction based on support vector regression using a hybrid genetic algorithm and simulated annealing algorithm

机译：基于支持向量回归的混合遗传算法和模拟退火算法的软件可靠性预测

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摘要

Software reliability prediction is very important for minimising cost and improving the effectiveness of the software development process. As an important method, relative data during software lifecycle is used to analyse and predict software reliability. However, predicting the variability of software reliability with time is very difficult. Recently, support vector regression (SVR) has been widely applied to solve non-linear predicting problems in many fields such as software reliability prediction and has obtained well performance in many situations, and it is still difficult to select its parameters. Previously, intelligence optimisation algorithms, such as genetic algorithm (GA), are mostly used for finding better parameters of SVR, however existing methods of selecting parameters require usually has some disadvantages. In this study, to overcome weaknesses of GA, such as the local minima and the premature convergence problems, GA and simulated annealing (SA) are integrated into a new optimise algorithm, called GA-SA, it is then applied to SVR for predicting software reliability. The authors compare proposed GA-SA-SVR model with other software reliability models through real software failure data. The experimental results show that the proposed GA-SA-SVR model can obtain better predictions results than the other models and has a fairly accurate prediction capability.
机译：软件可靠性预测对于最小化成本和提高软件开发过程的有效性非常重要。作为一种重要的方法，软件生命周期中的相对数据用于分析和预测软件的可靠性。但是，预测软件可靠性随时间的变化非常困难。近年来，支持向量回归（SVR）已被广泛用于解决诸如软件可靠性预测等许多领域中的非线性预测问题，并且在许多情况下都具有良好的性能，但是仍然很难选择其参数。以前，智能优化算法（例如遗传算法（GA））通常用于查找SVR的更好参数，但是现有的参数选择方法通常存在一些缺点。在这项研究中，为了克服GA的弱点，例如局部极小值和过早收敛的问题，将GA和模拟退火（SA）集成到称为GA-SA的新优化算法中，然后将其应用于SVR预测软件可靠性。通过真实的软件故障数据，作者将提出的GA-SA-SVR模型与其他软件可靠性模型进行了比较。实验结果表明，所提出的GA-SA-SVR模型能够获得比其他模型更好的预测结果，并且具有相当准确的预测能力。

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  • 来源
    《Software, IET》 |2011年第4期|p.398-405|共8页
  • 作者

    Jin C.;

  • 作者单位

    Dept. of Comput. Sci., Central China Normal Univ., Wuhan, China;

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