PREDICTING SOFTWARE ERRORS, DURING DEVELOPMENT, USING NONLINEAR-REGRESSION MODELS - A COMPARATIVE-STUDY
PREDICTING SOFTWARE ERRORS, DURING DEVELOPMENT, USING NONLINEAR-REGRESSION MODELS - A COMPARATIVE-STUDY
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DOI：
10.1109/24.159804
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发表时间：
1992-09-01
影响因子：
5.9
通讯作者：
RICHARDSON, GD
中科院分区：
文献类型：
--
作者：
KHOSHGOFTAAR, TM;BHATTACHARYYA, BB;RICHARDSON, GD
准确预测程序模块中的错误数量是大型软件系统质量控制中的一个主要问题。我们的技术是适合一个非线性回归模型的程序模块（因变量）中的故障数量在适当的软件度量。此模型将在软件开发的测试阶段开始时使用。我们的目标是，不建立一个明确的模型，但调查和评估的性能，4估计技术用于确定模型参数。文中给出了两个实例，软件危机使软件工程师的注意力集中在研究软件开发的系统化技术上，试图使软件系统更加可靠。这就需要更多的研究来建立更好的回归模型和估计技术。最小二乘法是软件可靠性工程师常用的估计模型参数的方法。然而，感知其他估计技术，如相对最小二乘（RLS），最小绝对值，最小相对误差（MRE）打开了一个广阔的新的频谱在我们的搜索，以获得具有上级预测质量的模型。从表中记录的平均相对误差（ARE）值的结果表明，RLS和MRE程序具有良好的性能，从预测能力的角度来看。此外，给出了这些估计方法在非线性模型参数估计中具有强相合性的充分条件，只要数据近似正态分布，则LS可能具有上级预测质量。然而，在大多数实际应用中，都存在与正态性的重大偏离;因此RLS和MRE似乎更加稳健。我们的研究结果表明，使用RLS和MRE估计，以确定故障倾向的程序模块的经验基础。
Accurately predicting the number of faults in program modules is a major problem in quality control of a large software system. Our technique is to fit a nonlinear regression model to the number of faults in a program module (dependent variable) in terms of appropriate software metrics. This model is to be used at the beginning of the test phase of software development. Our aim is, not to build a definitive model, but to investigate and evaluate the performance of 4 estimation techniques used to determine the model parameters. Two empirical examples are presented.The software crisis focuses attention of software engineers on the research of systematic techniques for software development in an attempt to make software systems more reliable. This calls for more research into building better regression models and estimation techniques. The method of least squares is widely used by software reliability engineers to estimate the parameters of the model. However, perception of other estimation techniques like relative least squares (RLS), least absolute value, and minimum relative error (MRE) opens a broad new spectrum in our search to obtain models possessing superior quality of prediction. Results from average relative error (ARE) values recorded in the tables suggest that RLS & MRE procedures possess good properties from the standpoint of predictive capability. Moreover, sufficient conditions are given to ensure that these estimation procedures demonstrate strong consistency in parameter estimation for nonlinear models.Whenever the data are approximately normally distributed, then LS may wry well possess superior predictive quality. However, in most practical applications there are important departures from normality; thus RLS & MRE appear to be more robust. Our findings suggest an empirical basis for use of RLS & MRE estimators in order to identify fault-prone program modules.