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Journal of Software:2019.30(6):1759-1777

Weibull分布引进故障的软件可靠性增长模型
王金勇,张策,米晓萍,郭新峰,李济洪
(山西大学 软件学院, 山西 太原 030006;哈尔滨工业大学(威海) 计算机科学与技术学院, 山东 威海 264209)
Software Reliability Growth Model Based on Weibull Distribution Introduced Faults
WANG Jin-Yong,ZHANG Ce,MI Xiao-Ping,GUO Xin-Feng,LI Ji-Hong
(School of Software Engineering, Shanxi University, Taiyuan 030006, China;School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai 264209, China)
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Received:April 17, 2017    Revised:September 30, 2017
> 中文摘要: 软件调试是复杂过程,可能会受到很多种因素的影响,例如调试资源分配、调试工具的使用情况、调试技巧等.在软件调试过程中,当检测到的故障被去除时,新的故障可能会被引进.因此,研究故障引进的现象对建立高质量的软件可靠性增长模型具有重要意义.但是到目前为止,模拟故障引进过程仍是一个复杂和困难的问题.虽然有许多研究者开发了一些不完美调试的软件可靠性增长模型,但是一般都是假设故障内容(总数)函数为线性、指数分布或者是与故障去除的数量成正比.这个假设与实际的软件调试过程中故障引进情况并不完全一致.提出一种基于Weibull分布引进故障的软件可靠性增长模型,考虑故障内容(总数)函数服从Weibull分布,并用相关的实验验证了提出的模型的拟合和预测性能.在用两个故障数据集进行的模拟实验中,实验结果指出:提出的模型和其他模型相比,有更好的拟合和预测性能以及更好的鲁棒性.
Abstract:Software debugging is a complex process and affected by many factors, such as debugging resources, debugging tools, debugging skills, etc. When detected faults were removed, new faults may be introduced. Therefore, it plays an important role to research an imperfect debugging phenomenon in the software debugging process. How to model fault introduction in building an imperfect debugging model is still an unresolved issue. So far, numerous software debugging models are developed by researchers, for example, assuming the fault content function is a linear, exponential distribution or proportional to the number of removed faults, etc. However, they can not entirely satisfy the realistic needs due to fault introduction complicated changes over time. In this study, an NHPP software reliability model is proposed based on Weibull distribution introduced faults and the fault content function following Weibull distribution is considered. The related experiment is carried out which validates the fitting and predictive power of the proposed model. The experimental results also show the proposed model has much better fitting and predictive performance than other models using two fault data sets, as well as better robustness.
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基金项目:山西省自然科学基金(201801D121120);NSFC-广东联合基金(U1501501);山西省软科学研究项目(2017041039-6) 山西省自然科学基金(201801D121120);NSFC-广东联合基金(U1501501);山西省软科学研究项目(2017041039-6)
Foundation items:Natural Science Foundation of Shanxi Province of China (201801D121120); NSFC-Guangdong Joint Fund Key Support Program of China (U1501501); Soft Science Research Projects of Shanxi Province of China (2017041039-6)
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王金勇,张策,米晓萍,郭新峰,李济洪.Weibull分布引进故障的软件可靠性增长模型.软件学报,2019,30(6):1759-1777

WANG Jin-Yong,ZHANG Ce,MI Xiao-Ping,GUO Xin-Feng,LI Ji-Hong.Software Reliability Growth Model Based on Weibull Distribution Introduced Faults.Journal of Software,2019,30(6):1759-1777