Discrete Software Reliability Growth Modeling for Errors of Different Severity Incorporating Change-point Concept

作     者：D.N.Goswami Sunil K.Khatri Reecha Kapur 

作者机构：School of Studies in Computer Science and ApplicationsJiwaji UniversityGwalior 474011India Mother Teresa Institute of ManagementGuru Gobind Singh Indraprastha UniversityDelhi110092India Department of Mathematics and Computer ApplicationsBundelkhand UniversityJhansi 284128India 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2007年第4卷第4期

页      面：396-405页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 081202[工学-计算机软件与理论] 

主　　题：Discrete software reliability growth model non-homogeneous Poisson process fault severity change point probability generating function. 

摘      要：Several software reliability growth models （SRGM） have been developed to monitor the reliability growth during the testing phase of software development. In most of the existing research available in the literatures, it is considered that a similar testing effort is required on each debugging effort. However, in practice, different types of faults may require different amounts of testing efforts for their detection and removal. Consequently, faults are classified into three categories on the basis of severity： simple, hard and complex. This categorization may be extended to r type of faults on the basis of severity. Although some existing research in the literatures has incorporated this concept that fault removal rate （FRR） is different for different types of faults, they assume that the FRR remains constant during the overall testing period. On the contrary, it has been observed that as testing progresses, FRR changes due to changing testing strategy, skill, environment and personnel resources. In this paper, a general discrete SRGM is proposed for errors of different severity in software systems using the change-point concept. Then, the models are formulated for two particular environments. The models were validated on two real-life data sets. The results show better fit and wider applicability of the proposed models as to different types of failure datasets.