A stochastic software reliability model with imperfect-debugging and change-point

Shyur, Huan‐Jyh

doi:10.1016/s0164-1212(02)00071-7

Cited by 140 publications

(75 citation statements)

References 7 publications

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“…He established that incorporation of change point in SRGMs is vital for effective fault detection modeling. Shyur [23], Wang and Wang [25] also made offerings in this area. In accumulation, some studies included change-point analysis in their models as the testing resource consumption may not be smooth over time [4,10,15].…”

Section:  Debugging Group (Programmers\failure Rectification Personnmentioning

confidence: 99%

A study of Optimal Testing Resource Allocation Problem for Modular Software with Change Point

Kaur

Aggarwal

Kedia³

2018

Annals of Computer Science and Information Systems

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Abstract-For evolving trustworthy software, engrossing on uncovering process of fault in software is central. Nevertheless, during testing, modifications in the testing routine, defect gravity or testing-skill maturity and working environment, there can be notable change in fault detection rate. When this sort of pattern is observed in testing time it is called change point. In this article, we inquire a resource distribution problem that optimally distributes software developing resources in such a way that the cost of development is curtailed to optimization. In this problem, for all modules the effect of chief circumstantial element of change-point is considered. The constraint of pulling off the desired reliability level for every individual module is also incorporated in the formulation of the problem. A framework based on Karush Kuhn Tucker (KKT) conditions is presented to work out the resulting non-linear optimization problem. A simulated numerical illustration has been analyzed to reflect the formulation of the case and its solution by the algorithm proposed.

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Section:  Debugging Group (Programmers\failure Rectification Personnmentioning

confidence: 99%

A study of Optimal Testing Resource Allocation Problem for Modular Software with Change Point

Kaur

Aggarwal

Kedia³

2018

Annals of Computer Science and Information Systems

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“…This phenomenon is called imperfect debugging. Previous studies [13,16,18,2] incorporated the concept of imperfect debugging. Recently, Ahmad et al [1] analysed a srgm by considering the log-logistic testing effort and imperfect debugging.…”

Section: C184mentioning

confidence: 99%

“…Changing these factors during the software testing phase is called a change point. Shyur [16] studied a srgm with imperfect debugging and a change point. The environmental effects of a change point on the srgm were evaluated by Zhao et al [20].…”

Section: C184mentioning

confidence: 99%

Cost optimization of a software reliability growth model with imperfect debugging and a fault reduction factor

Manjula

Jain²,

Gulati³

2014

ANZIAMJ

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In modern society people depend on both hardware and software systems. A software system is embedded in every activity of a computer system. The desired performance of a software system is an important issue for many critical systems. Over the past decades, many software models were proposed for estimating the growth of reliability. To improve software quality, software reliability growth models (srgm) play an important role. The present investigation deals with a srgm with imperfect debugging, change points and a fault reduction factor (frf). A frf is the net number of faults removed in proportion to the failures experienced. This article proposes a new scheme for constructing a srgm based on a non-homogeneous Poisson process by

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Section: Introductionmentioning

confidence: 99%

“…An insight into imperfect debugging can be found in Obha [3,7], Pham [8], Kapur and Younes [9], Shyur [10] and Chiu and Huang [11]. Other realistic assumptions for running environment, testing/debugging strategies and resource allocation can also affect the reliability growth as discussed in Chiu and Huang [11] and Shyur [10]. Many researchers have used NHPP based SRGMs to capture the reliability growth of a software from the processes of testing and debugging [23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Some Software Reliability Growth Models with Learning Effects

Iqbal¹

2016

IJMSC

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A newly developed software system before its deployment is subjected to vigorous testing so as to minimize the probability of occurrence of failure very soon. Software solutions for safety critical and mission-critical application areas need a much focused level of testing. The testing process is basically carried out to build confidence in the software for its use in real world applications. Thus, reliability of systems is always a matter of concern for us. As we keep on performing the error detection and correction process on our software, the reliability of the system grows. In order to model this growth in the system reliability, many formulations in Software Reliability Growth Models (SRGMs) have been proposed including some based on NonHomogeneous Poisson Process (NHPP). The role of human learning and experiential pattern gains are being studied and incorporated in such models. The realistic assumptions about human learning behavior and experiential gains of new skill-sets for better detection and correction of faults on software are being incorporated and studied in such models. In this paper, a detailed analysis of some select SRGMs with learning effects is presented based on use of seven data sets. The estimation of parameters and comparative analysis based on goodness of fit using seven data sets are presented. Moreover, model comparisons on the basis of total defects predicted by the select models are also tabulated.Index Terms: Software Reliability, Software Reliability Growth Model (SRGM), Non-Homogeneous Poisson Process (NHPP), Learning effect, two-type learning effect.

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A stochastic software reliability model with imperfect-debugging and change-point

Cited by 140 publications

References 7 publications

A study of Optimal Testing Resource Allocation Problem for Modular Software with Change Point

A study of Optimal Testing Resource Allocation Problem for Modular Software with Change Point

Cost optimization of a software reliability growth model with imperfect debugging and a fault reduction factor

Analysis of Some Software Reliability Growth Models with Learning Effects

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