An S-shaped software reliability model with imperfect debugging and improved testing learning process

Roy, Pratik; Mahapatra, G. S.; Dey, Kashi Nath

doi:10.1504/ijrs.2013.057423

Cited by 14 publications

(11 citation statements)

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“…We consider Yamada model [6], where α is time-dependent exponentially increasing function, and nature of fault introduction rate is exponential. Further, we consider PNZ model [9] where αfalse(tfalse) is time-dependent, and nature of fault introduction rate is linearly increasing function, and also the Roy model [55] which presented that the fault introduction rate is constant.…”

Section: Main Textmentioning

confidence: 99%

“…Data Set-3 (DS3): DS3 (Telecommunication System Data [9]) was pair of the time of observation and the cumulative number of faults detected during 21 days of testing, measured on basis of the number of shifts spent running test cases, and analyzing the results 43 faults were detected.…”

Section: Main Textmentioning

confidence: 99%

“…Reliability is certainly the prime factor to be claimed for study of any discipline of engineering as it quantitatively measures quality, and the quantity can be properly engineered ([5], [6], [7]). Software reliability is a propensity to improve and it can be treated as a growth factor during the testing process ([4], [5], [7], [9], [10], [12], [13], [14]).…”

Section: Introductionmentioning

confidence: 99%

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A novel approach of NPSO on dynamic weighted NHPP model for software reliability analysis with additional fault introduction parameter

Rani

Mahapatra²

2019

Heliyon

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This paper presents software fault detection, which is dependent upon the effectiveness of the testing and debugging team. A more skilled testing team can achieve higher rates of debugging success, and thereby removing a larger fraction of faults identified without introducing additional faults. A complex software is often subject to two or more stages of testing that exhibits distinct rates of fault discovery. This paper proposes a two-stage Enhanced neighborhood-based particle swarm optimization (NPSO) technique with the assimilation of the three conventional non homogeneous Poisson process (NHPP) based growth models of software reliability by introducing an additional fault introduction parameter. The proposed neuro and swarm recurrent neural network model is compared with similar models, to demonstrate that in some cases the additional fault introduction parameter is appropriate. Both the theoretical and predictive measures of goodness of fit are used for demonstration using data sets through NPSO.

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Section: Main Textmentioning

confidence: 99%

Section: Main Textmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel approach of NPSO on dynamic weighted NHPP model for software reliability analysis with additional fault introduction parameter

Rani

Mahapatra²

2019

Heliyon

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“…The failure intensity function, ie, λ ( t ), or mean value function, ie, m ( t ), is the building block of all NHPP models , which represents the number of faults detected per unit testing time. One simple class of NHPP is the mean value function model, which has an exponential growth of the cumulative number of failures experienced.…”

Section: Introductionmentioning

confidence: 99%

Neural network for software reliability analysis of dynamically weighted NHPP growth models with imperfect debugging

Rani¹,

Mahapatra²

2018

Software Testing Verif & Rel

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This paper propose a learning algorithm of supervised back-propagation neural networks for dynamic weighted combination of software reliability model. The proposed model is an assimilation of 3 well-known non-homogeneous poisson process (NHPP)-based software reliability growth models with imperfect debugging. The novel approach of proposed supervised back propagation-based neural network 2-stage architecture has a great impact on the network by combining the imperfect debugging models based on the nature of fault introduction rate during testing and debugging. Function approximation metrics are used for comparing the proposed model with individual models. Three data sets are trained using supervised back-propagation neural networks to compare the performance and validity evaluation of proposed and existing NHPP models and dynamic weighted combinational model. Reliability analysis among important NHPP models incorporating imperfect debugging is illustrated through numerical and graphical explanation of several metrics using supervised back-propagation neural networks. KEYWORDS imperfect debugging, fault introduction rate, neural networks, non-homogenous poisson process, software reliability growth model, supervised back-propagation algorithm INTRODUCTIONSoftware plays a vital role in every aspect of technological world with its ever-increasing dependency on technology, and its size and complexity have dramatically increased many folds [1]. Developers are facing a lot of problems to develop a trustworthy and reliable software for the user. A reliable software must meet or exceed the specified usage period, under specified operating conditions in a manner that satisfies customer's expectations.Software reliability is quantitative measurement of quality [2][3][4].Software cannot be developed error-free because of the uncertainties and amount of manual effort involved in the software development process. Specifically, erroneous software incurs high work costs. On the other hand, debugging and testing reduces the error content[5] but increases the development cost [6]. Software failure occurs because of hidden bugs in the software instead of immediate fixes for detected errors during development, but new errors may be introduced during debugging. Some reliability models have also been developed to address fault detection; correction [7, 8]; testing coverage [9]; and imperfect debugging processes [10, 11].Many factors such as software development methodology and complexity of the software are strongly dependant on making assumptions that influences the behaviour of software reliability growth. The software reliability measurement is based on type of data set [1]; therefore, traditional statistical theory plays a vital role in software reliability modelling. There are many constraints on existing statistical models that are based on prior assumptions in software testing. Neural network (NN) approaches can be applied to estimate and predict the software reliability. Neural network methods recommend far better models than ...

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“…Xiao and Dohi 28 proposed a novel modeling approach for the NHPP-based software reliability models to describe the stochastic behavior of software fault detection processes. Roy et al 29 proposed an NHPP-based S-shaped SRGM in presence of imperfect debugging with a new exponentially increasing fault content function and S-shaped fault detection rate.…”

Section: Introductionmentioning

confidence: 99%

An NHPP Software Reliability Growth Model With Imperfect Debugging and Error Generation

Roy

Mahapatra²,

Dey

2014

Int. J. Rel. Qual. Saf. Eng.

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In this paper, we propose a non-homogeneous Poisson process (NHPP) based software reliability growth model (SRGM) in the presence of modified imperfect debugging and fault generation phenomenon. The testing team may not be able to remove a fault perfectly on observation of a failure due to the complexity of software systems and incomplete understanding of software, and the original fault may remain, or get replaced by another fault causing error generation. We have proposed an exponentially increasing fault content function and constant fault detection rate. The total fault content of the software for our proposed model increases rapidly at the beginning of the testing process. It grows gradually at the end of the testing process because of increasing efficiency of the testing team with testing time. We use the maximum likelihood estimation method to estimate the unknown parameters of the proposed model. The applicability of our proposed model and comparisons with established models in terms of goodness of fit and predictive validity have been presented using five known software failure data sets. Experimental results show that the proposed model gives a better fit to the real failure data sets and predicts the future behavior of software development more accurately than the traditional SRGMs.

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An S-shaped software reliability model with imperfect debugging and improved testing learning process

Cited by 14 publications

References 0 publications

A novel approach of NPSO on dynamic weighted NHPP model for software reliability analysis with additional fault introduction parameter

A novel approach of NPSO on dynamic weighted NHPP model for software reliability analysis with additional fault introduction parameter

Neural network for software reliability analysis of dynamically weighted NHPP growth models with imperfect debugging

An NHPP Software Reliability Growth Model With Imperfect Debugging and Error Generation

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