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

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

doi:10.1142/s0218539314500089

Cited by 35 publications

(21 citation statements)

References 32 publications

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“…Roy et al. proposed an non‐homogenous poisson process‐based SRGM in the presence of modified imperfect debugging and error generation phenomenon. Zhu and Pham proposed a two‐phase model, which considers software fault dependency and imperfect fault removal.…”

Section: Related Workmentioning

confidence: 99%

“…Many scholars and experts have developed software reliability growth models (SRGMs), based on the faults and failures present in the software development life cycle. In general, well‐developed stochastic processes such as SRGMs define software failures as a random process characterized by either time to failure or the failure counts at a specific time . The optimization of the cost and time for any software is inseparable from the question of software reliability.…”

Section: Introductionmentioning

confidence: 99%

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An improved software reliability prediction model by using high precision error iterative analysis method

Jabeen

Luo

Afzal

2019

Software Testing Verif & Rel

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Software reliability deals with the probability that software will not cause the failure of a system in a specified time interval. Software reliability growth models (SRGMs) are used to predict future behaviour from known characteristics of software, like historical failures. With the increasing demand to deliver quality software, more accurate SRGMs are required to estimate the software release time and cost of the testing effort. Software failure predictions at early phases also provide an opportunity for investing in proper quality assurance and upfront resource planning. Up till now, many parametric software reliability growth models (PSRGMs) have been proposed. However, several limitations of them mean that their predictive capacities differ from one dataset to others. In this paper, to enhance the prediction accuracy of existing PSRGMs, a high precision error iterative analysis method (HPEIAM) has been proposed based on the residual errors. In HPEIAM, residual errors from the estimated results of SRGMs are considered as another source of data that can combine the residual error modification with artificial neural network sign estimator. The repeated computation of residual errors by SRGMs improves and corrects the prediction accuracy up to the expected level. The performance of HPEIAM is tested with several PSRGMs using two sets of real software failure data based on three performance criteria. Moreover, we have compared the estimated failures predicted by HPEIAM with genetic algorithm (GA)-based prediction improvement. The results demonstrate that HPEIAM gives an improvement in goodness-of-fit and predictive performance for every PSRGM in initial few iterations.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved software reliability prediction model by using high precision error iterative analysis method

Jabeen

Luo

Afzal

2019

Software Testing Verif & Rel

View full text Add to dashboard Cite

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“…Specifically, erroneous software incurs high work costs. On the other hand, debugging and testing reduces the error content but increases the development cost . 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.…”

Section: Introductionmentioning

confidence: 99%

“…For the past 3 decades, research has been emphasized on non‐homogeneous poisson process (NHPP) model, which is used as quite a successful tool in realistic prediction of software reliability ‐. NHPP models consider the debugging process as a counting process characterized by its mean value function.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the NHPP models have been developed for estimating reliability growth of software product during the software‐testing phase based on assumption of imperfect debugging. Roy et al proposed an NHPP‐based S‐shaped Software Reliability Growth Model (SRGM) in presence of imperfect debugging with a new exponentially increasing fault content function. The parameter estimation through the autonomous error‐detected factor was explained by Wen .…”

Section: Introductionmentioning

confidence: 99%

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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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Selection of optimal software reliability growth models using an integrated entropy–Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach

Kumar

Saxena

Garg

2021

Math Methods in App Sciences

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A large number of software reliability growth models (SRGMs) have been studied to estimate the reliability of software systems over the past 40 years. Different models have been developed upon different sets of assumptions. A few models were developed in a practical environment by considering testing effort, testing coverage, time delay fault correction, and fault reduction factor. Generally, SRGMs are not dataset independent, and thus the selection of an appropriate SRGM for use in a specific application is a challenging task in the software reliability area. The wrong selection of SRGMs might create a wrong estimation of reliability and consequently a delay in the release of the software. To overcome this problem, we have proposed a unique hybrid entropy weight‐based multi‐criteria decision‐making (MCDM) method and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach for the selection of a suitable SRGM and applied it for optimal selection and ranking of SRGMs. The proposed hybrid approach identifies the need of the relative importance of criteria for a given application without which inter‐criterion comparison cannot be accomplished. It requires a set of model selection criteria along with a set of SRGMs and their level of criteria for optimal selection. It successfully displays the result in terms of a merit value which is used to rank the SRGMs. The proposed approach has been validated on two data sets of the real software failure. The results of the study play a vital role for the decision maker to judge the suitability of the SRGM.

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

Cited by 35 publications

References 32 publications

An improved software reliability prediction model by using high precision error iterative analysis method

An improved software reliability prediction model by using high precision error iterative analysis method

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

Selection of optimal software reliability growth models using an integrated entropy–Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach

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