Modified Jelinski-Moranda Software Reliability Model with Imperfect Debugging Phenomenon

Mahapatra, G. S.; Roy, Pratik

doi:10.5120/7451-0534

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…Geol considered that the time between failures are taken to be exponentially distributed. Different enhancements have been proposed to the existing time between failure models .…”

Section: Related Workmentioning

confidence: 99%

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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“…Geol considered that the time between failures are taken to be exponentially distributed. Different enhancements have been proposed to the existing time between failure models .…”

Section: Related Workmentioning

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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“…Here, INPUTS as normalized time and TARGETS as normalized cumulative faults, the work flow for the proposed SBPNNDWCM design process has a form of pseudocode as given below : S1: INITIALIZE S2: Set initial INPUTS and TARGETS. S3: INPUTS and TARGETS can be considered as either rows or columns of preprocessed data. S4: Load the data sets INPUTS and TARGETS. S5: Create the network object and select randomly divided data sets for validation and testing: Training: The network is adjusted according to its error. S6: Set the hidden layers. S7: Configure the network, and train the network. S8: Repeat S2 to S4 if the network does not perform well after training. S9: Finally, evaluate the network. The best network with highest prediction performance were recorded. S10: Optionally test network on more data: increase iteration then retrain of the network, (b)if increase, then network size should be adjusted, (c)if data set does not work, then follow S3. S14: Save the network; otherwise, follow S1 to S5 S15: STOP. …”

Section: Description Of Proposed Sbpnn For Dwcm Modelmentioning

confidence: 99%

“…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 ; testing coverage ; and imperfect debugging processes .…”

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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Introduction to Software Reliability Models

Hanagal

Bhalerao

2021

Infosys Science Foundation Series

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Modified Jelinski-Moranda Software Reliability Model with Imperfect Debugging Phenomenon

Cited by 8 publications

References 14 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

Introduction to Software Reliability Models

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