A software reliability growth model with Gompertz-logarithmic failure time distribution

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tahere Yaghoobi
Keyword(s):  
Growth Model ◽  
Software Reliability ◽  
Failure Time ◽  
Model Parameters ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Content Type ◽  
Software Failure ◽  
Proposed Model ◽  
Software Reliability Growth

PurposeThe Gompertz curve has been used in industry to estimate the number of remaining software faults. This paper aims to introduce a family of distributions for fitting software failure times which subsumes the Gompertz distribution.Design/methodology/approachThe mean value function of the corresponding non-homogenous Poisson process software reliability growth model is presented. Model parameters are estimated by the method of maximum likelihood. A comparison of the new model with eight models that use well-known failure time distributions of exponential, gamma, Rayleigh, Weibull, Gompertz, half normal, log-logistic and lognormal is performed according to the several statistical and informational criteria. Moreover, a Shannon entropy approach is used for ranking and model selection.FindingsNumerical experiments are implemented on five real software failure datasets varying from small to large datasets. The results exhibit that the proposed model is promising and particularly outperforms the Gompertz model in all considered datasets.Originality/valueThe proposed model provides optimized reliability estimation.

AN NHPP SOFTWARE RELIABILITY GROWTH MODEL WITH IMPERFECT DEBUGGING AND ERROR GENERATION

2014 ◽  
Vol 21 (02) ◽  
pp. 1450008 ◽  
Author(s):  
P. ROY ◽  
G. S. MAHAPATRA ◽  
K. N. DEY
Keyword(s):  
Growth Model ◽  
Software Reliability ◽  
Data Sets ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Imperfect Debugging ◽  
Failure Data ◽  
Proposed Model ◽  
Software Reliability Growth ◽  
Error Generation

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.

scholarly journals A STUDY OF VARIOUS SOFTWARE RELIABILITY SYSTEMS BY USING ANN

2021 ◽  
Vol 23 (07) ◽  
pp. 968-976
Author(s):  
Vidushi Awasthi ◽  
◽  
Shiv Kumar Sharma ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Growth Model ◽  
Software Reliability ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Proposed Model ◽  
Software Reliability Growth ◽  
Artificial Neural ◽  
Reliability Systems

One of the quantifiable credits of software quality is reliability.Programmable/ Software Reliability Growth Model (SRGM) can be used for continuous quality during difficult times. In all conditions where test work fluctuates over time, the customary time-sensitive SRGM may not be clear enough. In order to close this gap, testing work was used instead of time in SRGM. It may be unwise to put forward a restricted test pressure limit in advance because the test work will be endless within the incomprehensible test time. Later in this article, we propose a permanent test stress service related to the old inhomogeneous Poisson process model (NHPP). We use an artificial neural network (ANN) to configure the proposed model, which contains frustration data from the software. Here, it is reasonable to obtain a huge load of game plans for the comparison model, which represents past disappointment data in a comparable way. We use artificial intelligence methods to select game plans with reasonable load for the model to describe the past and future data well. We use a reasonable software disappointment data set to decompose the presentation of the proposed model from the current model. Use the artificial neural network method to design the general Direct Software Reliability Growth Model (SRGM) through test work.: The true quality software is shown by current research mainly focuses on the best method of general guessing modeling.

scholarly journals 2-Dimensional Multi-Release Software Reliability Modelling considering Fault Reduction Factor under imperfect debugging

2018 ◽  
Vol 14 (25) ◽  
pp. 1-12 ◽  
Author(s):  
Sameer Anand ◽  
Vibha Verma ◽  
Anu Gupta Aggarwal
Keyword(s):  
Growth Model ◽  
Software Reliability ◽  
Goodness Of Fit ◽  
Reduction Factor ◽  
Release Time ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Imperfect Debugging ◽  
Proposed Model ◽  
Software Reliability Growth

Introduction: The present research was conducted at the University of Delhi, India in 2017.Methods: We develop a software reliability growth model to assess the reliability of software products released in multiple versions under limited availability of resources and time. The Fault Reduction Factor (frf) is considered to be constant in imperfect debugging environments while the rate of fault removal is given by Delayed S-Shaped model.Results: The proposed model has been validated on a real life four-release dataset by carrying out goodness of fit analysis. Laplace trend analysis was also conducted to judge the trend exhibited by data with respect to change in the system’s reliability.Conclusions: A number of comparison criteria have been calculated to evaluate the performance of the proposed model relative to only time-based multi-release Software Reliability Growth Model (srgm).Originality: In general, the number of faults removed is not the same as the number of failures experienced in given time intervals, so the inclusion of frf in the model makes it better and more realistic. A paradigm shift has been observed in software development from single release to multi release platform.Limitations: The proposed model can be used by software developers to take decisions regarding the release time for different versions, by either minimizing the development cost or maximizing the reliability and determining the warranty policies.

Software Reliability Growth Model with Rate of Change in Application Characteristics.

2020 ◽  
Vol 13 ◽  
Author(s):  
Prarna Mehta ◽  
Himanshu Sharma ◽  
Abhishek Tandon
Keyword(s):  
Growth Model ◽  
Software Reliability ◽  
Time Frame ◽  
Rate Of Change ◽  
Software Project ◽  
Reliability Growth ◽  
Market Demand ◽  
Software Reliability Growth Model ◽  
Proposed Model ◽  
Software Reliability Growth

Background:: There has been continuous advancement in technologies for past few decades by incorporating new features in accordance to the market demand. The evolution of software projects/applications has intricated debugging process by generating numerous faults in it. Objective:: In this study, an attempt is made to develop a software reliability growth model (SRGM) taking into account the software project/application’s characteristic such as complexity of code and testing environment. The simulation is based on previous fault data in order to foresee the future latent faults occurring in the system for a given time frame. This model not only forecast the number of faults but is an extended version of Kapur and Garg’s error removal phenomenon model incorporating factors that might have influence on the model. Methods:: The performance of the model is validated using three data sets and finally compared with extant models, namely GO model and Yamada model to assess the proposed model’s efficiency. Results:: The parameter estimations were significant and the proposed model performed better in comparison to the other two models. Conclusion:: The proposed model is a contribution to the studies on the reliability of the project and can be extended in future by generalizing the results over various datasets and models.

Developing a hybrid software reliability growth model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Avinash Kumar Shrivastava ◽  
Ruchi Sharma
Keyword(s):  
Distribution Function ◽  
Growth Model ◽  
Software Reliability ◽  
Change Point ◽  
Distribution Functions ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Content Type ◽  
Before And After ◽  
Software Reliability Growth

PurposeThe purpose of this paper is to develop a new software reliability growth model considering different fault distribution function before and after the change point.Design/methodology/approachIn this paper, the authors have developed a framework to incorporate change-point in developing a hybrid software reliability growth model by considering different distribution functions before and after the change point.FindingsNumerical illustration suggests that the proposed model gives better results in comparison to the existing models.Originality/valueThe existing literature on change point-based software reliability growth model assumes that the fault correction trend before and after the change is governed by the same distribution. This seems impractical as after the change in the testing environment, the trend of fault detection or correction may not follow the same trend; hence, the assumption of same distribution function may fail to predict the potential number of faults. The modelling framework assumes different distributions before and after change point in developing a software reliability growth model.

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