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Journal articles on the topic 'Estimation software'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Capretz, Luiz Fernando, and Venus Marza. "Improving Effort Estimation by Voting Software Estimation Models." Advances in Software Engineering 2009 (September 1, 2009): 1–8. http://dx.doi.org/10.1155/2009/829725.

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Estimating software development effort is an important task in the management of large software projects. The task is challenging, and it has been receiving the attentions of researchers ever since software was developed for commercial purpose. A number of estimation models exist for effort prediction. However, there is a need for novel models to obtain more accurate estimations. The primary purpose of this study is to propose a precise method of estimation by selecting the most popular models in order to improve accuracy. Consequently, the final results are very precise and reliable when they are applied to a real dataset in a software project. Empirical validation of this approach uses the International Software Benchmarking Standards Group (ISBSG) Data Repository Version 10 to demonstrate the improvement in software estimation accuracy.

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2

Yücalar, Fatih, Deniz Kilinc, Emin Borandag, and Akin Ozcift. "Regression Analysis Based Software Effort Estimation Method." International Journal of Software Engineering and Knowledge Engineering 26, no. 05 (June 2016): 807–26. http://dx.doi.org/10.1142/s0218194016500261.

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Estimating the development effort of a software project in the early stages of the software life cycle is a significant task. Accurate estimates help project managers to overcome the problems regarding budget and time overruns. This paper proposes a new multiple linear regression analysis based effort estimation method, which has brought a different perspective to the software effort estimation methods and increased the success of software effort estimation processes. The proposed method is compared with standard Use Case Point (UCP) method, which is a well-known method in this area, and simple linear regression based effort estimation method developed by Nassif et al. In order to evaluate and compare the proposed method, the data of 10 software projects developed by four well-established software companies in Turkey were collected and datasets were created. When effort estimations obtained from datasets and actual efforts spent to complete the projects are compared with each other, it has been observed that the proposed method has higher effort estimation accuracy compared to the other methods.

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3

Bajaj, Nonika, Alok Tyagi, and Rakesh Agarwal. "Software estimation." ACM SIGSOFT Software Engineering Notes 31, no. 3 (May 2006): 1–5. http://dx.doi.org/10.1145/1127878.1127881.

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4

Ayyıldız, Tülin Erçelebi, and Hasan Can Terzi. "Case Study on Software Effort Estimation." International Journal of Information and Electronics Engineering 7, no. 3 (May 2017): 103–7. http://dx.doi.org/10.18178/ijiee.2017.7.3.670.

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5

Jain, Parita, Arun Sharma, and Laxmi Ahuja. "Software Maintainability Estimation in Agile Software Development." International Journal of Open Source Software and Processes 9, no. 4 (October 2018): 65–78. http://dx.doi.org/10.4018/ijossp.2018100104.

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Agile methodologies have gained wide acceptance for developing high-quality products with a quick and flexible approach. However, until now, the quality of the agile process has not been validated quantitatively. Quality being important for the software system, there is a need for measurement. Estimating different quality factors will lead to a quality product. Also, agile software development does not provide any precise models to evaluate maintainability. Therefore, there is a need for an algorithmic approach that can serve as the basis for estimation of maintainability. The article proposes an adaptive neuro-fuzzy inference system (ANFIS) model for estimating agile maintainability. Maintainability is one of the prominent quality factors in the case of agile development. The proposed model has been verified and found to be effective for assessing the maintainability of agile software.

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6

Shivakumar, Shailesh Kumar. "Software Estimation Framework for Packaged Products." International Journal of Project Management and Productivity Assessment 9, no. 1 (January 2021): 15–24. http://dx.doi.org/10.4018/ijpmpa.2021010102.

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Packaged products play a major role in successful implementation of various software projects. Many of the software solutions are built around packaged products. In this paper, the authors propose a novel “software packaged product estimation framework” for an end to end estimation framework for estimating effort for packaged products. The software packaged product estimation framework provides end to end estimation coverage for various project lifecycle stages and supporting activities. The software packaged product estimation framework was used to predict the effort for two projects with MMRE of 0.261 and pred(0.3) of 66.67%.

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7

., E. Karunakaran. "EXTREME SOFTWARE ESTIMATION (XSOFT ESTIMATION)." International Journal of Research in Engineering and Technology 02, no. 12 (December 25, 2013): 366–73. http://dx.doi.org/10.15623/ijret.2013.0212063.

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8

Ferdiana, Ridi, Paulus Insap Santoso, Lukito Edi Nugroho, and Ahmad Ashari. "USER STORY SOFTWARE ESTIMATION:A SIMPLIFICATION OF SOFTWARE ESTIMATION MODEL WITH DISTRIBUTED EXTREME PROGRAMMING ESTIMATION TECHNIQUE." JUTI: Jurnal Ilmiah Teknologi Informasi 9, no. 1 (January 1, 2011): 41. http://dx.doi.org/10.12962/j24068535.v9i1.a67.

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9

Chatzipetrou, Panagiota. "Software Cost Estimation." International Journal of Service Science, Management, Engineering, and Technology 10, no. 3 (July 2019): 14–31. http://dx.doi.org/10.4018/ijssmet.2019070102.

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Software cost estimation (SCE) is a critical phase in software development projects. A common problem in building software cost models is that the available datasets contain projects with lots of missing categorical data. There are several techniques for handling missing data in the context of SCE. The purpose of this article is to show a state-of-art statistical and visualization approach of evaluating and comparing the effect of missing data on the accuracy of cost estimation models. Five missing data techniques were used: multinomial logistic regression, listwise deletion, mean imputation, expectation maximization and regression imputation; and compared with respect to their effect on the prediction accuracy of a least squares regression cost model. The evaluation is based on various expressions of the prediction error. The comparisons are conducted using statistical tests, resampling techniques and visualization tools like the regression error characteristic curves.

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10

Deng, Jeremiah D., Martin Purvis, and Maryam Purvis. "Software Effort Estimation." International Journal of Intelligent Information Technologies 7, no. 3 (July 2011): 41–53. http://dx.doi.org/10.4018/jiit.2011070104.

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Software development effort estimation is important for quality management in the software development industry, yet its automation still remains a challenging issue. Applying machine learning algorithms alone often cannot achieve satisfactory results. This paper presents an integrated data mining framework that incorporates domain knowledge into a series of data analysis and modeling processes, including visualization, feature selection, and model validation. An empirical study on the software effort estimation problem using a benchmark dataset shows the necessity and effectiveness of the proposed approach.

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11

Saif, Huda. "Software Cost Estimation." Global Sci-Tech 8, no. 1 (2016): 15. http://dx.doi.org/10.5958/2455-7110.2016.00003.3.

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12

Boehm, B. W., and R. E. Fairley. "Software estimation perspectives." IEEE Software 17, no. 6 (November 2000): 22–26. http://dx.doi.org/10.1109/ms.2000.895164.

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13

Lehder, Wilfred E., D. Paul Smith, and Weider D. Yu. "Software Estimation Technology." AT&T Technical Journal 67, no. 4 (July 8, 1988): 10–18. http://dx.doi.org/10.1002/j.1538-7305.1988.tb00634.x.

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14

Heemstra, F. J. "Software cost estimation." Information and Software Technology 34, no. 10 (October 1992): 627–39. http://dx.doi.org/10.1016/0950-5849(92)90068-z.

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15

Hamdy, Abeer. "Genetic Fuzzy System for Enhancing Software Estimation Models." International Journal of Modeling and Optimization 4, no. 3 (June 2014): 227–32. http://dx.doi.org/10.7763/ijmo.2014.v4.378.

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16

Sekhar, R. Poorna Chandra, and Dr G. Anjan Babu. "Comparison of Software Cost Estimation Techniques: An Overview." International Journal of Trend in Scientific Research and Development Volume-1, Issue-5 (August 31, 2017): 26–32. http://dx.doi.org/10.31142/ijtsrd2248.

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17

Gupta, Sanjali. "A Comparison between Various Software Cost Estimation Models." International journal of Emerging Trends in Science and Technology 03, no. 11 (November 22, 2016): 4771–76. http://dx.doi.org/10.18535/ijetst/v3i11.08.

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18

Pagadala, Srivyshnavi, Sony Bathala, and B. Uma. "An Efficient Predictive Paradigm for Software Reliability." Asian Journal of Computer Science and Technology 8, S3 (June 5, 2019): 114–16. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2051.

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Software Estimation gives solution for complex problems in the software industry which gives estimates for cost and schedule. Software Estimation provides a comprehensive set of tips and heuristics that Software Developers, Technical Leads, and Project Managers can apply to create more accurate estimates. It presents key estimation strategies and addresses particular estimation challenges. In the planning of a software development project, a major challenge faced by project managers is to predict the defects and effort. The Software defect plays critical role in software product development. The estimation of defects can be determined in the product development using many advanced statistical modelling techniques based on the empirical data obtained by the testing phases. The proposed estimation technique in this paper is a model which was developed using Rayleigh function for estimating effect of defects in Software Project Management. The present study offers to decide how many defects creep in to production and determine the effort spent in months. The estimation model was used on Software Testing Life Cycle (STLC) to complete product. The accuracy of the model explains the variation in spent efforts in months associated with number of defects. The model helps the senior management in estimating the defects, schedule, cost and effort.

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19

O.I., Bederdinova, and Boytsova Yu.A. "SOFTWARE INTEGRAL QUALITATIVE ESTIMATION." “Vestnik of Northern (Arctic) Federal University. Series "Natural Science", no. 2 (June 20, 2016): 99–106. http://dx.doi.org/10.17238/issn2227-6572.2016.2.99.

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20

Kumar, Neeraj, Yogesh Kumar, and Rahul Rishi. "Software Effort Estimation Techniques." International Journal of Computer Sciences and Engineering 7, no. 1 (January 31, 2019): 139–42. http://dx.doi.org/10.26438/ijcse/v7i1.139142.

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21

Chowdary, E. J. Sai Pavan. "Software Effort Estimation Techniques." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 2018): 2497–500. http://dx.doi.org/10.22214/ijraset.2018.4424.

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22

Nair, T. R. Gopalakrishnan, and R. Selvarani. "Estimation of software reusability." ACM SIGSOFT Software Engineering Notes 35, no. 1 (January 25, 2010): 1–6. http://dx.doi.org/10.1145/1668862.1668868.

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23

Kushwaha, Dharmender Singh, and A. K. Misra. "Software test effort estimation." ACM SIGSOFT Software Engineering Notes 33, no. 3 (May 2008): 1–5. http://dx.doi.org/10.1145/1360602.1361211.

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24

Schooff, R. M., and Y. Y. Haimes. "Dynamic multistage software estimation." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 29, no. 2 (May 1999): 272–84. http://dx.doi.org/10.1109/5326.760571.

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25

Yang, Hai. "Research on Improved Staged Software Cost Estimation Method Based on COCOMO Model." Advanced Materials Research 989-994 (July 2014): 1501–4. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1501.

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The accuracy of software cost estimation is essential for software development management. By introducing and analyzing the estimation methods of software cost systematically, the paper discussed the necessary of considering the software maintenance stage and estimating the software cost by separating the procedure of software development into several small stages. Then a staged software cost estimation method based on COCOMO model was proposed. The use of the new software cost estimation method proposed by this paper not only contributes to the cost control of software project, but also effectively avoids the bias problem due to using by single cost estimation method so that the accuracy of cost estimation could be improved.

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26

Iwata, Kazunori, Toyoshiro Nakashima, Yoshiyuki Anan, and Naohiro Ishii. "Machine Learning Classification to Effort Estimation for Embedded Software Development Projects." International Journal of Software Innovation 5, no. 4 (October 2017): 19–32. http://dx.doi.org/10.4018/ijsi.2017100102.

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This paper discusses the effect of classification in estimating the amount of effort (in man-days) associated with code development. Estimating the effort requirements for new software projects is especially important. As outliers are harmful to the estimation, they are excluded from many estimation models. However, such outliers can be identified in practice once the projects are completed, and so they should not be excluded during the creation of models and when estimating the required effort. This paper presents classifications for embedded software development projects using an artificial neural network (ANN) and a support vector machine. After defining the classifications, effort estimation models are created for each class using linear regression, an ANN, and a form of support vector regression. Evaluation experiments are carried out to compare the estimation accuracy of the model both with and without the classifications using 10-fold cross-validation. In addition, the Games-Howell test with one-way analysis of variance is performed to consider statistically significant evidence.

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27

Resmi, V., and S. Vijayalakshmi. "Analogy-Based Approaches to Improve Software Project Effort Estimation Accuracy." Journal of Intelligent Systems 29, no. 1 (June 27, 2019): 1468–79. http://dx.doi.org/10.1515/jisys-2019-0023.

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Abstract In the discipline of software development, effort estimation renders a pivotal role. For the successful development of the project, an unambiguous estimation is necessitated. But there is the inadequacy of standard methods for estimating an effort which is applicable to all projects. Hence, to procure the best way of estimating the effort becomes an indispensable need of the project manager. Mathematical models are only mediocre in performing accurate estimation. On that account, we opt for analogy-based effort estimation by means of some soft computing techniques which rely on historical effort estimation data of the successfully completed projects to estimate the effort. So in a thorough study to improve the accuracy, models are generated for the clusters of the datasets with the confidence that data within the cluster have similar properties. This paper aims mainly on the analysis of some of the techniques to improve the effort prediction accuracy. Here the research starts with analyzing the correlation coefficient of the selected datasets. Then the process moves through the analysis of classification accuracy, clustering accuracy, mean magnitude of relative error and prediction accuracy based on some machine learning methods. Finally, a bio-inspired firefly algorithm with fuzzy analogy is applied on the datasets to produce good estimation accuracy.

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28

Silhavy, Radek, Petr Silhavy, and Zdenka Prokopova. "Using Actors and Use Cases for Software Size Estimation." Electronics 10, no. 5 (March 4, 2021): 592. http://dx.doi.org/10.3390/electronics10050592.

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Software size estimation represents a complex task, which is based on data analysis or on an algorithmic estimation approach. Software size estimation is a nontrivial task, which is important for software project planning and management. In this paper, a new method called Actors and Use Cases Size Estimation is proposed. The new method is based on the number of actors and use cases only. The method is based on stepwise regression and led to a very significant reduction in errors when estimating the size of software systems compared to Use Case Points-based methods. The proposed method is independent of Use Case Points, which allows the elimination of the effect of the inaccurate determination of Use Case Points components, because such components are not used in the proposed method.

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29

Gharehchopogh, Farhad Soleimanian, Isa Maleki, and Seyyed Reza Khaze. "A novel particle swarm optimization approach for software effort estimation." International Journal of Academic Research 6, no. 2 (March 30, 2014): 69–76. http://dx.doi.org/10.7813/2075-4124.2014/6-2/a.12.

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30

Gharehchopogh, Farhad Soleimanian, Akbar Talebi, and Isa Maleki. "Analysis of use case points models for software cost estimation." International Journal of Academic Research 6, no. 3 (May 30, 2014): 118–24. http://dx.doi.org/10.7813/2075-4124.2014/6-3/a.16.

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31

Abdulmehdi, Ziyad T., M. S. Saleem Basha, Mohamed Jameel, and P. Dhavachelvan. "A Variant of COCOMO II for Improved Software Effort Estimation." International Journal of Computer and Electrical Engineering 6, no. 4 (2014): 346–50. http://dx.doi.org/10.7763/ijcee.2014.v6.851.

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32

COSTAGLIOLA, G., F. FERRUCCI, G. TORTORA, and G. VITIELLO. "A METRIC FOR THE SIZE ESTIMATION OF OBJECT-ORIENTED GRAPHICAL USER INTERFACES." International Journal of Software Engineering and Knowledge Engineering 10, no. 05 (October 2000): 581–603. http://dx.doi.org/10.1142/s0218194000000304.

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In order to achieve quality products with reliable cost and effort estimations, one of the main tasks for planning software project development is size estimation. This is especially true when dealing with interactive applications which represent critical components in a software project. In the paper, we address the problem of the size estimation of interactive graphical applications developed using the object-oriented methodology. In particular, we define and validate a metric, the Class Point metric, for estimating the size of object-oriented GUIs. The method is based on the idea of quantifying classes in a program analogous to function counting performed by the function point metric. Theoretical validation has proven the consistency of the Class Point metric as size measure. Empirical validation provides evidence that the Class Point metric is a useful measure for OO software size.

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33

Iok Kuan, Simon WU. "Factors on Software Effort Estimation." International Journal of Software Engineering & Applications 8, no. 1 (January 30, 2017): 23–32. http://dx.doi.org/10.5121/ijsea.2017.8103.

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34

Qin, Xiaotie, and Miao Fang. "Summarization of Software Cost Estimation." Procedia Engineering 15 (2011): 3027–31. http://dx.doi.org/10.1016/j.proeng.2011.08.568.

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35

Chen, Yeh-Ling, and Arnold J. Stromberg. "Robust estimation in software experiments." ACM SIGSOFT Software Engineering Notes 22, no. 4 (July 1997): 60–64. http://dx.doi.org/10.1145/263244.263260.

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36

Suresh, Nalina, and A. J. G. Babu. "Software reliability estimation and optimization." International Journal of Quality & Reliability Management 14, no. 3 (April 1997): 287–300. http://dx.doi.org/10.1108/02656719710165491.

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37

Rojas Puentes, M. P., M. F. Mora Méndez, L. F. Bohórquez Chacón, and S. M. Romero. "Estimation metrics in software projects." Journal of Physics: Conference Series 1126 (November 2018): 012050. http://dx.doi.org/10.1088/1742-6596/1126/1/012050.

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38

Heidrich, Jens, Markku Oivo, and Andreas Jedlitschka. "Software productivity and effort estimation." Journal of Software: Evolution and Process 27, no. 7 (June 26, 2015): 465–66. http://dx.doi.org/10.1002/smr.1722.

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39

McGough, Keith. "Cost Estimation in Software Engineering." Journal of Parametrics 8, no. 2 (June 1988): 5–11. http://dx.doi.org/10.1080/10157891.1988.10472824.

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40

Ferens, Daniel V., and Frank Albanese. "Software Size Estimation: Deja Vu?" Journal of Parametrics 9, no. 3 (October 1989): 35–47. http://dx.doi.org/10.1080/10157891.1989.10472852.

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41

Ross, S. M. "Statistical Estimation of Software Reliability." IEEE Transactions on Software Engineering SE-11, no. 5 (May 1985): 479–83. http://dx.doi.org/10.1109/tse.1985.232487.

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42

Mellor, P. "Experiments in software reliability estimation." Reliability Engineering 18, no. 2 (January 1987): 117–29. http://dx.doi.org/10.1016/0143-8174(87)90026-6.

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43

Kitchenham, Barbara A., and N. R. Taylor. "Software project development cost estimation." Journal of Systems and Software 5, no. 4 (November 1985): 267–78. http://dx.doi.org/10.1016/0164-1212(85)90026-3.

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44

Bosu, Michael Franklin, Stephen G. MacDonell, and Peter A. Whigham. "Analyzing the Stationarity Process in Software Effort Estimation Datasets." International Journal of Software Engineering and Knowledge Engineering 30, no. 11n12 (November 2020): 1607–40. http://dx.doi.org/10.1142/s0218194020400239.

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Software effort estimation models are typically developed based on an underlying assumption that all data points are equally relevant to the prediction of effort for future projects. The dynamic nature of several aspects of the software engineering process could mean that this assumption does not hold in at least some cases. This study employs three kernel estimator functions to test the stationarity assumption in five software engineering datasets that have been used in the construction of software effort estimation models. The kernel estimators are used in the generation of nonuniform weights which are subsequently employed in weighted linear regression modeling. In each model, older projects are assigned smaller weights while the more recently completed projects are assigned larger weights, to reflect their potentially greater relevance to present or future projects that need to be estimated. Prediction errors are compared to those obtained from uniform models. Our results indicate that, for the datasets that exhibit underlying nonstationary processes, uniform models are more accurate than the nonuniform models; that is, models based on kernel estimator functions are worse than the models where no weighting was applied. In contrast, the accuracies of uniform and nonuniform models for datasets that exhibited stationary processes were essentially equivalent. Our analysis indicates that as the heterogeneity of a dataset increases, the effect of stationarity is overridden. The results of our study also confirm prior findings that the accuracy of effort estimation models is independent of the type of kernel estimator function used in model development.

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45

Ba’abbad, Ibrahim Mohammad, and M. Rizwan Jameel Qureshi. "Quality Extended Use Case Point (QUCP): An Improved Cost Estimation Method." International Journal of Computer Science and Mobile Computing 10, no. 6 (June 30, 2021): 1–9. http://dx.doi.org/10.47760/ijcsmc.2021.v10i06.001.

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The quality of a product is one of the major interests of the manufacturing process in all industries. The software industry imposes to construct a project with several phases to ensure producing high-quality software. A software development company estimates time, effort and cost of the project during planning phase. It is important to have accurate estimations to reduce the risks of project failure. Several cost estimation methods are practiced in the software development companies such as Function Point (FP), Use Case Points (UCP), Constructive Cost Model I and II and Story Points (SP). UCP cost estimation method is taken in this research to improve the accuracy of its estimation. UCP estimation depends on the use case diagram of the proposed system. A use case diagram describes the main functional requirements of the proposed system. UCP partially considers non-functional requirements through the technical and environmental factors. There is a lacking in the UCP method to consider the importance of quality attributes in the estimating process. This paper proposes an extended version of the existing UCP method named Quality Extended Use Case Point (QUCP) method in which quality attributes are included to improve the accuracy of cost estimation. A questionnaire is used to validate the proposed QUCP method. It is found after data analysis that seventy five percentages of the participants are agreed that the proposed method will not only help to improve the accuracy of cost estimation but it will also enable a software development company to deliver high-quality products.

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46

PARK, GEE-YONG, and SEUNG CHEOL JANG. "A SOFTWARE RELIABILITY ESTIMATION METHOD TO NUCLEAR SAFETY SOFTWARE." Nuclear Engineering and Technology 46, no. 1 (February 2014): 55–62. http://dx.doi.org/10.5516/net.04.2012.067.

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47

Lennselius, Bo, Claes Wohlin, and Ctirad Vrana. "Software metrics: fault content estimation and software process control." Microprocessors and Microsystems 11, no. 7 (September 1987): 365–75. http://dx.doi.org/10.1016/0141-9331(87)90524-2.

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48

Silas, Faki Agebee, Musa Yusuf, and Anah Hassan Bijik. "Hybridization of Class Responsibility Collaborators Model (HCRCM) with Function Point to enhance Project Estimation Cost in Agile Software Development." Circulation in Computer Science 2, no. 6 (July 20, 2017): 20–24. http://dx.doi.org/10.22632/ccs-2017-252-32.

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Estimating software cost in an agile system in terms of effort is very challenging. This is because the traditional models of software cost estimation do not completely fit in the agile development process. This paper presents a methodology to enhance the cost of project estimation in agile development. The hybridization adopts Class Responsibility Collaborators models with function point thereby boosting the agile software development estimation process. The study found out that adopting the Hybridized Class Responsibility Collaborator with function point has great improvement on cost estimation in agile software development.

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49

Bhawana Verma, Satish Kumar Alaria. "Design & Analysis of Cost Estimation for New Mobile-COCOMO Tool for Mobile Application." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 1 (January 31, 2019): 27–34. http://dx.doi.org/10.17762/ijritcc.v7i1.5222.

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Software cost estimation is a resource forecasting method, which is required by the software development process. However, estimating the workload, schedule and cost of a software project is a complex task because it involves predicting the future using historical project data and extrapolating to see future values. For cost estimates for software projects, several methods are used. Among the various software cost estimation methods available, the most commonly used technology is the COCOMO method. Similarly, to calculate software costs, there are several cost estimating tools available for software developers to use. But these released cost estimation tools can only provide parameters (i.e. cost, development time, average personnel) for large software with multiple lines of code. However, if a software developer wants to estimate the cost of a small project that is usually a mobile application, the available tools will not give the right results. Therefore, to calculate the cost of the mobile application, the available cost estimation method COCOMO II is improved to a new model called New Mobile COCOMO Tool. The New Mobile COCOMO tool developed specifically for mobile applications is a boon for software developers working in small software applications because it only includes important multipliers that play a vital role in estimating the cost of developing mobile applications. Therefore, the objective of this paper is to propose a cost estimation model with a special case of COCOMO II, especially for mobile applications, which calculates the person-month, the programmed time and the average personnel involved in the development of any mobile app.

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50

G KrishnaMohan, Dr, B. Sowmya, K. Mohanvamsi, and K. Sandeep. "An Effective Software Reliability Estimation with Real-Valued Genetic Algorithm." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 359. http://dx.doi.org/10.14419/ijet.v7i2.32.15713.

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Abstract:

The implemented approach is powerful method for estimating reliability of the software parameters growing SRGM by utilizing an Algorithm which is known as RGA. The full form of RGA is Real-valued Genetic Algorithm. Parameters required for current SRGM, if we take an illustration, the Failures average number or identification rate of the failure utilizing the techniques which are numerical, estimation of the maximum probability or estimation of minimum square.RGA means the free form of SRGM parameter estimation limitations. Instead of these, this can be much adapted for optimizing domain continuously compared to the algorithm of the binary genetic. The operators of GA which is 2 real valued crossovers& mutation of non-uniform interfaced for enhancing SRGM parameters estimation execution and accuracy enhancement. I led tests over eight datasets which are real valued to contrast implemented scheme & techniques of the numerical & another generic algorithm which are typical. The results describes that in estimation of SRGM parameters, the RGA is the most powerful compared to the others. So that we can trust the RGA which is the right solution for getting the efficient software quality with estimation of reliable accuracy.

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