Journal ArticleDOI
An efficient parameter optimization of software reliability growth model by using chaotic grey wolf optimization algorithm
P. Dhavakumar,N.P. Gopalan +1 more
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TLDR
To enhance the reliability of SRGM, the parameters ofSRGM estimated using TEF and enhanced using chaotic maps to improve search performance are enhanced and the constrained benchmark functions the results of chaotic maps are obtained.Abstract:
Software reliability growth model (SRGM) with modified testing-effort function (TEF) is a function to evaluate and foresee the parameters of the data. Reliability of software is portrayed as the distinct possibility that for a predefined time, a software package will continue to run on an advance domain without frustration. SRGM utilized a few optimization procedure algorithms to advance the parameters by bifurcating them into a few stages however to upgrade the technique by using all of the parameters at the same time, the algorithm utilized is the chaotic grey wolf optimization algorithm (CGWO). CGWO is an advanced heuristic system for portraying the execution by achieving complex parameter optimization and designing application issues. Different parametric reliabilities rely upon the attributes or characteristics of the data. The parameters are predicted using the Pham–Zhang (PZ) model. Tandem computer software dataset DS1 and DS2 are used to compare the predicted parameter of SRGM obtained by Pham–Zhang (PZ) model using testing effort functions (TEFs) based on the evaluation metrics mean square error (MSE), relative error (RE) and coefficient of determination (R2). To enhance the reliability of SRGM, the parameters of SRGM estimated using TEF and enhanced using chaotic maps to improve search performance. By using the constrained benchmark functions the results of chaotic maps are obtained. Based on the chaotic graph results, the Chebyshev graph shows a good convergence rate of 78%. Overall, 86% of the results revealed an association between the choice variable and fitness criteria for CGWO. In the SRGM using CGWO, the expected result is completely mechanized and does not require any client necessity.read more
Citations
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Software defect prediction using hybrid model (CBIL) of convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM)
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References
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Journal ArticleDOI
Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
Journal ArticleDOI
Chaotic grey wolf optimization algorithm for constrained optimization problems
Mehak Kohli,Sankalap Arora +1 more
TL;DR: This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating its global convergence speed, and shows that with an appropriate chaotic map, CGWO can clearly outperform standard GWO, with very good performance in comparison with other algorithms and in application to constrained optimization problems.
Journal ArticleDOI
Progress on approaches to software defect prediction
TL;DR: The authors survey almost 70 representative defect prediction papers in recent years, most of which are published in the prominent software engineering journals and top conferences, and identify some practical guidelines for both software engineering researchers and practitioners in future software defect prediction.
Journal ArticleDOI
A Learning-to-Rank Approach to Software Defect Prediction
Xiaoxing Yang,Ke Tang,Xin Yao +2 more
TL;DR: Empirical studies demonstrate the effectiveness of directly optimizing the model performance measure for the learning-to-rank approach to construct defect prediction models for the ranking task.
Journal ArticleDOI
CBSO: a memetic brain storm optimization with chaotic local search
TL;DR: A novel method which incorporates BSO with chaotic local search (CLS) to make BSO break the stagnation and keep the population’s diversity simultaneously, thus realizing a better balance between exploration and exploitation.
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