Whale–crow optimization (WCO)-based Optimal Regression model for Software Cost Estimation

Ahmad, Sumera W; Bamnote, G. R.

doi:10.1007/s12046-019-1085-1

Cited by 13 publications

(9 citation statements)

References 27 publications

(49 reference statements)

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“…Boehm et al 20 were the first to propose a well‐known model called the COCOMO for predicting software cost‐effectively and a total of 63 projects is used to validate the model's effectiveness. Over the last three decades, lots of new SCE models based on algorithmic, soft computing, machine learning, and meta‐heuristic techniques have been proposed 6,21‐25 . Benala et al 26 introduced an approach based on DE with five mutation strategies.…”

Section: Background Methods and Software Cost Estimationmentioning

confidence: 99%

A new evolutionism based self‐adaptive multi‐objective optimization method to predict software cost estimation

Gouda

Mehta

2022

Softw Pract Exp

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One of the major challenge that organizations face in the present environment is having an efficient model for software cost estimation (SCE). In this article, the significance of the meta‐heuristic algorithm in addressing various optimization challenges faced in mathematical models and software applications is discussed. The proposed method uses the new evolutionism‐based self‐adaptive mutation operator to solve the multi‐objective optimization problems. This approach addresses the issues that exist in multi‐objective differential evolution algorithms. To improve diversity among candidate solutions, the Pareto optimality principle is integrated with the evolutionism‐based self‐adaptive mutation operator in a multi‐objective DE algorithm. To reduce the time complexity of Pareto dominance, we have adopted the non‐dominated sorting algorithm. We used eight benchmark test functions to evaluate the effectiveness of the proposed method, and it outperformed the most recent multi‐objective evolutionary algorithms (MOEAs). Furthermore, this article explores software engineering problems like SCE by using the proposed approach, where SCEs are accurately predicted by optimizing the tuning parameters of the multi‐objective constructive cost model. The proposed algorithm achieves better cost prediction as compared to the other standard benchmark algorithms for all objective problems in terms of prediction, mean absolute error, and root mean square error.

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Section: Background Methods and Software Cost Estimationmentioning

confidence: 99%

A new evolutionism based self‐adaptive multi‐objective optimization method to predict software cost estimation

Gouda

Mehta

2022

Softw Pract Exp

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“…Various meta-heuristic techniques for software cost estimation have been implemented over the last decade. ere is work that computes the effectiveness of meta heuristics algorithms [17,20,[22][23][24] related to the optimization of software cost estimation. For example, in the existing literature, genetic algorithm (GA) [23], hybrid GA [24], ants colony optimization (ACO) [25] algorithm, and firefly algorithm (FA) [26] improved cost estimation.…”

Section: Related Workmentioning

confidence: 99%

“…ere is work that computes the effectiveness of meta heuristics algorithms [17,20,[22][23][24] related to the optimization of software cost estimation. For example, in the existing literature, genetic algorithm (GA) [23], hybrid GA [24], ants colony optimization (ACO) [25] algorithm, and firefly algorithm (FA) [26] improved cost estimation. Moreover, existing literature also demonstrates the effectiveness of meta-heuristic algorithms in terms of optimizing the parameters of COCOMO [25][26][27][28][29].…”

Section: Related Workmentioning

confidence: 99%

Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach

Hassan

Khan

Irfan

et al. 2022

Computational Intelligence and Neuroscience

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Effective software cost estimation significantly contributes to decision-making. The rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. The constructive cost model (COCOMO) method is a well-known regression-based algorithmic technique for estimating software costs. The limitation of the COCOMO models is that the values of these coefficients are constant for similar kinds of projects whereas, in reality, these parameters vary from one organization to another organization. Therefore, for accurate estimation, it is necessary to fine-tune the coefficients. The research community is now examining deep learning (DL) as a forward-looking solution to improve cost estimation. Although deep learning architectures provide some improvements over existing flat technologies, they also have some shortcomings, such as large training delays, over-fitting, and under-fitting. Deep learning models usually require fine-tuning to a large number of parameters. The meta-heuristic algorithm supports finding a good optimal solution at a reasonable computational cost. Additionally, heuristic approaches allow for the location of an optimum solution. So, it can be used with deep neural networks to minimize training delays. The hybrid of ant colony optimization with BAT (HACO-BA) algorithm is a hybrid optimization technique that combines the most common global optimum search technique for ant colonies (ACO) in association with one of the newest search techniques called the BAT algorithm (BA). This technology supports the solution of multivariable problems and has been applied to the optimization of a large number of engineering problems. This work will perform a two-fold assessment of algorithms: (i) comparing the efficacy of ACO, BA, and HACO-BA in optimizing COCOMO II coefficients; and (ii) using HACO-BA algorithms to optimize and improve the deep learning training process. The experimental results show that the hybrid HACO-BA performs better as compared to ACO and BA for tuning COCOMO II. HACO-BA also performs better in the optimization of DNN in terms of execution time and accuracy. The process is executed upto 100 epochs, and the accuracy achieved by the proposed DNN approach is almost 98% while NN achieved accuracy of up to 85% on the same datasets.

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“…The main drawback of existing literature [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] is that it is very difficult to figure out which meta-heuristic algorithm provides better accuracy in estimating software effort. The main reasons behind unpredictability in the performances of the meta-heuristic algorithms are as follows.…”

Section: A Problem Formulationmentioning

confidence: 99%

“…Hybrid method combines two or more methods. Ahmed et al [23] presented Whale-Crow Optimization (WCO) algorithm for software cost estimation. WCO integrates Whale Optimization Algorithm (WOA) and the Crow Search Algorithm (CSA) to find the optimal regression coefficients for a regression model.…”

Section: State Of the Artmentioning

confidence: 99%

Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

et al. 2021

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Effort estimation is the most critical activity for the success of overall solution delivery in software engineering projects. In this context, the paper's main contributions to the literature on software effort estimation are twofold. First, this paper examines the application of meta-heuristic algorithms to have a logical and acceptable parametric model for software effort estimation. Secondly, to unravel the benefits of nature-inspired meta-heuristic algorithms usage in optimizing Deep Learning (DL) architectures for software effort estimation, this paper presents a Deep Neural Network (DNN) model for software effort estimation based on meta-heuristic algorithms. In this paper, Grey Wolf Optimizer (GWO) and StrawBerry (SB) meta-heuristic algorithms are applied for having a logical and acceptable parametric model for software effort estimation. To validate the performances of these two algorithms, a set of nine benchmark functions having wide dimensions is applied. Results from GWO and SB algorithms are compared with five other meta-heuristic algorithms used in literature for software effort estimation. Experimental results showed that the GWO has comprehensive superiority in terms of accuracy in estimation. The proposed DNN model (GWDNNSB) using meta-heuristic algorithms for initial weights and learning rate selection, produced better results compared to existing work on using DNN for software effort estimation.

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Whale–crow optimization (WCO)-based Optimal Regression model for Software Cost Estimation

Cited by 13 publications

References 27 publications

A new evolutionism based self‐adaptive multi‐objective optimization method to predict software cost estimation

A new evolutionism based self‐adaptive multi‐objective optimization method to predict software cost estimation

Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach

Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

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