Least Square Support Vector Machine in Analogy-Based software development effort estimation

Benala, Tirimula Rao; Bandarupalli, Rohitha

doi:10.1109/icraie.2016.7939524

Cited by 10 publications

(5 citation statements)

References 17 publications

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“…A Squares Support Vector Machine (LS-SVM) method that is nonlinear adjustment method is used for calibration. The work tested it on some datasets and compared it results with artificial neural network (ANN) and extreme learning machines (ELM) [23].…”

Section: Related Workmentioning

confidence: 99%

Using Standard Deviation with Analogy-Based Estimation for Improved Software Effort Prediction

2023

KSII TIIS

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Software effort estimation is one of the most difficult tasks in software development whereas predictability is also of equal importance for strategic management. Accurate prediction of the actual cost that will be incurred in software development can be very beneficial for the strategic management. This study discusses the latest trends in software estimation focusing on analogy-based techniques to show how they have improved the accuracy for software effort estimation. It applies the standard deviation technique to the expected value of analogy-based estimates to improve accuracy. In more than 60 percent cases the applied technique of this study helped in improving the accuracy of software estimation by reducing the Magnitude of Relative Error (MRE). The technique is simple and it calculates the expected value of cost or time and then uses different confidence levels which help in making more accurate commitments to the customers.

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Section: Related Workmentioning

confidence: 99%

Using Standard Deviation with Analogy-Based Estimation for Improved Software Effort Prediction

2023

KSII TIIS

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“…In this case, the features that have a weak correlation, i.e ., less impact, are given less weight, and the features with stronger correlations are given higher weight, and features without correlation are removed. Some studies using Traditional methods ( Mendes, Mosley & Counsell, 2003 ; Phannachitta et al, 2013 ) and machine learning methods ( Benala & Bandarupalli, 2016 ; Zare, Zare & Fallahnezhad, 2016 ; Benala & Mall, 2018 ; Ezghari & Zahi, 2018 ) have demonstrated an improvement in ABE performance. The analogy-based estimation has been widely used to enhance the accuracy of software cost estimation.…”

Section: Related Workmentioning

confidence: 99%

LEMABE: a novel framework to improve analogy-based software cost estimation using learnable evolution model

Dashti

Gandomani

Adeh

et al. 2022

PeerJ Computer Science

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One of the most important and critical factors in software projects is the proper cost estimation. This activity, which has to be done prior to the beginning of a project in the initial stage, always encounters several challenges and problems. However, due to the high significance and impact of the proper cost estimation, several approaches and methods have been proposed regarding how to perform cost estimation, in which the analogy-based approach is one of the most popular ones. In recent years, many attempts have been made to employ suitable techniques and methods in this approach in order to improve estimation accuracy. However, achieving improved estimation accuracy in these techniques is still an appropriate research topic. To improve software development cost estimation, the current study has investigated the effect of the LEM algorithm on optimization of features weighting and proposed a new method as well. In this research, the effectiveness of this algorithm has been examined on two datasets, Desharnais and Maxwell. Then, MMRE, PRED (0.25), and MdMRE criteria have been used to evaluate and compare the proposed method against other evolutionary algorithms. Employing the proposed method showed considerable improvement in estimating software cost estimation.

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“…7,14,20,37,40,50 In the ABE methods, the effort of a new project can be estimated based on the efforts of K similar projects utilizing an adaptation function. In the majority of the previous ABE methods, mean, 5,14,[36][37][38][39][40][41][42][43][44][45][46][47][49][50][51][52][53][54]56,57,64,68 median, 8,67 and inverse rank weighted mean 7,18,48,51,55,62,63,66 are the mostly used adaptation functions.…”

Section: Evaluation Structures In Abe Techniquesmentioning

confidence: 99%

Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing

Shahpar

Bardsiri

2021

Concurrency and Computation

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Software development effort estimation is an effective factor in the success or failure of software projects. There are several methods to estimate the effort of software projects, the most common of which is analogy-based estimation (ABE). In this article, a polynomial version of ABE (named PABE) is presented, in which, the project effort is calculated based on a polynomial ensemble of different ABE models. To optimize the controllable parameters of the PABE model, a combined global-local search metaheuristic algorithm based on particle swarm optimization and simulated annealing is utilized in two steps. At the first step, for each similarity and adaptation function, the optimized ABE model is determined by exploiting the optimal value of feature weights, the number of similar projects, and other parameters of the ABE model. Then, at the second step, the amount of effort attained by the optimized models is used for estimating the final effort by the proposed polynomial equation. The proposed PABE method has been successfully executed on five well-known software effort estimation datasets: Maxwell, Albrecht, Cocomo81, Desharnais, and Kemerer. Obtained results show the superiority of the proposed PABE model in terms of accuracy and efficiency compared to other techniques.

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Least Square Support Vector Machine in Analogy-Based software development effort estimation

Cited by 10 publications

References 17 publications

Using Standard Deviation with Analogy-Based Estimation for Improved Software Effort Prediction

Using Standard Deviation with Analogy-Based Estimation for Improved Software Effort Prediction

LEMABE: a novel framework to improve analogy-based software cost estimation using learnable evolution model

Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing

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