Optimizing Basic COCOMO Model Using Simplified Genetic Algorithm

Sachan, Rohit Kumar; Nigam, Ayush; Singh, Avinash; Singh, Sharad; Choudhary, Manjeet; Tiwari, Avinash; Kushwaha, Dharmender Singh

doi:10.1016/j.procs.2016.06.107

Cited by 40 publications

(29 citation statements)

References 13 publications

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“…PSO is applied to the Turkish Software Industry dataset. The offered experiments apply the PSO technique for optimization using equation (1) and (2). The implementation of PSO in updating the velocity and position of the optimization using equations (5) and (6) for the parameter of A and equation 7and (8) for the parameter of B.…”

Section: Resultsmentioning

confidence: 99%

“…Activity estimates of costs and efforts are intended to obtain an accurate estimation result, not the result of either overestimate or underestimate. It can also manage the application's quick configuration [1]. The accuracy is determined by several variables or cost drivers of a method that is used, so to get an accurate cost estimate of the software requires control of the variables or cost drivers that affect it.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Effort Parameter of COCOMO II Using Particle Swarm Optimization Method

2018

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Estimating the effort and cost of software is an important activity for software project managers. A poor estimate (overestimates or underestimates) will result in poor software project management. To handle this problem, many researchers have proposed various models for estimating software cost. Constructive Cost Model II (COCOMO II) is one of the best known and widely used models for estimating software costs. To estimate the cost of a software project, the COCOMO II model uses software size, cost drivers, scale factors as inputs. However, this model is still lacking in terms of accuracy. To improve the accuracy of COCOMO II model, this study examines the effect of the cost factor and scale factor in improving the accuracy of effort estimation. In this study, we initialized using Particle Swarm Optimization (PSO) to optimize the parameters in a model of COCOMO II. The method proposed is implemented using the Turkish Software Industry dataset which has 12 data items. The method can handle improper and uncertain inputs efficiently, as well as improves the reliability of software effort. The experiment results by MMRE were 34.1939%, indicating better high accuracy and significantly minimizing error 698.9461% and 104.876%.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing Effort Parameter of COCOMO II Using Particle Swarm Optimization Method

2018

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“…Algoritmo Genético é um algoritmo de busca evolucionária que pertence ao grupo de algoritmos inspirados na natureza. De acordo com (Chalotra et al 2015) foi proposto por John Holland que abstraiu os conceitos da evolução das espécies (Chalotra et al 2015, Sachan et al 2016.…”

Section: Algoritmo Genéticounclassified

Otimização Do Cocomo Básico Utilizando Algoritmo Genético Para Estimativa De Esforço No Desenvolvimento De Software

Souza

Araújo²,

Zanetti³

2020

Conteúdo Conceitual E Aspectos Práticos Da Ciência Da Computação

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“…This can manage both the overestimates and the underestimates for software efforts and the costs required to complete the project. It can also manage the application's quick configuration [3]. This level of accuracy comes from several influential variables or cost drivers.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Time and Effort Parameters of COCOMO II using Fuzzy Multi-Objective Particle Swarm Optimization

2018

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Estimating the efforts, costs, and schedules of software projects is a frequent challenge to software development projects. A bad estimation will result in bad management of a project. Various models of estimation have been defined to complete this estimate. The Constructive Cost Model II (COCOMO II) is one of the most famous models as a model for estimating efforts, costs, and schedules. To estimate the effort, cost, and schedule in project of software, the COCOMO II uses inputs: Effort Multiplier (EM), Scale Factor (SF), and Source Line of Code (SLOC). Evidently, this model is still lack in terms of accuracy rates in both efforts estimated and time of development. In this paper, we introduced to use Gaussian Membership Function (GMF) of Fuzzy Logic and Multi-Objective Particle Swarm Optimization (MOPSO) method to calibrate and optimize the parameters of COCOMO II. It is to achieve a new level of accuracy better on COCOMO II. The Nasa93 dataset is used to implement the method proposed. The experimental results of the method proposed have reduced the error downto 11.89% and 8.08% compared to the original COCOMO II. This method proposed has achieved better results than previous studies.

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Optimizing Basic COCOMO Model Using Simplified Genetic Algorithm

Cited by 40 publications

References 13 publications

Optimizing Effort Parameter of COCOMO II Using Particle Swarm Optimization Method

Optimizing Effort Parameter of COCOMO II Using Particle Swarm Optimization Method

Otimização Do Cocomo Básico Utilizando Algoritmo Genético Para Estimativa De Esforço No Desenvolvimento De Software

Optimizing Time and Effort Parameters of COCOMO II using Fuzzy Multi-Objective Particle Swarm Optimization

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