Optimizing design parameters of fuzzy model based COCOMO using genetic algorithms

Chhabra, Sonia; Singh, H. N.

doi:10.1007/s41870-019-00325-7

Cited by 16 publications

(7 citation statements)

References 18 publications

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“…Moreover, with the proposed method MdMRE and MMRE are 0.15 and 0.25, respectively. Although previous studies 12,15,16 have obtained lower MMRE, their PRED (0.25) is less than ours. Considering the MAR measure, our proposed method has resulted in a much lower value than in Jose Thiago and Oliveira.…”

Section: Results On Nasa60 Datasetcontrasting

confidence: 98%

CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics

Feizpour

Tahayori

Sami

2023

J Software Evolu Process

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Software development effort estimation (SDEE) is a critical activity in developing software. Accurate effort estimation in the early phases of software design life cycle has important effects on the success of software projects. COCOMO (Constructive Cost Model) is a parametric data‐driven SDEE model whose parameters must be calibrated with an organization's local data for accurate estimation. Such data are scarce for most organizations. On the other hand, CoBRA (Cost estimation, Benchmarking, and Risk Assessment) is one of the powerful hybrid methods that need a small number of local historical data for effort estimation. However, data gathering in CoBRA is time‐consuming and costly. To ease the use of CoBRA, in this paper, we design a methodology that extracts CoBRA‐required data from COCOMO datasets. By the proposed method, data collected for COCOMO would be used in CoBRA. Using CoBRA, a more accurate estimation of the required effort would be achieved with fewer number of historical data than what is required to calibrate the COCOMO model. We apply the proposed method on six well‐known public COCOMO datasets and use them in CoBRA. Obtained results depict an increase in the accuracy of estimations in comparison with other existing methods.

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Section: Results On Nasa60 Datasetcontrasting

confidence: 98%

CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics

Feizpour

Tahayori

Sami

2023

J Software Evolu Process

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“…It has been observed that due to improved selection owing to the GA fitness function a 25% reduction has been observed in Mean Magnitude of Relative Error. This high improvement was mainly due to increased stability of GA in optimizing the fuzzy model that improved the overall prediction accuracy for the effort estimation [14]. www.ijacsa.thesai.org Öztürk et al 2021, a feed forward DNN algorithm (FFDNN) was put forth in this article.…”

Section: Related Workmentioning

confidence: 96%

Performance Analysis of Software Test Effort Estimation using Genetic Algorithm and Neural Network

Chahar¹,

Bhatia²

2022

IJACSA

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In present scenario, the software companies are frequently involving software test effort estimation to allocate the resources efficiently during the software development process. Different machine learning models are developed to estimate the total effort that would be required before the software product could be delivered. These computational models are used to use the past data to estimate the efforts. In the current studies, test effort estimation for software is predicted using the Genetic algorithm and Neural Network. The attributes are selected using the Genetic algorithm and similarity measure between the attribute values has been computed using the Cosine Similarity measure. The simulation experiments were done using the PROMISE and Kaggle repository and implementation was done using the MATLAB software. The performance metrics namely, precision, recall, and accuracy are computed to evaluate against the existing techniques. The accuracy of the proposed model is 91.3% and results are improved by 8.9% in comparison to existing technique and comparison has been made for superiority to predict the test effort for software development.

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“…In order to compare ANN with LR and SVR, the performance metrics MSE, RMSE, and MAE are considered. To justify the scope of the proposed optimal ANN model, the study also considers performance analysis with similar existing approaches such as estimation technique based on fuzzy-genetic [33] and based Dolphin optimization technique [34], Bat optimization [34], and combined Dolphin-BAT [34], the performance metric PRED and MMRE is used. The quantitative outcome obtained for the proposed system and its comparison is shown in Table V.…”

Section: Outcome Analysismentioning

confidence: 99%

Effective ANN Model based on Neuro-Evolution Mechanism for Realistic Software Estimates in the Early Phase of Software Development

N¹,

Suresh²

2022

IJACSA

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There is no doubt that the software industry is one of the fastest-growing sectors on the planet today. As the cost of the entire development process continues to rise, an effective mechanism is needed to estimate the required development cost to control better the cost overrun problem and make the final software product more competitive. However, in the early stages of planning, the project managers have difficulty estimating the realistic value of the effort and cost required to execute development activities. Software evaluation prior to development can minimize risk and upsurge project success rates. Many techniques have been suggested and employed for cost estimation. However, computations based on several of these techniques show that the estimation of development effort and cost vary, which may cause problems for software industries in allocating overall resources costs. The proposed research study proposes the artificial neural network (ANN) based Neural-Evolution technique to provide more realistic software estimates in the early stages of development. The proposed model uses the advantages of the topology augmentation using an evolutionary algorithm to automate and achieve optimality in ANN construction and training.Based on the results and performance analysis, it is observed that software effort prediction using the proposed approach is more accurate and better than other existing approaches.

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Optimizing design parameters of fuzzy model based COCOMO using genetic algorithms

Cited by 16 publications

References 18 publications

CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics

CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics

Performance Analysis of Software Test Effort Estimation using Genetic Algorithm and Neural Network

Effective ANN Model based on Neuro-Evolution Mechanism for Realistic Software Estimates in the Early Phase of Software Development

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