Parametric Software Effort Estimation Based on Optimizing Correction Factors and Multiple Linear Regression

Nhung, Ho Le Thi Kim; Hai, Vo Van; Šilhavý, Radek; Prokopová, Zdenka; Silhavy, Petr

doi:10.1109/access.2021.3139183

Cited by 26 publications

(17 citation statements)

References 70 publications

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“…The investigator claimed that these parts are insignificant concerning the effort estimation. Recently, Nhung et al [41] optimized the correction factors (ECF and TCF) and multiple regression models to improve the estimation accuracy of the modified UCP.…”

Section: Related Workmentioning

confidence: 99%

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Optimizing complexity weight parameter of use case points estimation using particle swarm optimization

Ardiansyah

Ferdiana

Permanasari

2022

Int. J. Adv. Intell. Informatics

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Among algorithmic-based frameworks for software development effort estimation, Use Case Points I s one of the most used. Use Case Points is a well-known estimation framework designed mainly for object-oriented projects. Use Case Points uses the use case complexity weight as its essential parameter. The parameter is calculated with the number of actors and transactions of the use case. Nevertheless, use case complexity weight is discontinuous, which can sometimes result in inaccurate measurements and abrupt classification of the use case. The objective of this work is to investigate the potential of integrating particle swarm optimization (PSO) with the Use Case Points framework. The optimizer algorithm is utilized to optimize the modified use case complexity weight parameter. We designed and conducted an experiment based on real-life data set from three software houses. The proposed model’s accuracy and performance evaluation metric is compared with other published results, which are standardized accuracy, effect size, mean balanced residual error, mean inverted balanced residual error, and mean absolute error. Moreover, the existing models as the benchmark are polynomial regression, multiple linear regression, weighted case-based reasoning with (PSO), fuzzy use case points, and standard Use Case Points. Experimental results show that the proposed model generates the best value of standardized accuracy of 99.27% and an effect size of 1.15 over the benchmark models. The results of our study are promising for researchers and practitioners because the proposed model is actually estimating, not guessing, and generating meaningful estimation with statistically and practically significant.

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Section: Related Workmentioning

confidence: 99%

“…Moreover, these approaches showed promoting results by expanding the complexity weight in the original UCP method. Despite Nhung et al [41] and Hoc et al [54] conducted the optimization in UCP. However, they do not explore the potential of continuous complexity weight level in terms of optimization function.…”

Section: Related Workmentioning

confidence: 99%

Optimizing complexity weight parameter of use case points estimation using particle swarm optimization

Ardiansyah

Ferdiana

Permanasari

2022

Int. J. Adv. Intell. Informatics

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show abstract

“…According to [7], [70], and [71], the Mean Magnitude of Relative Error (MMRE) and Mean Magnitude of Relative Error Relative (MMER) criteria are the most common metrics used. However, [72], [73], and [74] have demonstrated that these methods are biased. Azzeh et al [75] recommend using unbiased methods instead.…”

Section: Evaluation Criteriamentioning

confidence: 99%

Toward Improving the Efficiency of Software Development Effort Estimation via Clustering Analysis

et al. 2022

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Introduction:The precise estimation of software effort is a significant difficulty that project managers encounter during software development. Inaccurate forecasting leads to either overestimating or underestimating software effort, which can be detrimental for stakeholders. The International Function Point Users Group's Function Point Analysis (FPA) method is one of the most critical methods for software effort estimation. However, the practice of using the FPA method in the same fashion across all software areas needs to be reexamined. Aim: We propose a model for evaluating the influence of data clustering on software development effort estimation and then finding the best clustering method. We call this model the effort estimation using machine learning applied to the clusters (EEAC) model. Method: We cluster the dataset according to the clustering method and then apply the FPA and EEAC methods to these clusters for effort estimation. The clustering methods we use in this study include five categorical variable criteria (Development Platform, Industrial Sector, Language Type, Organization Type, and Relative Size) and the k-means clustering algorithm. Results: The experimental results show that the estimation accuracy obtaining with clustering consistently outperforms the accuracy without clustering for both the FPA and EEAC methods. Significantly, using the FPA method, the average improvement rate from using clustering as opposed to non-clustered was highest at 58.06%, according to the RMSE. With the EEAC method, this number reached 65.53%. The Industry Sector categorical variable achieves the best accuracy estimation compared to the other clustering criteria and k-means clustering. The improvement in accuracy in terms of the RMSE when applying this criterion is 63.68% for the FPA method and 72.02% for the EEAC method. Conclusion: Better results are obtained through dataset clustering compared to no clustering for both the FPA and EEAC methods. The Industry Sector is the most suitable clustering method among the tested clustering methods.

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“…Another optimization technique was discussed in the work of Nhung et al [42], where a parametric approach toward effort estimation is applied. This work predominantly focuses on numerous regression models to control the errors in estimation using least squared regression towards all the elements in points of use cases.…”

Section: Related Workmentioning

confidence: 99%

OLCE: Optimized Learning-based Cost Estimation for Global Software Projects

Rao

Bolla

Mummana³

et al. 2022

Preprint

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Software Cost Estimation (SCE) is an integral part of pre-development stage of software project with a target to accomplish a better visibility towards possible risk while gaining more information towards reaching success rate to meet the deadline of delivery. Irrespective of multiple research contribution model towards SCE, the problem and challenges towards accurate cost estimation in presence of dynamicity and uncertainty is yet not reported to be accomplished. Apart from this, learning-based models are slowly gaining pace in almost every field and yet it is still in nascent stage of progress in software engineering. Therefore, the proposed manuscript introduces Optimized Learning-based Cost Estimation (OLCE) which is a novel learning-based model capable of accurate prediction considering global and large scale software project. The proposed system harnesses the learning potential from artificial neural network integrated with novel search-based approach for optimizing the learning method considering the benchmarked COCOMO NASA 2 dataset. The study outcome shows OLCE offers 50% faster response time with approximately 73% of accuracy compared to existing models that are reportedly found to be adopted for SCE. Hence, OLCE is found to offer a balance between accuracy and computational efficiency during SCE.

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Parametric Software Effort Estimation Based on Optimizing Correction Factors and Multiple Linear Regression

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 26 publications

References 70 publications

Optimizing complexity weight parameter of use case points estimation using particle swarm optimization

Optimizing complexity weight parameter of use case points estimation using particle swarm optimization

Toward Improving the Efficiency of Software Development Effort Estimation via Clustering Analysis

OLCE: Optimized Learning-based Cost Estimation for Global Software Projects

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