A Learning-Based Adjustment Model with Genetic Algorithm of Function Point Estimation

Liu, Jiaqi; Du, Qingfeng; Xu, Jincheng

doi:10.1109/hpcc/smartcity/dss.2018.00039

Cited by 4 publications

(6 citation statements)

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“…In [55], the authors proposed a learning-based adjustment model for the FPA =using a Genetic Algorithm and analogybased estimation. They also used Language Type, Development Platform, Development Type, and 1st Database System as the crucial factors.…”

Section: Related Workmentioning

confidence: 99%

“…Clustering Method References 1 Development Type (DT) [45], [46], [48], [49], [52], [55], [56], [58] 2 Development Platform (DP) [45], [50], [52], [54], [55], [56], [58] 3 Language Type (LT) [50], [51], [52], [55], [56], [57], [58] 4 Industry Sector (IS) [25], [36], [47], [48], [52], [53], [56] 5 Organization Type (OT) [45], [46], [54], [58] 6 Relative Size (RS) [47], [53], [56], [57] 7 Application Type (AT) [45], [46], [48] [56]…”

Section: Nomentioning

confidence: 99%

“…8 Business Area Type (BAT) [45], [47], [53] 9 Primary Programming Language (PPL) [49], [52] 10 Application Group (AG) [36], [52] 11 1st Database System (1DB) [52], [55] 12 Used Methodology (UM) [52], [56] 13 Count Approach (CA) [47], [57] 14 Project Type (PT) [54] 15 Resources Level (RL) [56] 16 1st Operation System (1OS) [57] 17 K-Means Algorithm [37], [38], [39], [40], [41], [42], [43], [44]…”

Section: Nomentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Nomentioning

confidence: 99%

Section: Nomentioning

confidence: 99%

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

et al. 2022

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“…Over six hundred projects of the International Software Benchmarking Standards Group (ISBSG) dataset are used as a case study to validate the proposed solution. Liu et al [2] recommend more research to find alternative methods to select KIFs.…”

Section: Related Workmentioning

confidence: 99%

“…Software cost estimation is a cumbersome task because it requires the estimator to predict cost accurately at the planning phase of the project. Some software cost estimation methods are proposed over the years such as Function Point Analysis (FPA), Cost Constructive Model (COCOMO) I & II, Use Case Points (UCP) and story point [1][2][3][4][5]. Some researchers combine them with novel approaches to generate more accurate estimations of software projects.…”

Section: Introductionmentioning

confidence: 99%

Quality Extended Use Case Point (QUCP): An Improved Cost Estimation Method

Ba’abbad¹,

Qureshi²

2021

IJCSMC

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The quality of a product is one of the major interests of the manufacturing process in all industries. The software industry imposes to construct a project with several phases to ensure producing high-quality software. A software development company estimates time, effort and cost of the project during planning phase. It is important to have accurate estimations to reduce the risks of project failure. Several cost estimation methods are practiced in the software development companies such as Function Point (FP), Use Case Points (UCP), Constructive Cost Model I and II and Story Points (SP). UCP cost estimation method is taken in this research to improve the accuracy of its estimation. UCP estimation depends on the use case diagram of the proposed system. A use case diagram describes the main functional requirements of the proposed system. UCP partially considers non-functional requirements through the technical and environmental factors. There is a lacking in the UCP method to consider the importance of quality attributes in the estimating process. This paper proposes an extended version of the existing UCP method named Quality Extended Use Case Point (QUCP) method in which quality attributes are included to improve the accuracy of cost estimation. A questionnaire is used to validate the proposed QUCP method. It is found after data analysis that seventy five percentages of the participants are agreed that the proposed method will not only help to improve the accuracy of cost estimation but it will also enable a software development company to deliver high-quality products.

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Estimation in Agile Software Development Using Artificial Intelligence

Srivastava

Agarwal

Singh

2022

Lecture Notes in Networks and Systems

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A Learning-Based Adjustment Model with Genetic Algorithm of Function Point Estimation

Cited by 4 publications

References 12 publications

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

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

Quality Extended Use Case Point (QUCP): An Improved Cost Estimation Method

Estimation in Agile Software Development Using Artificial Intelligence

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A Learning-Based Adjustment Model with Genetic Algorithm of Function Point Estimation

Cited by 4 publications

References 12 publications

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

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

Quality Extended Use Case Point (QUCP): An Improved Cost Estimation Method﻿

Estimation in Agile Software Development Using Artificial Intelligence

Contact Info

Product

Resources

About

Quality Extended Use Case Point (QUCP): An Improved Cost Estimation Method