Utilization of Three Software Size Measures for Effort Estimation in Agile World: A Case Study

Ünlü, Hüseyin; Hacaloğlu, Tuna; Buber, Fatma; Berrak, Kivilcim; Leblebici, Onur; Demirörs, Onur

doi:10.1109/seaa56994.2022.00045

Cited by 7 publications

(1 citation statement)

References 14 publications

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“…UFP is one of the techniques to get the size of software based on its functionality. Today, the measurement methods that are widely used are those based on functionality, so they are called functional size measurements (FSM) [4,5]. FSM is based on functional user requirements (FUR) are generally used to estimate efforts in the software project phase's initial stages.…”

Section: Introductionmentioning

confidence: 99%

Implementation of COSMIC Function Points (CFP) as Primary Input to COCOMO II: Study of Conversion to Line of Code Using Regression and Support Vector Regression Models

2023

IJIES

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In COCOMO II, the primary input for estimating development effort in person month (PM), duration, and cost is the size of the software. Until now, there are two ways to get the size, namely (1) size is estimated using a line of code of software, and (2) size is estimated using unadjusted function points (UFP), which is one of the functional size measurements (FSM). In this study, we added a new way to obtain the size as the primary input in COCOMO II, namely with COSMIC function points (CFP). CFP has several advantages compared to other FSMs, including UFP. Therefore, like UFP, CFP is converted first to LOC, so the conversion equation must be obtained first. We applied four models to get the conversion functions: Ordinary least squares regression (OLSR), support vector regression (SVR) with linear, polynomial, and Gaussian kernel functions. The four models were applied using a dataset from small-scale business application software in Java. The results showed that PM estimation using the CFP model as the primary input produced better accuracy based on MMRE and Pred (0.25), namely 17%-19% and 67%-80%, than the UFP model on the COCOMO II of 135% and 10%.

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Section: Introductionmentioning

confidence: 99%

Implementation of COSMIC Function Points (CFP) as Primary Input to COCOMO II: Study of Conversion to Line of Code Using Regression and Support Vector Regression Models

2023

IJIES

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Application of a Size Measurement Standard for Data Warehouse Projects

Ünlü,

Yürüm,

Yıldız

et al. 2024

Softw Pract Exp

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MethodologyIn this research, we conducted a case study to establish a foundation for size measurement and effort estimation in DWH projects. We first applied a productivity‐based estimation approach using linear regression with the ISBSG repository to assist organizations without historical data. We then evaluated various machine learning algorithms to improve estimation accuracy. Finally, we tested a combined model that integrates both approaches for estimating effort in external projects.ResultsUsing the ISBSG dataset, linear regression models based on productivity achieved a Mean Magnitude of Relative Error (MMRE) of 0.285. Machine learning algorithms improved accuracy by 22.81%, reducing the MMRE to 0.220. The final model, applied to external projects, yielded MRE values between 0.010 and 0.245.ConclusionThe ISBSG repository is a valuable resource for effort estimation in DWH projects. Combining productivity‐based estimation with machine learning enhances accuracy and predictive performance, making it a more reliable approach than traditional models.

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Optimizing Effort Estimation in Agile Software Development: Traditional vs. Advanced ML Methods

Sunda,

Sinha

2023

2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI)

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Utilization of Three Software Size Measures for Effort Estimation in Agile World: A Case Study

Cited by 7 publications

References 14 publications

Implementation of COSMIC Function Points (CFP) as Primary Input to COCOMO II: Study of Conversion to Line of Code Using Regression and Support Vector Regression Models

Implementation of COSMIC Function Points (CFP) as Primary Input to COCOMO II: Study of Conversion to Line of Code Using Regression and Support Vector Regression Models

Application of a Size Measurement Standard for Data Warehouse Projects

Optimizing Effort Estimation in Agile Software Development: Traditional vs. Advanced ML Methods

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