A Comparative Analysis of Software Development Cost Estimation Models: From Parametric Paradigms to Machine-Learning-Driven Estimation

Registration ID: IJNRD_327470

Published ID: IJNRD2607404

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Dr.ambarish kumar patel

Keywords: Agile Effort Estimation , Analogy-Based Estimation , CART , COCOMO , Data Mining Techniques , Deep Learning , Effort Estimation , Function Points , Machine Learning Techniques , MMRE , Regression Analysis , Software Development Cost Estimation (SDCE) , SLIM , Story Points.

Objectives: Software development cost estimation (SDCE) is one of the most ill-defined problems to which forty years of relentless research have been devoted. This study aims to scrutinize the principal families of software cost estimation models and assess their comparative validity in a light of survey-type literature review. The paper cross-references the descriptive taxonomy proposed by Rajper and Shaikh [1] and evaluates the accuracy of the competing approaches using the comprehensive data sample provided by Briand, El Emam, Surmann, Wieczorek and Maxwell [2]. Methods/Analysis: A survey of the literature was carried out between 1978 and 2026 following the standard four-step protocol, including planning, conducting, extracting and reporting. The primary sources of information used in this study consist of the survey by Rajper and Shaikh [1] and the extensive project data sample collected by Briand, El Emam, Surmann, Wieczorek and Maxwell [2]. The latter reference is used to compare the accuracy of ordinary least-squares regressions, stepwise ANOVA, CART, analogy-based estimation and their combinations using five metrics, including MMRE, MdMRE and Pred(0.25). These descriptive statistics were re-calculated for verification purposes during the extraction process, and two computational errors were detected and corrected, namely, a discrepancy between the text of section 3.1 and Table 1, and a repetition of Table 4 caption. In addition to supporting the findings of Rajper and Shaikh [1] regarding the increased diversity of the contemporary estimation practices, the analysis processes the literature of the last five years in order to evaluate if the subsequent advances in deep learning and agile estimation practices would affect the conclusion. Findings: The review findings confirm that the body of estimation practice is growing primarily due to the increased complexity of the software development process, which renders any single mathematical model inadequate. However, as demonstrated by the analysis of [2], any changes in estimation accuracy achieved by varying the choice of terms in the regression equation or using alternative model families (CART, analogy) are mostly negligible, while applying estimation methods at the level of single organizations fails to deliver value in most cases. Application/Improvements: The paper argues that the potential benefits of model optimization are limited, while the improvements in organizational domain knowledge and input data mining should be prioritized, and hybrid machine-learning approaches to software cost estimation should be evaluated as data-conditioning techniques.

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Dr.ambarish kumar patel (July-2026). A Comparative Analysis of Software Development Cost Estimation Models: From Parametric Paradigms to Machine-Learning-Driven Estimation. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(7), e48-e64. https://ijnrd.org/papers/IJNRD2607404.pdf

Volume 11 Issue 7, July-2026

Pages : e48-e64

Paper Reg. ID: IJNRD_327470

Published Paper Id: IJNRD2607404

Research Area: Other area not in list

Author Type: Indian Author

Country: raipur, chhattisgarh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2607404.pdf

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