Review and Empirical Analysis of Machine Learning-Based Software Effort Estimation Article Swipe
Mizanur Rahman
,
Hasan Sarwar
,
Md. Abdul Kader
,
Teresa Gonçalves
,
Ting Tin Tin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3404879
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3404879
The average software company spends a huge amount of its revenue on Research and Development (R&D) for how to deliver software on time. Accurate software effort estimation is critical for successful project planning, resource allocation, and on-time delivery within budget for sustainable software development. However, both overestimation and underestimation can pose significant challenges, highlighting the need for continuous improvement in estimation techniques. This study reviews recent machine learning approaches employed to enhance the accuracy of software effort estimation (SEE), focusing on research published between 2020 and 2023. The literature review employed a systematic approach to identify relevant research on machine learning techniques for SEE. Additionally, comparative experiments were conducted using five commonly employed Machine Learning (ML) methods: K-Nearest Neighbor, Support Vector Machine, Random Forest, Logistic Regression, and LASSO Regression. The performance of these techniques was evaluated using five widely adopted accuracy metrics: Mean Squared Error (MSE), Mean Magnitude of Relative Error (MMRE), R-squared, Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The evaluation was carried out on seven benchmark datasets: Albrecht, Desharnais, China, Kemerer, Mayazaki94, Maxwell, and COCOMO, which are publicly available and extensively used in SEE research. By carefully reviewing study quality, analyzing results across the literature, and rigorously evaluating experimental outcomes, clear conclusions were drawn about the most promising techniques for achieving state-of-the-art accuracy in estimating software effort. This study makes three key contributions to the field: firstly, it furnishes a thorough overview of recent machine learning research in software effort estimation (SEE); secondly, it provides data-driven guidance for researchers and practitioners to select optimal methods for accurate effort estimation; and thirdly, it demonstrates the performance of publicly available datasets through experimental analysis. Enhanced estimation supports the development of better predictive models for software project time, cost, and staffing needs. The findings aim to guide future research directions and tool development toward the most accurate machine learning approaches for modelling software development effort, costs, and delivery schedules, ultimately contributing to more efficient and cost-effective software projects.
Related Topics
Concepts
Machine learning
Computer science
Mean squared error
Software
Artificial intelligence
Random forest
Lasso (programming language)
COCOMO
Data mining
Support vector machine
Benchmark (surveying)
Mean absolute percentage error
Software development
Statistics
Artificial neural network
Software construction
Mathematics
Geography
Programming language
Geodesy
World Wide Web
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3404879
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10538131.pdf
- OA Status
- gold
- Cited By
- 17
- References
- 69
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4398249650
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4398249650Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2024.3404879Digital Object Identifier
- Title
-
Review and Empirical Analysis of Machine Learning-Based Software Effort EstimationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Mizanur Rahman, Hasan Sarwar, Md. Abdul Kader, Teresa Gonçalves, Ting Tin TinList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2024.3404879Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10538131.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10538131.pdfDirect OA link when available
- Concepts
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Machine learning, Computer science, Mean squared error, Software, Artificial intelligence, Random forest, Lasso (programming language), COCOMO, Data mining, Support vector machine, Benchmark (surveying), Mean absolute percentage error, Software development, Statistics, Artificial neural network, Software construction, Mathematics, Geography, Programming language, Geodesy, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
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17Total citation count in OpenAlex
- Citations by year (recent)
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2025: 17Per-year citation counts (last 5 years)
- References (count)
-
69Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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