Review of Existing Datasets Used for Software Effort Estimation Article Swipe
Mizanur Rahman
,
Teresa Gonçalves
,
Hasan Sarwar
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2023.01407100
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2023.01407100
The Software Effort Estimation (SEE) tool calculates an estimate of the amount of work that will be necessary to effectively finish the project. Managers usually want to know how hard a new project will be ahead of time so they can divide their limited resources in a fair way. In fact, it is common to use effort datasets to train a prediction model that can predict how much work a project will take. To train a good estimator, you need enough data, but most data owners don't want to share their closed source project effort data because they are worried about privacy. This means that we can only get a small amount of effort data. The purpose of this research was to evaluate the quality of 15 datasets that have been widely utilized in studies of software project estimation. The analysis shows that most of the chosen studies use artificial neural networks (ANN) as ML models, NASA as datasets, and the mean magnitude of relative error (MMRE) as a measure of accuracy. In more cases, ANN and support vector machine (SVM) have done better than other ML techniques.
Related Topics
Concepts
Computer science
Software
Estimator
Support vector machine
Data mining
Artificial neural network
Estimation
Measure (data warehouse)
Quality (philosophy)
Work (physics)
Machine learning
Artificial intelligence
Data science
Statistics
Systems engineering
Philosophy
Mathematics
Programming language
Mechanical engineering
Engineering
Epistemology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14569/ijacsa.2023.01407100
- http://thesai.org/Downloads/Volume14No7/Paper_100-Review_of_Existing_Datasets_Used_for_Software_Effort_Estimation.pdf
- OA Status
- diamond
- Cited By
- 10
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385586750
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385586750Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.14569/ijacsa.2023.01407100Digital Object Identifier
- Title
-
Review of Existing Datasets Used for Software Effort EstimationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Mizanur Rahman, Teresa Gonçalves, Hasan SarwarList of authors in order
- Landing page
-
https://doi.org/10.14569/ijacsa.2023.01407100Publisher landing page
- PDF URL
-
https://thesai.org/Downloads/Volume14No7/Paper_100-Review_of_Existing_Datasets_Used_for_Software_Effort_Estimation.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://thesai.org/Downloads/Volume14No7/Paper_100-Review_of_Existing_Datasets_Used_for_Software_Effort_Estimation.pdfDirect OA link when available
- Concepts
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Computer science, Software, Estimator, Support vector machine, Data mining, Artificial neural network, Estimation, Measure (data warehouse), Quality (philosophy), Work (physics), Machine learning, Artificial intelligence, Data science, Statistics, Systems engineering, Philosophy, Mathematics, Programming language, Mechanical engineering, Engineering, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 3Per-year citation counts (last 5 years)
- References (count)
-
52Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_date | 2023-01-01 |
| publication_year | 2023 |
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