Software development effort estimation: Difference between revisions

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m Add cited 2024 Springer study on AI-based effort-estimation accuracy.
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* Formal estimation model: The quantification step is based on mechanical processes, e.g., the use of a formula derived from historical data.
* Combination-based estimation: The quantification step is based on a judgmental and mechanical combination of estimates from different sources.
===AI-based estimation===
Recent peer-reviewed studies report that machine-learning and ensemble techniques can now outperform traditional parametric models in software-effort estimation tasks.<ref>Rankovic N. et al. “AI in Software Effort Estimation.” In: '''Recent Advances in Artificial Intelligence in Cost Estimation in Project Management'''. Springer, 2024, pp. 157–174. doi:10.1007/978-3-031-76572-8_4</ref>
 
Below are examples of estimation approaches within each category.
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| doi = 10.1109/32.965341
| year = 2001
| bibcode = 2001ITSEn..27.1014S
| url = http://bura.brunel.ac.uk/handle/2438/1102
}}
</ref> This implies that different organizations benefit from different estimation approaches. Findings<ref name="Jørgensen, M">{{cite web
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| url = http://portal.acm.org/citation.cfm?id=951936 | doi = 10.1109/TSE.2003.1245300
| year = 2003
| citeseerxbibcode = 102003ITSEn.1.129.101.5792985F
| citeseerx = 10.1.1.101.5792
}}
</ref> and there are several alternative measures, such as more symmetric measures,<ref>{{cite journal
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| url = https://ieeexplore.ieee.org/document/689296 | archive-url = https://web.archive.org/web/20170920055746/http://ieeexplore.ieee.org/document/689296/ | url-status = dead | archive-date = September 20, 2017 | doi = 10.1049/ip-sen:19983370
| year = 1998
| doi-broken-date = 712 DecemberJuly 20242025
}}</ref>