Software Development and Maintenance Effort Estimation Using Function Points and Simpler Functional Measures
Functional size measures are widely used for estimating software development effort. After the introduction of Function Points, a few “simplified” measures have been proposed, aiming to make measurement simpler and applicable when fully detailed software specifications are not yet available. However...
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MDPI AG
2024-10-01
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| author | Luigi Lavazza Angela Locoro Roberto Meli |
| author_facet | Luigi Lavazza Angela Locoro Roberto Meli |
| author_sort | Luigi Lavazza |
| collection | DOAJ |
| description | Functional size measures are widely used for estimating software development effort. After the introduction of Function Points, a few “simplified” measures have been proposed, aiming to make measurement simpler and applicable when fully detailed software specifications are not yet available. However, some practitioners believe that, when considering “complex” projects, traditional Function Point measures support more accurate estimates than simpler functional size measures, which do not account for greater-than-average complexity. In this paper, we aim to produce evidence that confirms or disproves such a belief via an empirical study that separately analyzes projects that involved developments from scratch and extensions and modifications of existing software. Our analysis shows that there is no evidence that traditional Function Points are generally better at estimating more complex projects than simpler measures, although some differences appear in specific conditions. Another result of this study is that functional size metrics—both traditional and simplified—do not seem to effectively account for software complexity, as estimation accuracy decreases with increasing complexity, regardless of the functional size metric used. To improve effort estimation, researchers should look for a way of measuring software complexity that can be used in effort models together with (traditional or simplified) functional size measures. |
| format | Article |
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| institution | OA Journals |
| issn | 2674-113X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Software |
| spelling | doaj-art-ec0a01bf31d24f47a8706da79dbb33942025-08-20T02:01:29ZengMDPI AGSoftware2674-113X2024-10-013444247210.3390/software3040022Software Development and Maintenance Effort Estimation Using Function Points and Simpler Functional MeasuresLuigi Lavazza0Angela Locoro1Roberto Meli2Department of Theoretical and Applied Sciences, Università degli Studi dell’Insubria, 21100 Varese, ItalyDepartment of Economics and Management, Università degli Studi di Brescia, 25121 Brescia, ItalyData Processing Organization Srl, 00155 Roma, ItalyFunctional size measures are widely used for estimating software development effort. After the introduction of Function Points, a few “simplified” measures have been proposed, aiming to make measurement simpler and applicable when fully detailed software specifications are not yet available. However, some practitioners believe that, when considering “complex” projects, traditional Function Point measures support more accurate estimates than simpler functional size measures, which do not account for greater-than-average complexity. In this paper, we aim to produce evidence that confirms or disproves such a belief via an empirical study that separately analyzes projects that involved developments from scratch and extensions and modifications of existing software. Our analysis shows that there is no evidence that traditional Function Points are generally better at estimating more complex projects than simpler measures, although some differences appear in specific conditions. Another result of this study is that functional size metrics—both traditional and simplified—do not seem to effectively account for software complexity, as estimation accuracy decreases with increasing complexity, regardless of the functional size metric used. To improve effort estimation, researchers should look for a way of measuring software complexity that can be used in effort models together with (traditional or simplified) functional size measures.https://www.mdpi.com/2674-113X/3/4/22unadjusted Function Points (UFPs)simple Function Points (SFPs)effort estimationsimple functional size measures |
| spellingShingle | Luigi Lavazza Angela Locoro Roberto Meli Software Development and Maintenance Effort Estimation Using Function Points and Simpler Functional Measures Software unadjusted Function Points (UFPs) simple Function Points (SFPs) effort estimation simple functional size measures |
| title | Software Development and Maintenance Effort Estimation Using Function Points and Simpler Functional Measures |
| title_full | Software Development and Maintenance Effort Estimation Using Function Points and Simpler Functional Measures |
| title_fullStr | Software Development and Maintenance Effort Estimation Using Function Points and Simpler Functional Measures |
| title_full_unstemmed | Software Development and Maintenance Effort Estimation Using Function Points and Simpler Functional Measures |
| title_short | Software Development and Maintenance Effort Estimation Using Function Points and Simpler Functional Measures |
| title_sort | software development and maintenance effort estimation using function points and simpler functional measures |
| topic | unadjusted Function Points (UFPs) simple Function Points (SFPs) effort estimation simple functional size measures |
| url | https://www.mdpi.com/2674-113X/3/4/22 |
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