An evaluation of the statistical convertibility of Function Points into COSMIC Function Points.
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| Title: | An evaluation of the statistical convertibility of Function Points into COSMIC Function Points. |
|---|---|
| Authors: | Lavazza, Luigi1 luigi.lavazza@uninsubria.it |
| Source: | Empirical Software Engineering. Aug2014, Vol. 19 Issue 4, p1075-1110. 36p. |
| Subjects: | Function point analysis, Software productivity measurement, Computer software development, Software maintenance, Computer programming management |
| Abstract: | Since the introduction of COSMIC Function Points, the problem of converting historical data measured using traditional Function Points into COSMIC measures have arisen. To this end, several researchers have investigated the possibility of identifying the relationship between the two measures by means of statistical methods. This paper aims at improving statistical convertibility of Function Points into COSMIC Function Points by improving previous work with respect to aspects-like outlier identification and exclusion, model non-linearity, applicability conditions, etc.-which up to now were not adequately considered, with the purpose of confirming, correcting or enhancing current models. Available datasets including software sizes measured both in Function Points and COSMIC Function Points were analyzed. The role of outliers was studied; non linear models and piecewise linear models were derived, in addition to linear models. Models based on transactions only were also derived. Confidence intervals were used throughout the paper to assess the values of the models' parameters. The dependence of the ratio between Function Points and COSMIC Function Points on size was studied. The union of all the available datasets was also studied, to overcome problems due to the relatively small size of datasets. It is shown that outliers do affect the linear models, typically increasing the slope of the regression lines; however, this happens mostly in small datasets: in the union of the available datasets there is no outlier that can influence the model. Conditions for the applicability of the statistical conversion are identified, in terms of relationships that must hold among the basic functional components of Function Point measures. Non-linear models are shown to represent well the relationships between the two measures, since the ratio between COSMIC Function Points and Function Points appears to increase with size. In general, it is confirmed that convertibility can be modeled by different types of models. This is a problem for practitioners, who have to choose one of these models. Anyway, a few practical suggestions can be derived from the results reported here. The model assuming that one FP is equal to one CFP causes the biggest conversion errors observed and is not generally supported. All the considered datasets are characterized by a ratio of transaction to data functions that is fairly constant throughout each dataset: this can be regarded as a condition for the applicability of current models; under this condition non-linear (log-log) models perform reasonably well. The fact that in Function Point Analysis the size of a process is limited, while it is not so in the COSMIC method, seems to be the cause of non linearity of the relationship between the two measures. In general, it appears that the conversion can be successfully based on transaction functions alone, without losing in precision. [ABSTRACT FROM AUTHOR] |
| Copyright of Empirical Software Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 96086650 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An evaluation of the statistical convertibility of Function Points into COSMIC Function Points. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lavazza%2C+Luigi%22">Lavazza, Luigi</searchLink><relatesTo>1</relatesTo><i> luigi.lavazza@uninsubria.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Empirical+Software+Engineering%22">Empirical Software Engineering</searchLink>. Aug2014, Vol. 19 Issue 4, p1075-1110. 36p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Function+point+analysis%22">Function point analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Software+productivity+measurement%22">Software productivity measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink><br /><searchLink fieldCode="DE" term="%22Software+maintenance%22">Software maintenance</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+programming+management%22">Computer programming management</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Since the introduction of COSMIC Function Points, the problem of converting historical data measured using traditional Function Points into COSMIC measures have arisen. To this end, several researchers have investigated the possibility of identifying the relationship between the two measures by means of statistical methods. This paper aims at improving statistical convertibility of Function Points into COSMIC Function Points by improving previous work with respect to aspects-like outlier identification and exclusion, model non-linearity, applicability conditions, etc.-which up to now were not adequately considered, with the purpose of confirming, correcting or enhancing current models. Available datasets including software sizes measured both in Function Points and COSMIC Function Points were analyzed. The role of outliers was studied; non linear models and piecewise linear models were derived, in addition to linear models. Models based on transactions only were also derived. Confidence intervals were used throughout the paper to assess the values of the models' parameters. The dependence of the ratio between Function Points and COSMIC Function Points on size was studied. The union of all the available datasets was also studied, to overcome problems due to the relatively small size of datasets. It is shown that outliers do affect the linear models, typically increasing the slope of the regression lines; however, this happens mostly in small datasets: in the union of the available datasets there is no outlier that can influence the model. Conditions for the applicability of the statistical conversion are identified, in terms of relationships that must hold among the basic functional components of Function Point measures. Non-linear models are shown to represent well the relationships between the two measures, since the ratio between COSMIC Function Points and Function Points appears to increase with size. In general, it is confirmed that convertibility can be modeled by different types of models. This is a problem for practitioners, who have to choose one of these models. Anyway, a few practical suggestions can be derived from the results reported here. The model assuming that one FP is equal to one CFP causes the biggest conversion errors observed and is not generally supported. All the considered datasets are characterized by a ratio of transaction to data functions that is fairly constant throughout each dataset: this can be regarded as a condition for the applicability of current models; under this condition non-linear (log-log) models perform reasonably well. The fact that in Function Point Analysis the size of a process is limited, while it is not so in the COSMIC method, seems to be the cause of non linearity of the relationship between the two measures. In general, it appears that the conversion can be successfully based on transaction functions alone, without losing in precision. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Empirical Software Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10664-013-9246-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 36 StartPage: 1075 Subjects: – SubjectFull: Function point analysis Type: general – SubjectFull: Software productivity measurement Type: general – SubjectFull: Computer software development Type: general – SubjectFull: Software maintenance Type: general – SubjectFull: Computer programming management Type: general Titles: – TitleFull: An evaluation of the statistical convertibility of Function Points into COSMIC Function Points. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lavazza, Luigi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 13823256 Numbering: – Type: volume Value: 19 – Type: issue Value: 4 Titles: – TitleFull: Empirical Software Engineering Type: main |
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