Quantitative Evaluation of Software Quality Metrics in Open-Source Projects
The validation of software quality metrics lacks statistical significance. One reason for this is that the data collection requires quite some effort. To help solve this problem, we develop tools for metrics analysis of a large number of software projects (146 projects with ca. 70.000 classes and in...
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Växjö universitet, Matematiska och systemtekniska institutionen
2009
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ndltd-UPSALLA1-oai-DiVA.org-vxu-25622018-01-14T05:13:26ZQuantitative Evaluation of Software Quality Metrics in Open-Source ProjectsengBarkmann, HenrikeVäxjö universitet, Matematiska och systemtekniska institutionen2009software quality metricsdistributionsComputer SciencesDatavetenskap (datalogi)The validation of software quality metrics lacks statistical significance. One reason for this is that the data collection requires quite some effort. To help solve this problem, we develop tools for metrics analysis of a large number of software projects (146 projects with ca. 70.000 classes and interfaces and over 11 million lines of code). Moreover, validation of software quality metrics should focus on relevant metrics, i.e., correlated metrics need not to be validated independently. Based on our statistical basis, we identify correlation between several metrics from well-known objectoriented metrics suites. Besides, we present early results of typical metrics values and possible thresholds. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-2562Rapporter från MSI, 1650-2647 ; 1650-2647application/pdfinfo:eu-repo/semantics/openAccess |
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software quality metrics distributions Computer Sciences Datavetenskap (datalogi) |
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software quality metrics distributions Computer Sciences Datavetenskap (datalogi) Barkmann, Henrike Quantitative Evaluation of Software Quality Metrics in Open-Source Projects |
description |
The validation of software quality metrics lacks statistical significance. One reason for this is that the data collection requires quite some effort. To help solve this problem, we develop tools for metrics analysis of a large number of software projects (146 projects with ca. 70.000 classes and interfaces and over 11 million lines of code). Moreover, validation of software quality metrics should focus on relevant metrics, i.e., correlated metrics need not to be validated independently. Based on our statistical basis, we identify correlation between several metrics from well-known objectoriented metrics suites. Besides, we present early results of typical metrics values and possible thresholds. |
author |
Barkmann, Henrike |
author_facet |
Barkmann, Henrike |
author_sort |
Barkmann, Henrike |
title |
Quantitative Evaluation of Software Quality Metrics in Open-Source Projects |
title_short |
Quantitative Evaluation of Software Quality Metrics in Open-Source Projects |
title_full |
Quantitative Evaluation of Software Quality Metrics in Open-Source Projects |
title_fullStr |
Quantitative Evaluation of Software Quality Metrics in Open-Source Projects |
title_full_unstemmed |
Quantitative Evaluation of Software Quality Metrics in Open-Source Projects |
title_sort |
quantitative evaluation of software quality metrics in open-source projects |
publisher |
Växjö universitet, Matematiska och systemtekniska institutionen |
publishDate |
2009 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-2562 |
work_keys_str_mv |
AT barkmannhenrike quantitativeevaluationofsoftwarequalitymetricsinopensourceprojects |
_version_ |
1718610890460233728 |