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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:vxu-2562 |
| Date | January 2009 |
| Creators | Barkmann, Henrike |
| Publisher | Växjö universitet, Matematiska och systemtekniska institutionen |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | Rapporter från MSI, 1650-2647 ; 1650-2647 |
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