<p>The validation of software quality metrics lacks statistical</p><p>significance. One reason for this is that the data collection</p><p>requires quite some effort. To help solve this problem,</p><p>we develop tools for metrics analysis of a large number of</p><p>software projects (146 projects with ca. 70.000 classes and</p><p>interfaces and over 11 million lines of code). Moreover, validation</p><p>of software quality metrics should focus on relevant</p><p>metrics, i.e., correlated metrics need not to be validated independently.</p><p>Based on our statistical basis, we identify correlation</p><p>between several metrics from well-known objectoriented</p><p>metrics suites. Besides, we present early results of</p><p>typical metrics values and possible thresholds.</p>
| Identifer | oai:union.ndltd.org:UPSALLA/oai:DiVA.org:vxu-2562 |
| Date | January 2009 |
| Creators | Barkmann, Henrike |
| Publisher | Växjö University, School of Mathematics and Systems Engineering |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, text |
| Relation | Rapporter från MSI, 1650-2647 ; 1650-2647 |
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