Defect Density Metric (DD) is one of the most widely used metrics in the industry in addition to the code row (LOC) metric. The defect density metrics are defined as follows:
DD = Defects (k) LOC
Defects is the number of defects after the software issuance (general 6 months). Here we will not discuss metrics in practical applications. The following chart can illustrate the effect when using the DD metric.
Version 1Version 2 # DefectsLOCDefects / LOCDefectsLOCDefects / LOCRelation 1 12 777 0.01544 3 55 0.05455 <2 5 110 0.04545 6 110 0.05455 <3 2 110 0.01818 3 110 0.02727 <4 3 110 0.02727 4 110 0.03636 <5 6 1000 0.00600 70 10000 0.00700
Table 1: Defect density metrics. Version 1 correspondence to version 2 defect density metrics
Suppose we have two programs, versions 1, and version 2, and each version contains 5 modules. We examine its defect density metrics for each version. We can see that each module of version 1 has a smaller defect density metric than each module of version 2. When each version of the defect number (DEFECTS) is added to the code line (LOC), we get a total of a total. The result was surprising, although the defect density metrics of each module of version 1 were smaller than version 2, but the defect density metrics of all modules of version 1 were larger than version 2. The problem is: How did this happen? Please see the conclusions given in Third Lecture
