Existing privacy impact agreements between the ProvLab and Alberta Health Services enabled analysis of patient-level data.
All analysis procedure we adopted indicates that software size impacts agreement on god class detection.
From the results, we consider that the experience impacts agreement on god class detection, but not definitively.
In this section, we present results related to our analysis of how software size impacts agreement on god class detection.
To analyze how design comprehension impacts agreement on god class detection, we considered FinG 1 and FinG 2 experiments, independently.
Due to this, although we do not consider the evidences are very strong, we believe knowledge also impacts agreement to a certain level, and the subject should be further analyzed.
To evaluate how experience impacts agreement on god class detection, we compared results of FinG 1 against results of FinG 2. The experiments had similar setup and the main difference between them was the participants' profile.
We analyzed how software size impacts agreement on god class detection comparing results of FinG 2 and FinG 3. We have to consider that, despite differences on software size, the population of the experiments was formed by different participants subjects.
To investigate whether there are factors that consistently impact annotator agreement we conducted a meta-analytic investigation of annotation studies reporting agreement percentages.
This will not impact the agreement's ratification, but could influence debate over future treaties with other countries.
