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Heatmap Clustering to Understand the Misbehaviours Exposed by Automatically Generated Test Inputs
Try to make the execution faster on datasets when possible (e.g., use vectorized functions or apply_raw)
Change the code so that whatever dataset can be plugged in. Now it must be a TF module.
Make sure to repeat all the non-deterministic steps in all the runs.
Correlate the features chosen by the evaluators during the sufficiency pilot study for the high-level approaches with the features' combinations actually used.
Correlate the regions chosen by the evaluators during the sufficiency pilot study for the low-level approaches with the ranking of the regions.
The images for the labelability should show two taggers at a time, the RQ is whether it exists a property such that it distinguishes the two clusters.
The clusters are chosen based on the number of misclassified elements in the cluster.
When choosing the images, we consider only images of misclassified entries. We select n
images as the medoid of the cluster plus its n - 1
closest neighbors.
Correlate the agreement measured during the understandability pilot study with the purity of the cluster.
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