the calculation of the AUC is not wrong. In the standard implementation (neither optimistic nor pessimistic), we smooth the line by interpolating between the steps of the function
Marius, the ROC's first step in the process I provided above happens at x=0, so the result of "smoothing" the line is not the main diagonal (which leads to an area of 0.5) but the horizontal line y=1 (which leads to an area of 1). The point (0,1) is part of the pessimistic, neutral and optimistic ROCs, so your interpolation should take this into account. I am afraid the RM's calculation is indeed wrong.
If this does not convince you, here is a second intuitive rationale. The AUC is one of the indicators of a model's performance. A model that randomly guesses the class has an AUC of about 0.5. In contrast a model that always predicts the correct class should achieve a much better performance (that is, a higher AUC in this case precisely) than a random guesser, shouldn't it? Such a perfect model is built by the process above, yet according to RM it is as good as a random guesser if performance is measured by AUC. This is an anomaly, and this anomaly is due to the wrong RM's calculation of AUC. Consult (***) below for a reference.
Finally, look at the ROC your software draws in the process I provided: the area under that curve is 1x1=1 indeed, as you have there a rectangle and not a triangle! The drawing is correct, and is inconsistent with the calculation which is clearly wrong.
(***) Reference: Tan, Steinbach, Kumar, Introduction to Data Mining, Addison Wesley, 2005
Subsection 5.7.2 on ROC: " The area under the ROC curve (AUC) provides another approach for evaluating which model is better on average. If the model is perfect, then its area under the ROC curve would equal 1. If the model simply performs random guessing, then its area under the ROC curve would equal 0.5"