Many statistics were presented. In the view of the author (and I think he is correct), none of them show evidence for an increased bug rate from Claude. That is absence of evidence (...for the increased bug rate).

The two examples you bring are not claims of absence of evidence, but claims of evidence of absence. The author takes the result as evidence that there is no effect. As I wrote, the author shouldn't do that, because indeed you cannot distinguish between "no effect exists" and "no effect observed". But again, these are (wrong) claims for evidence of absence.

The author can absolutely claim: I did these statistical tests, and none showed evidence that there is an effect. Absence of evidence. It's not a claim that there will never be evidence. Just that there is none from these tests.

Edit: To convert the absence of evidence into evidence for absence, indeed you need to understand the statistical power of your test, and how it is affected by alternate hypotheses. And for that, without having done the math, having only two data points seems very thin.