Scientists are used to some level of unavoidable noise from experiments done on the natural world because the natural world is not fully controllable. Thus they are expected to work hard to minimize the uncertainty in their measurements, then characterize what's left and take that into account in their calculations.
They are not expected to make beginner level mistakes when solving simple mathematical equations. Avoidable errors introduced by doing their maths wrong is fundamentally different to unavoidable measurement uncertainty. The whole point of doing simulations in silico is to avoid the problems of the natural world and give you a fully controllable and precisely measurable environment, in which you can re-run the simulation whilst altering only a single variable. That's the justification for creating these sorts of models in the first place!
Perhaps you think the errors were small. The errors in their model due to their bugs were of the same order of magnitude as the predictions themselves. They knew this but presented the outputs to the government as "science" anyway, then systematically attacked the character and motives of anyone who pointed out they were making mistakes. Every single member of that team should have been fired years ago, yet instead what happened is the attitude you're displaying here: a widespread argument that scientists shouldn't be or can't be held to the quality standards we expect of a $10 video game.
How can anyone trust the output of "science" when this attitude is so widespread? We wouldn't accept this kind of argument from people in any other field.
Functionally, what's the difference? The output of their model varied based on environmental factors (how the OS chose to schedule things). The lower-order bits of some of the values got corrupted, due to floating-point errors. In essence, their model had noise, bias, and lower precision than a floating point number – all things that scientists are used to.