It is an unfortunate artifact of history and culture but academia is still in the dark ages with regards to sharing source code. Serious computational researchers will often provide at least a detailed algorithm, and often the code as well. But dabblers who had to learn how to code "on the job", or people very set in their ways and traditions, will often neglect to do so and publications standard rarely require it, even though you would think that anyone in their right mind would agree that all scientific code should be open source (or at least shared with reviewers) if it is to be worth discussion.
Unfortunately, the established culture does not expect sharing of code, even though the in the analogous case of physical experiments there is an expectation of sharing the exact process down to every detail of method and material such that it may be exactly reproduced by other researchers. I would suspect that the reason for this is that in the grand scheme of things computers are a relatively recent tool of science, and the ability to easily share code is still more recent. That said, we've had ubiquitous internet and zero effort code hosting like Github for over a decade now, so if you ask me, it's about damn time. But it looks like there is still quite a bit of inertia.
I have been reviewing the articles for various top-rank journals and conferences for the last few years. After all these experiences, I can tell you there is no way to confirm the correctness of simulation results. Therefore, I usually make comments on design, procedure, mathematical and analytical analysis.
That's about the best you can do. You can also try to intuitively conjecture based on the rough description (if any) of the computational approach, whether the results achieved are credible or not. But ultimately it is impossible to know for sure.
I try to add a little nag at the end of my reviews about releasing the source code, although I don't think it gets taken seriously very often.
In the results section, I can ask some questions about why this or that is so, but how can I judge if the simulation was really performed or these are just fabricated graphs?
Well, the way you phrase it, you can't really know if any graph or result is fabricated, unless maybe you were personally present while the research was done. There is inevitably an element of trust. But without source code, even if you do trust, you cannot offer meaningful critique about some computational parts of the paper. Obviously you can still comment on initial assumptions and the approach chosen. You can comment on how the results are interpreted. But the implementation itself is out of reach until you can see the code. Actually, even providing a detailed algorithm would not be sufficient: The authors' implementation may not necessarily be an exact match for the algorithm they intended.
This question came in my mind because I observed on few occasions, during review process, a reviewer ask for including new results, which in my opinion required a lot of coding and effort to implement, but author responded within 7-10 days with new results and improved article.
I don't think it's fair to be suspicious just because they did it a little too quickly. They may just be very good at coding. Personally, my development rate is very variable: Sometimes things just click and I can write code really fast, sometimes simple things take forever. They may know of easier ways to implement the change than you are aware. They may have already coded something similar in separate work and been able to repurpose it quickly.
If someone were to falsify results, I think they would either respond right away because they don't care, or wait "long enough" to avoid suspicion. If they bothered to wait at all, I don't think they would jeopardize the whole enterprise by waiting too little.