I just handed in my PhD in computer science. Our department teaches "best practices" but adherence to them is hardly possible in research:
1) Requirements change constantly, since... it's research. We don't know where exactly we're going and what problems we encounter.
2) Buying faster hardware is usually an option.
3) Time spent on documentation, optimization or anything else that does not directly lead to results is directly detrimental to your progress. The published paper counts, nothing else. If a reviewer ask about reproducibility, just add a git repository link.
4) Most PhD students never worked in industry, and directly come from the Master's to the PhD. Hence there is no place where they'd encounter the need to create scalable systems.
I guess Nr. 3 is has the worst impact. I would love to improve my project w.r.t. stability and reusability, but I would shoot myself into the foot: It's no publishable, I can't mention it a lot in my thesis, and the professorship doesn't check.
1) Requirements change constantly, since... it's research. We don't know where exactly we're going and what problems we encounter.
2) Buying faster hardware is usually an option.
3) Time spent on documentation, optimization or anything else that does not directly lead to results is directly detrimental to your progress. The published paper counts, nothing else. If a reviewer ask about reproducibility, just add a git repository link.
4) Most PhD students never worked in industry, and directly come from the Master's to the PhD. Hence there is no place where they'd encounter the need to create scalable systems.
I guess Nr. 3 is has the worst impact. I would love to improve my project w.r.t. stability and reusability, but I would shoot myself into the foot: It's no publishable, I can't mention it a lot in my thesis, and the professorship doesn't check.