It is very common, especially in academia, to use designs with a small numbers of versions - sometimes only one. In this case one could program the design in our software as all fixed tasks.
These small designs typically make it hard or impossible to estimate interactions. An interesting academic paper just came out concluding that random designs (even truly random designs using the Random design strategy in our software) are more robust than orthogonal (catalog) designs or efficient designs that many researchers use (and which I used to use up until about 5 years ago).
The paper is "D-Efficient or deficient? A robustness analysis of stated choice experimental designs," and it's cite is Walker, J.L., Wang, Y., Thorhauge, M. et al. Theory Decis (2017). https://doi.org/10.1007/s11238-017-9647-3.