Programming

The amount of CPU time compiling code is usually negligible compared to CPU time at runtime. Your comparison only really works if you are comparing against something like Rust, where less bugs are introduced due to certain guarantees by the language.

Regarding "language constructs" it really depends on what you mean. For example using numpy in python is kind of cheating because numpy is implemented in C. However using something like the algorithm libraries in Rust woulf be considered fair game since they are likely written in Rust itself.

I find this paper false/misleading.

They presented their methodology in an open and clear way and provide their data for everyone to interpret. You can disagree with conclusions but it's pretty harsh to say it's "misleading" simply because you don't like the results.

They just translated one algorithm in many languages, without using the language constructs or specificities to make the algorithm decent performant wise.

They used two datasets, if you read the paper... It wasn't "one algorithm" it was several from publicly available implementations of those algorithms. They chose an "optimized" set of algorithms from "The Computer Language Benchmarks Game" to produce results for well-optimized code in each language. They then used implementations of various algorithms from Rosetta Code which contained more... typical implementations that don't have a heavy focus on performance.

In fact - using "typical language constructs or specificities" hurt the Java language implementations since List is slower than using arrays. It performed much better (surprisingly well actually) in the optimized tests than in the Rosetta Code tests.