Generalization of trace-trained model findings beyond Pencil Code

Determine whether the improvements observed from training language models on full edit traces of Pencil Code—such as enhanced modeling of student behaviors and steerable code generation—extend to programming platforms beyond Pencil Code through empirical evaluation.

Background

The paper introduces a large-scale dataset of 3.8 million programming edit traces from Pencil Code and shows that LLMs trained on real edit traces better capture student behaviors and produce more controllable, stylistically aligned code than models trained on final programs or synthetic traces. While these results are demonstrated within the Pencil Code environment (which uses CoffeeScript/JavaScript and a block-text editor), it remains to be empirically established whether the same benefits hold for other programming platforms and languages.

The authors posit that similarities between Pencil Code libraries (e.g., turtle graphics) and those in other ecosystems (such as Python) suggest positive transfer, but they explicitly defer the empirical test of this external validity to future work.