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Reliable automatic coding of described rules by 2030?

In a previous question, we asked "How long until a machine-learning system can take a simple text description and turn it into a program coded in C/Python?" While interesting and understandable, this criterion suffers from possible ambiguity and difficulty in exact quantification.

As a supplement, there is an interesting framework, metric, and dataset provided by DeepMind's "card2code" dataset, which consists of Magic the Gathering and Hearthstone cards, in addition to Java and Python implementations of card logic, respectively. The task is to translate the rules/instructions on the card into an accurate code implementing them.

The state-of-the-art at question launch is 16.7% accuracy, achieved by the Syntactic Neural Modal (SNM) presented in this paper; see also EFF AI benchmarks.

Effective semantic parsing is a requisite skills of a general AI system, and code generation is potentially a very powerful capability, so we ask the following question that will benchmark how well a system can write general-purpose code:

Will a code generation system achieve 75% accuracy on the Hearthstone card set by 2030?

Positive resolution occurs if a paper is published or posted describing a code generation system that has credibly achieved 75% on Hearthstone card set by 2030.


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