Automatic programming: The open issue?

26 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Evaluating Large Language Models Trained on Code (2021, neutral): “Indeed, the synthesis and automatic programming community (O’Neill & Spector, 2019) have recently called for principled benchmarks and grand challenge problems to be made in order to adopt a scientiﬁcally rigorous approach to compare synthesis methodologies against.”
A Hazard Analysis Framework for Code Synthesis Large Language Models (2022, background): “Indeed, the Synthesis and automatic programming community [29] have recently called for principled benchmarks and grand challenge problems to be made in order to adopt a scientifically rigorous approach against which to compare synthesis methodologies.”