Automatic programming has seen increasing popularity due to the emergence of
tools like GitHub Copilot which rely on Large Language Models (LLMs). At the
same time, automatically generated code faces challenges during deployment due
to concerns around quality and trust. In this article, we study automated
coding in a general sense and study the concerns around code quality, security
and related issues of programmer responsibility. These are key issues for
organizations while deciding on the usage of automatically generated code. We
discuss how advances in software engineering such as program repair and
analysis can enable automatic programming. We conclude with a forward looking
view, focusing on the programming environment of the near future, where
programmers may need to switch to different roles to fully utilize the power of
automatic programming. Automated repair of automatically generated programs
from LLMs, can help produce higher assurance code from LLMs, along with
evidence of assurance

自动编程的流行增加了对代码质量、安全性、程序修复和程序员责任等相关问题的关注，这些问题是组织在决定使用自动生成代码时所关心的关键问题，本文研究了自动编程的各个方面，并讨论了软件工程的进展，如程序修复和分析可以实现自动编程，并对未来的编程环境提出了展望，认为程序员可能需要转变角色以充分利用自动编程的力量。通过从大型语言模型自动生成的程序进行自动修复，可以生成更可靠的代码，并提供可靠性证据。

自动编程：大型语言模型与更多

Automatic Programming: Large Language Models and Beyond

We present a new class of neurons, ARNs, which give a cross entropy on test
data that is up to three times lower than the one achieved by carefully
optimized LSTM neurons. The explanations for the huge improvements that often
are achieved are elaborate skip connections through time, up to four internal
memory states per neuron and a number of novel activation functions including
small quadratic forms. The new neurons were generated using automatic
programming and are formulated as pure functional programs that easily can be
transformed. We present experimental results for eight datasets and found
excellent improvements for seven of them, but LSTM remained the best for one
dataset. The results are so promising that automatic programming to generate
new neurons should become part of the standard operating procedure for any
machine learning practitioner who works on time series data such as sensor
signals.

我们介绍了一种新的神经元 ——ARNs，它们通过时间上的跨度、每个神经元的内部存储状态以及一些新的激活函数来提高机器学习在时间序列数据上的表现。利用自动编程生成这些新的神经元，可以显著提高性能，成为任何处理时间序列数据（如传感器信号）的机器学习从业者标准操作流程的一部分。