The Premature Obituary of Programming

Much yet to do here, but I would expect this would ultimately take over many such capabilities.    Especially when integrated with automated testing for threat weaknesses, which could be updated to changing contexts.  

The Premature Obituary of Programming  (abstract)     By Daniel M. Yellin

Communications of the ACM, February 2023, Vol. 66 No. 2, Pages 41-44  10.1145/3555367

Deep learning (DL) has arrived, not only for natural language, speech, and image processing but also for coding, which I refer to as deep programming (DP). DP is used to detect similar programs, find relevant code, translate programs from one language to another, discover software defects, and to synthesize programs from a natural language description. The advent of large transformer language models10 is now being applied to programs with encouraging results. Just like DL is enabled by the enormous amount of textual and image data available on the Internet, DP is enabled by the vast amount of code available in open source repositories such as GitHub, as well as the ability to reuse libraries via modern package managers such as npm and pip. Two trail-blazing transformer-based DP systems are OpenAI's Codex8 and Deepmind's AlphaCode.18 The former is used in the Github Copilot project14 and integrates with development environments to automatically suggest code to developers. The latter generates code to solve problems presented at coding competitions. Both achieve amazing results. Multiple efforts are under way to establish code repositories for benchmarking DP, such as CodeXGLUE19 and CodeNET.20

The advent of DP systems has led to a few sensational headlines declaring that in the not-too-distant future coding will be done by computers, not humans.1 As DL technologies get even better and more code is deposited into public repositories, programmers will be replaced by specification writers outlining what code they want in natural language and presto, the code appears. This Viewpoint argues that while DP will influence software engineering and programming, its effects will be more incremental than the current hype suggests. To get away from the hype, I provide a careful analysis of the problem. I also argue that for DP to broaden its influence, it needs to take a more multidisciplinary approach, incorporating techniques from software engineering, program synthesis, and symbolic reasoning, to name just a few. Note I do not argue with the premise that DL will be used to solve many problems that are solved today by traditional programming methods16 and that software engineering will evolve to make such systems robust.17 In this Viewpoint, I am addressing the orthogonal question of using DL to synthesize programs themselves. ... '