• AI-automated code production will speed up software development by generating more code in less time.
• How far will AI go in replacing or augmenting human coders’ labor? According to the experts polled by IEEE Spectrum, coding as we know it may be on its way out.
• However, computer programming and software development appear to be largely human undertakings for the foreseeable future.
• AI-automated code production is at the vanguard of a wider movement that will allow anybody to write software without knowing how to code.
• In the future, hand-coding software programs will resemble hand-knitting clothes.

Are programmers doomed? Since OpenAI’s huge language model, GPT-3, astonished everyone with its capacity to construct HTML websites from basic textual instructions, that question has circled computer-programming groups.

Is AI-automated code production the direct alternative to human labor?

Rapid-fire improvements in AI-automated code production in the months afterward have resulted in systems that can build full, albeit rudimentary, computer programs from natural-language descriptions (spoken or written human language) and automated coding assistants that speed up computer programmers’ jobs. How far will AI go in replacing or supplementing the labor of human coders?

According to the experts surveyed by IEEE Spectrum, the bad news is that coding as we know it may be doomed. However, the good news is that computer programming and software development look to be relatively human endeavors for the foreseeable future. Meanwhile, AI-automated code creation will accelerate software development by allowing more code to be generated in less time.

“I don’t believe AI is anywhere near replacing human developers,” said Vasi Philomin, Amazon’s vice president of AI services, adding that while AI tools will alleviate coders from repetitive duties, the creative job of computer programming will remain.

If someone wants to be a developer in ten years, they won’t necessarily need to learn a programming language. Instead, students must comprehend the semantics, ideas, and logical sequences involved in developing a computer program. This will make software development available to a much larger populace.

When the first electronic computers were programmed in the 1940s, programmers used numerical machine code. It wasn’t until the mid-1950s that Grace Hopper and her colleagues at Remington Rand created FLOW-MATIC, which allowed programmers to construct programs with a restricted English vocabulary. Since then, programming has progressed through increasingly efficient languages, allowing programmers to be more productive.

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AI-automated code production is at the forefront of a larger trend to enable anyone to develop software without needing to code at all. People can already develop machine-learning models using platforms like Akkio’s basic drag-and-drop and button-click functionalities. Microsoft’s Power Platform, which encompasses a range of low-code solutions, allows users to create simple applications by just describing them.

AI-automated code production is here to make things easier for developers

Amazon announced CodeWhisperer in June, a coding aid for programmers similar to GitHub’s Copilot, which was originally available in restricted preview in June 2021. Both methods rely on large language models (LLMs) trained on vast code libraries. Both provide autocomplete recommendations when a programmer writes code and provide executable instructions based on basic natural-language words.

According to a GitHub poll of 2,000 developers, Copilot decreases the time it takes to complete specific coding jobs in half and increases overall developer satisfaction with their work. However, in order to get beyond autocompletion, the computer must be taught the purpose. Software requirements are frequently ambiguous, and spoken language is notoriously imprecise.

Peter Schrammel, the cofounder of Diffblue, which automates the development of unit tests for Java, said, “To resolve all these ambiguities in English written specification, there needs to be some incremental refinement, some conversation between the human and the machine.”

To overcome these issues, Microsoft researchers have proposed adding a feedback system to LLM-based code creation, where the computer asks the programmer to explain any ambiguities before creating the code.

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TiCoder, an interactive system, refines and formalizes user intent by creating what is known as a “test-driven user-intent formalization,” which aims to divine the programmer’s algorithmic goal and then construct code that is compatible with the indicated intents through iterative feedback.

TiCoder, according to their article, enhances the correctness of automatically produced code by up to 85 percent when tested against the Mostly Basic Programming Problems (MBPP) benchmark. MBPP, which is intended to assess AI-generated code production, is made up of about 1,000 crowd-sourced Python programming problems that are supposed to be solved by entry-level programmers.

A unit of code, which might be hundreds of lines long, is the smallest element of a program that can be independently maintained and executed. A suite of unit tests, often consisting of dozens of unit tests with 10 to 20 lines of code apiece, ensures that the unit runs as intended so that when the units are stacked together, the program functions as intended.

Unit tests are important for troubleshooting individual functions as well as finding mistakes while manually changing code. A unit test may be used as the specification for the unit of code and to aid programmers in writing clean, bug-free code. While few programmers practice genuine test-driven development, in which unit tests are developed first, unit tests and units are frequently written concurrently.

Quality control will be easier with AI-automated code production

According to a Diffblue survey, developers spend around 35% of their time writing quality-control tests (as opposed to producing code for production usage); hence there are huge productivity benefits to be realized simply by utilizing the AI-automated code production for a portion of this.

Meanwhile, Github’s Copilot, Amazon’s CodeWhisperer, and AI programming assistant packages can be utilized as interactive auto-completion tools for creating unit tests. The programmer is offered options and chooses the one they believe would work best. Diffblue’s Diffblue Cover system automatically employs reinforcement learning to build unit tests without human interaction.

DeepMind, Google’s U.K.-based artificial intelligence division, advanced entirely automatic code production earlier this year with AlphaCode, a massive language model that can generate basic computer programs from natural-language instructions.

AlphaCode employs an encoder-decoder transformer architecture, first encoding the problem’s natural-language description and then decoding the resultant vector into code for a solution. The model was initially trained on the GitHub code repository until it could write reasonable-looking code.

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DeepMind employed 15,000 pairs of natural-language issue descriptions and successful code solutions from previous coding competitions to construct a specific data set of input-output samples to fine-tune the model. After AlphaCode had been educated and tweaked, it was tested against issues it had never encountered before.

The final stage was to develop many solutions before using a filtering technique to choose the best one. “We created many different program possibilities by sampling the language model almost a million times,” said DeepMind’s deep-learning project leader, Oriol Vinyals.

DeepMind utilizes a clustering method to partition the answers into groups in order to optimize the sample-selection process. The clustering method groups working solutions together, making it simpler to identify a limited selection of alternatives that are likely to work as well as those produced by human programmers.

DeepMind put the system to the test by submitting ten AlphaCode-written programs to a human coding competition on the popular Codeforces website, where its answers placed in the top 54%.

In a recent interview, Vinyals posed a rhetorical question, “To generate a program, will you just write it in natural language, no coding required, and then the solution comes out at the other end? I believe so.”

Vinyals and others warn that achieving that aim may take decades. “We are still very far away from when a person would be able to tell a computer about the requirements for an arbitrary complex computer program and have that automatically get coded,” said the founder and CEO of Landing AI and a Google Brain pioneer, Andrew Ng.

However, given the rapid advancement of AI-automated code production in recent years, it is certain that AI-automated code production systems will someday be able to produce code from natural-language instructions. Hand-coding software applications will become more akin to hand-knitting garments.

To offer a computer natural-language instructions, developers will still need to comprehend some logic and function principles, as well as how to arrange things. Even if students do not master specific programming languages or write computer code, they will still need to study basic programming. As a result, a broader spectrum of programmers will be able to produce more and more diverse types of software.

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According to Schrammel of Diffblue, AI-automated code production will free up software professionals to focus on more demanding and creative jobs. However, he says, at least one encounter with a person will be required to check that what the computer has grasped is what the human meant. He said, “Software developers will not lose their jobs because an automation tool replaces them. There always will be more software that needs to be written.”