The difficult part of software development has always been the continuing support. Did the chatbot setup a versioning system, a build system, a backup system, a ticketing system, unit tests, and help docs for users. Did it get a conflicting request from two different customers and intelligently resolve them? Was it given a vague problem description that it then had to get on a call with the customer to figure out and hunt down what the customer actually wanted before devising/implementing a solution?

This is the expensive part of software development. Hiring an outsourced, low-tier programmer for almost nothing has always been possible, the low-tier programmer being slightly cheaper doesn't change the game in any meaningful way.

"I gave an LLM a wildly oversimplified version of a complex human task and it did pretty well"

For how long will we be forced to endure different versions of the same article?

The study said 86.66% of the generated software systems were "executed flawlessly."

Like I said yesterday, in a post celebrating how ChatGPT can do medical questions with less than 80% accuracy, that is trash. A company with absolute shit code still has virtually all of it "execute flawlessly." Whether or not code executes it not the bar by which we judge it.

Even if it were to hit 100%, which it does not, there's so much more to making things than this obviously oversimplified simulation of a tech company. Real engineering involves getting people in a room, managing stakeholders, navigating conflicting desires from different stakeholders, getting to know the human beings who need a problem solved, and so on.

LLMs are not capable of this kind of meaningful collaboration, despite all this hype.

"We asked a Chat Bot to solve a problem that already has a solution and it did ok."

It cost less than a dollar to run all those chatbots?

Doubt

Do managment next and lets see who's gonna be replaced first

Plot twist - the AI just cut and paste from stack overflow like real devs.

A test that doesn’t include a real commercial trial or A/B test with real human customers means nothing. Put their game in the App Store and tell us how it performs. We don’t care that it shat out code that compiled successfully. Did it produce something real and usable or just gibberish that passed 86% of its own internal unit tests, which were also gibberish?

But did it work?

At the designing stage, the CEO asked the CTO to "propose a concrete programming language" that would "satisfy the new user's demand," to which the CTO responded with Python. In turn, the CEO said, "Great!" and explained that the programming language's "simplicity and readability make it a popular choice for beginners and experienced developers alike."

I find it extremely funny that project managers are the ones chatbots have learned to immitate perfectly, they already were doing the robot’s work: saying impressive sounding things that are actually borderline gibberish

the CTO responded with Python. In turn, the CEO said, "Great!" and explained that the programming language's "simplicity and readability make it a popular choice for beginners and experienced developers alike."

Yep, that does sound like my CEO.

Researchers, for example, tasked ChatDev to "design a basic Gomoku game," an abstract strategy board game also known as "Five in a Row."

What tech company is making Connect Four as their business model?

What a load of bullshit. If you have a group of researchers provide "minimal human input" to a bunch of LLMs to produce a laughable program like tic-tac-toe, then please just STFU or at least don't tell us it cost $1. This doesn't even have the efficiency of a Google search. This AI hype needs to die quick

This research seems to be more focused on whether the bots would interoperate in different roles to coordinate on a task than about creating the actual software. The idea is to reduce "halucinations" by providing each bot a more specific task.

The paper goes into more about this:

Similar to hallucinations encountered when using LLMs for natural language querying, directly generating entire software systems using LLMs can result in severe code hallucinations, such as incomplete implementation, missing dependencies, and undiscovered bugs. These hallucinations may stem from the lack of specificity in the task and the absence of cross-examination in decision- making. To address these limitations, as Figure 1 shows, we establish a virtual chat -powered software tech nology company – CHATDEV, which comprises of recruited agents from diverse social identities, such as chief officers, professional programmers, test engineers, and art designers. When presented with a task, the diverse agents at CHATDEV collaborate to develop a required software, including an executable system, environmental guidelines, and user manuals. This paradigm revolves around leveraging large language models as the core thinking component, enabling the agents to simulate the entire software development process, circumventing the need for additional model training and mitigating undesirable code hallucinations to some extent.