r/SoftwareEngineering

Hey Reddit,
I am the creator of agent-qa.

AI has accelerated development which allows devs to build products at lightning speed. But the confidence whether it works isn't there. Though coding agents can write tests on their own but they greedily writes tests to make them pass.

The intention of building agent-qa is to provide an AI native solution to E2E testing.
I have used playwright as a kernel for executing planned actions in the QA harness.

Looking forward to feedback.

GitHub - https://github.com/vostride/agent-qa
Consider giving it a ⭐
Thanks!

Demo - vostride.com/

I use a proxy that I developed myself, and it's this proxy that handles executing the APIs on other servers. All I have to do is register the different APIs and their characteristics in my proxy (URLs, endpoint, method, parameter type, etc.). Then, all I have to do is integrate my proxy's API into my code, specify which API, how, and which part of the API I want to call to my proxy, and choose the same response format for all my APIs. Then, my proxy handles reconstructing and executing the requests on its servers using the API data I've registered, retrieving the responses, cleaning them, and formatting them before sending them back to my project. I'm curious to see what you use or have developed for this type of problem in particular.

i dont understand why every app in nowadays have written in electronjs or something like that instead of writing in native. For example if you are using postman, mongodb compass and vs code its already 3 browsers taking your resources. How we end up in a situation like this. Its bloated AF

i dont understand why every app in nowadays have written in electronjs or something like that instead of writing in native. For example if you are using postman, mongodb compass and vs code its already 3 browsers taking your resources. How we end up in a situation like this. Its bloated AF

Same as the title

I’m back on the job market after 5 years at my current company, and the interview landscape has changed a lot. The last time I was doing this, AI wasn’t really a thing yet.

I’ve done a few interviews now where they ask me to build and deploy a small full stack app. These are tasks I can handle fine in a normal environment — but I keep getting in my head during the actual interview about how much to lean on AI.

The companies explicitly say AI usage is allowed and encouraged, but I’m stuck on the etiquette of it. I don’t want to look like I’m just prompting my way through the problem with zero understanding, but I also don’t want to look old-school by barely using it when they clearly expect me to.

For those who have successfully navigated the current interview process, do y’all have any tips?

hey friends i am a 3rd year CSE student. Currently, I am prepared for place along side with dsa leetcode and context, and I have to study cs core subjects i am confused like I read theory, but how i prepare these subjects for interview focused or only on those topics which is import if someone has knowledge and gives an interview before please help and share guidance

Hey all, I'm a former reporter turned software engineer, and wrote an article today about how leadership in the software engineering space need basic media training:

https://harrisoncramer.me/software-managers-are-media-illiterate/

Do you guys agree? Would love to hear your thoughts.

Hi devs — what kind of code review do you actually prefer?

Quick ✅ “Looks good”

Detailed senior-engineer feedback

AI-assisted reviews with explanations

Or do most teams just wait for one green flag before merging?

With AI agents doing more implementation work, agents can be given context about a feature contract, but when its time to put up a PR, that same contract often lives remotely in something like Google Drive or Confluence and its changes, or lack of changes, are not part of the PR and require manual updating.

There are doc-sync tools like DeepDocs, DocuWriter, and AutoDoc that keep documentation updated when code changes, but what about enforcement in the other direction? When code diverges from the spec without a corresponding spec update, are there mechanisms teams use to flag that the contract has been violated?

How are teams actually handling this? Or is it just an unsolved problem most quietly live with?

An interesting observation: I’ve stopped using the LLM-powered autocomplete in my IDE.

At first, it was one of the key features for me. It felt extremely convenient: you start writing a function in your code, and the LLM completes it based on common sense or the context from the open tabs.

But the most interesting thing is that back when LLM autocomplete was useful and in demand, I had already written a script that could go through the source files, let me select what I needed, and prepare the context to feed into an LLM chat so it could tell me what to add or fix. I worked like that for about six months.

And even that is gone now.

These days it’s easier to open a CLI interface with a coding agent, without even launching the IDE. You describe what you need, use @ to point it to the files it should inspect or modify, and that’s it. Everything is changing at an absolutely insane speed.

Basically, the only things I still use an IDE for are nice Git diff visualization, step-by-step debugging, and the ability to click on functions and jump into their implementation. In other words, code navigation. And even that functionality is only needed in about 5-10% of my work.

It’s interesting to think what comes next.

What I mean is that I have an all-products subscription from JetBrains because I program in several languages at once: Java, Scala, Python, TypeScript, and Rust. But the question is: why keep paying for it?

Sure, once every 2-3 months, some unclear issue appears, and debugging helps find it. On the other hand, I’ve already tried another approach: I give an LLM agent the path to the log of what is happening in the program. If it doesn’t have enough information to solve the problem, I ask it to add more logs, then I describe the problem again and ask it to understand from the logs what needs to be fixed.

And of course, it’s very convenient to ask an LLM to write tests. That really is useful. If the tests fail, it looks at what it changed in the code and what it broke. When the LLM starts going in circles, I directly tell it: cover this with tests and read the logs to understand how everything works. Very convenient.

One of my latest techniques is using a plan.md file. When I ask it to solve a complex task, I first ask it to create a work plan and write it into plan.md. Then I simply ask it to complete one task from that file at a time. And step by step, through small tasks, the LLM eventually gets to the result.

Overall, I think the industry is changing a lot.

Share your experience: how has your approach to programming changed? I’d be interested to hear how things have changed for others.

But please don’t reply if you have never programmed before and have just discovered vibe coding. I’ve been programming myself since 1990, which means I wrote my first program 36 years ago...