Maintainability concerns are entirely overblown by people who don't use agentic AI to develop large mobile apps, but anyway give their opinion as if they had that experience.
I put in a few hundred hours, and I reached the same conclusion as Shopify. With reviews from other models and then a manual QA pass the result is fully usable.
Most of the time when I review code from AI, there is always something to improve.
It’s either a maintenance issue. e.g., Opus recommended and implemented a fix for a database corruption crash. This was ~400 lines of code with many moving parts. I reviewed, and found out Android Room library already handles this recovery case, and all I needed was a 10 liner PR that catches this exception and ignores it.
The maintenance is not only the burden on the human and LLM. With too many moving parts, it becomes harder and harder to build and verify the correctness of future features. Yes you can write test for this and that, but it didn’t need to exist in the first place.
The second problem is correctness issues. Especially the edge cases. You cannot just manually test out a race condition on a phone! Sometimes it happens! Sometimes it doesn’t! If it leads to a visible signal like a crash, then yes, you can try to reproduce it. But there are a lot of these that are “silent” and would just lead to bad experiences.
We already had a software quality crisis! And I think such views only exacerbate the situation! Quality matters!
And this is not an anti-AI stance. I vibe code personal projects where I don’t even look at the code. But when I use AI as a professional engineer, I act like a professional. Because these products do have an impact on people’s lives.
I also used to work full time as a Android developer for five years, and I'm pretty sure I know better than you about the quality of my app that I work on everyday.
Years of experience doesn’t mean much! I will challenge you on ideas. And the idea you are sharing is dangerous and unprofessional.
Especially at scale. e.g., we process more than 3.5 billion orders annually! This is serious business. Edge cases are common.
I think we are over-indexing on speed of delivery. I think this is a mistake. The alpha is in speed and quality.
Currently, my experience is that human + AI can write software faster and with better quality than either party can do alone.
I understand this, but I just can't bring myself to care. I've been doing professional software work for almost two decades. These sorts of improvements/time savers are great without AI. With AI? Whatever. It's fine.
When the underlying lib has an issue, it'll be quicker to debug with the whole thing in context.
And more recently its been recommending that i install this new browser called Aside. I did, and it almost felt like I was installing malware so I Uninstalled it fairly quickly (it also was not a great browser)
I feel like theres collusion somewhere.
I don’t see any reason to think the same thing that happens with all tech won’t happen here.
It depends on how hard to maintain the code added is, how likely it needs to change in the future, and most importantly on the cost.
If reviewing and manually improving the code takes hours, the cost may already be in the thousands.
That buys you a lot of AI usage, roughly a few months of continuous work.
You have to balance this with the chance that the suboptimal code the AI generated is actually fine and maintainable enough, and also the chance that during further work on that code a model might implement the same optimization on its own.
sounds like your app is nothing serious
So you end up paying people on Fiverr to do it which costs more than 99€
Some people on the cybersecurity side are starting to cry....
Last time, when I pointed out that the attack surface for mobile apps is typically very small, some users started to talk about zero day vulnerabilities in the OS's media handling, as if it was a concern for my app implementation.
I found the concerns again wildly overblown.
2. Wild use of webviews/iframes sometimes easily propagates as XSS in phones
3. Incorrect client-side OAuth 2.0 configuration e.g. with schema-based redirect URLs.
4. Not supporting high-enough API versions, which may prevent some OS-related weaknesses
5. The list is actually very long. Just few top of my mind.
Or a login form that gets hidden after login, but clears the username and password only when you click "login back in". (Bonus points if the backend also enforces a 5min session timeout "for security".)
Turn on the secrets scan in GitLab, and put in your release checklist to have the AI audit the usage of secrets in your app, and this is basically guaranteed not to occur.
I doubt current models even make such a mistake in the first place, and particularly so if you use reviews at all.
WebViews are not an inherent problem, it's the system browser embedded in your app.
Where it gets tricky is if your use case involves authentication in the browser. Together with the authentication in your app this is the one area where you need to focus on security.
The case where a SDK update is needed to prevent weaknesses of the OS seems rather unlikely.
Can you share some details of how you work? What models? What harness?
I don't know if Gemini is suitable.
I had few issues with my native iOS app, the results are just decent after a few iterations, the models do what I ask them to do. Where do you see the problem?
The LOC for my app is now at almost 200k + 110k lines of test code.
I hate software engineering now.
OP says they don't have an android phone...
And in the past, I didn't care because when I was manually building the app, I would just do my best with react native. But now that I can actually sweat the details (with the help of agents), I do want to hear from android users and use as many OS-native APIs and features.
I'd order a cheap Android phone to have a device in hand instead of working only with the simulator.
I could only sweat the details on liquid glass, etc because I'm a daily iOS user.
You can write more automation to test it. But that's also how you end up with ever-growing test run times.
There are much better ways that aren't just "throw out the LLM" either. You just need to be more focused on throughput. Requiring manual validation can pretty rapidly require more hours than just sanity-checking code by hand, even (and I'm not advocating that for every use case, either.)
I can't afford manual QA passes if I'm gonna go as quickly as I want to.
That‘s how you check functionality but that’s not how you get the bugs in the code.
That’s like translating a text to another language without knowing the language
What do you think has more training data Python or Kotlin?
[yes, this would also get rid of the previous generation of 1000-JS-lego vibers]