Questo signore si dichiara Agilist, una delle metodologie moderne di sviluppo software, ma sembra non aderire ad alcuni suoi principi fondamentali, quali:
1. Pairwise programming: qui un copilot funge benissimo da secondo nella coppia 2. Deliver working software frequently: il che significa dedicare gran parte del tempo a scrivere codice — Beppe > On 29 Feb 2024, at 12:32, nexa-requ...@server-nexa.polito.it wrote: > > From: Daniela Tafani <daniela.taf...@unipi.it > <mailto:daniela.taf...@unipi.it>> > To: "nexa@server-nexa.polito.it <mailto:nexa@server-nexa.polito.it>" > <nexa@server-nexa.polito.it <mailto:nexa@server-nexa.polito.it>> > Subject: [nexa] LMs and AI make software development harder > Message-ID: <1ff9c3d20cd643eabb1bbe07a054d...@unipi.it > <mailto:1ff9c3d20cd643eabb1bbe07a054d...@unipi.it>> > Content-Type: text/plain; charset="Windows-1252" > > LLMs and AI make software development harder > > LLMs and AI make software development harder. Wait, what? Isn’t the whole > point of AI to make writing code easier? Well, yes. But writing code is the > easy part of software development. The hard part is understanding the > problem, designing business logic and debugging tough bugs. And that’s where > AI code assistants like copilot or chatgpt make our job harder, as they strip > a way the easy parts of our job and only leave us with the hard parts and > make it harder for new developers to master the craft of software development. > Coding is the easy part? > > Is coding really that easy? No, not exactly easy - mastering a programming > language still takes years of practice. But when looking at software > development as a whole, writing code is one of the easier part and it is no > wonder that chatgpt and copilot can write decent code. First, they have > trained on millions of lines of code and second, code is by its nature very > easy to understand for a machine as programming languages are very structured > languages with limited vocabularies. For a LLM it is probably much easier to > learn than natural language. > > Programming languages are just very powerful tools that we use to solve > problems > > In the end, programming languages are just very powerful tools that we use to > solve problems. And the hard part is not the learning tool, but understanding > the problem and designing a solution for it. This is instantly obvious as > most software engineering problems could be solved by a lot of different > programming languages, which one to pick is a matter of context or even > personal preference. > > Another indicator that programming is that easy part is, that the more senior > a software developer gets, the less time they usually spend writing code. > Instead seniors spend more time understanding the problem, designing the > solution, jumping in to debug tough bugs or doing design decisions and of > course mentoring junior team members. While this might not be true for every > senior developer, when looking at my software development bubble this is a > clear trend. > The hard parts of software development > > Copilot and other AI assistants are a great help for developers, but they are > not flawless. A part of it is natural, as they are trained on existing code > without any context and there are also some bad habits from the training data > that code assistants might have picked up. And while this might get optimized > over time, at the moment it means that developers still have to review the > code that is generated by the AI code assistants. And reviews are hard - > especially if one cannot query the author of the code for their intent. > > And even if the code is good enough, it might still introduce flaws into the > control logic of a program, might be missing edge cases or introduce a > regression bug when integrated into an existing code base. This means that > developers have to debug the code that is generated by the AI code assistants > in case of an error. And debugging is hard - especially for these kind of > problems where it might be hard to recreate the circumstances that cause the > bug in the first place. > > As the generated code heavily depends on the context we give the AI code > assistants, this means that we have to be very precise in our descriptions > which means that we have to understand the problem very well, which requires > domain knowledge and context awareness on the side of the developer. Even if > we just focus on the technical part, being aware of the surrounding > architecture and the existing code is crucial to get good results. > > Granted we could ask LLMs like chatgpt for help with integration into the > codebase or we could just pass it the whole codebase and let it redesign > everything. But apart from requiring lot of input to give enough context > debugging in an unfamiliar codebase is even tougher than debugging stuff that > you wrote yourself. > > And then there is the whole thing about figuring out what exactly our product > should do, how it should behave and how it should look like. At the moment > this still requires a lot of human smarts and while AI tools might allow us > to iterate faster on figuring out what we want to build in the end it is > still a human that has to make the decision. > AI generated software development is exhausting > > It seems a given that AI assistants will change our job by automating away > writing code and even helping us with some design decisions. It is very > convenient that we can ask chatgpt questions regarding system design and get > reasonable answers. What is still left to us is making the decision on which > answer to pick and which prompt to give to the LLM to get the results that we > need. And this is very exhausting - decision fatigue is a thing and it is > very real. Already before AI code assistants the limiting factor in the speed > of delivering software was not the often the decision making process of an > organization or a team - not writing the code. > > The limiting factor in delivery speed is decision making, not writing code > > On top of that is that current company structures will most likely still hold > software developers accountable for the code that is running in a product, > not the AI code assistants that wrote them in the first place. This will add > another layer of stress on it, not just do we need to make more decisions > faster, we are also to blame if the AI code assistants make a mistake. > > And if there is a mistake then the debugging needs to be done, which often > needs a lot of context and background knowledge to be efficient. AI tools are > of less help there, because they cannot figure out context changes by > themselves. They might help us with the easy part of debugging like running > tests with different variations, to narrow down the cause but finding the > prompt for an LLM to generate the fix will still be on us. > Are AI tools replacing developers? > > AI assistants might lower the initial hurdle to get into software > development, but they will not make it easier to become a good, experienced > software developer. Most of the senior developers I know gained the > background knowledge and context needed to formulate complex solutions from > years of slogging through (bad) code and learning from their mistakes. This > might be an inefficient way of learning but it is very effective in building > up the domain knowledge that is needed for software development. This > knowledge is also something that is very hard to teach in a formal way, as > books or online tutorials by nature are somewhat generic and and adaption to > real life situations still needs hands-on experience. > > As I see it, broad usage of AI tools will change the the skill distribution > of software developers. We might end up with a lot more junior developers > that are able to write code - or at least prompt the LLMs write the code - > but lack the deep understanding of software development to be efficient in > decision making. On the other hands senior developers that have acquired the > context and domain knowledge will be fewer and fewer as the effort to acquire > this knowledge will be higher as AI tools will hide away the parts that would > enable us to learn unless the generated code is reviewed in-depth, which then > raises the question if we gain that much efficiency through the tools at all. > > So are AI tools replacing developers? Currently no, they will transform the > job of a developer but they will not replace them. The question is how we as > an industry will make sure that we retain the knowledge and experience that > we have gained over the years. It will also raise the question how we handle > the human side of software development, as the job will either become more > boring because we just feed machines with prompts, yet more stressful because > we have to make more hard decisions faster. Or maybe AI tools are really just > a hype and a fad and nothing will change at all. > Written on November 3, 2023 > > > https://dominikberner.ch/ai-tools-make-our-job-harder/
_______________________________________________ nexa mailing list nexa@server-nexa.polito.it https://server-nexa.polito.it/cgi-bin/mailman/listinfo/nexa
