Okay, so let's dive into AI and how it's changing software development.
Sounds good.
We're taking a look at this paper today.
Okay.
It's called SES-TUE Bench.
Can language models resolve real world GitHub issues?
All right.
It's a pretty interesting concept they're exploring here.
It's about seeing how AI can handle real coding challenges.
Yeah, you know, moving beyond those like simplified coding puzzles we've seen in the past.
Right, exactly.
This is about testing AI in a more realistic setting.
So like real world scenarios, the kind of stuff developers face every day.
Exactly.
They're putting these language models to the test.
You know, the same kind of AI that powers tools like chat GPT or Claude.
Oh, well.
So we're not just talking about writing code from scratch.
You know.
We're talking about AI actually diving into existing projects.
Yeah, like massive messy code bases.
Finding and fixing bugs and even adding new features to really complex software.
Exactly.
And the researchers behind this paper are making a really interesting point.
They're saying that the existing benchmarks we have for AI, they're becoming too easy.
They're not really pushing the limits of what AI can do these days.
Yeah, I can see that.
I mean, think about how fast this field is moving.
Exactly.
And real world software development.
That's a whole different ball game.
Absolutely.
It's not just about knowing the syntax.
It's about understanding how everything sits together.
Right.
You've got these huge open source projects on GitHub.
Thousands of files.
Hundreds of thousands of lines of code.
And all these intricate interactions between different parts of the system.
It's mind bogglingly complex.
Definitely not for the faint of heart.
So that's where SWE Bench comes in.
OK, tell me more.
It's this new benchmark.
OK.
And it's a collection of over 2,000 real software engineering problems.
Wow.
2,000.
They can straight from GitHub issues.
Oh, wow.
So real problems that developers have actually faced.
Real problems.
Yeah.
And they've selected these problems from 12 popular Python repositories.
So it's a good mix of different types of projects and challenges.
Exactly.
It's designed to be a really representative sample.
I'm starting to see how this is different from those simpler coding challenges.
Right.
It's about putting the AI in a situation that's much closer to what a human developer would experience.
So how does it actually work?
Like how do they test the AI with these problems?
So basically they give the AI a broken piece of software.
OK.
And they provide a description of the issue.
Like a bug report.
Exactly.
Like a bug report.
And then the AI has to actually edit the code to fix it, just like a human would.
Wow.
So it's not just generating code from scratch.
It's actually going in and modifying existing code.
Exactly.
And here's the really clever part.
OK.
I'm intrigued.
Once the AI has come up with its solution, they actually test it using the repository's own testing framework.
Oh, that's smart.
So it's not just about whether the code compiles.
It's about whether it actually solves the problem.
Exactly.
It has to pass all the tests just like a human developer's code would.
That's a pretty high bar.
It is.
It really puts the AI in the shoes of a real software developer.
So it's not just a theoretical exercise.
It's about seeing how AI can actually perform in a real world setting.
Exactly.
And as you can imagine, it's not a walk in the park.
I can imagine real world coding is full of challenges.
It is.
There are a lot of hurdles for these language models.
The sheer size of these code bases is one thing.
Just finding the right lines of code to change must be a nightmare.
It's like finding a needle in a haystack.
Sometimes you're talking about tens of thousands or even hundreds of thousands of lines of code.
And then on top of that, you have all these dependencies and interactions between different parts of the code.
Right.
Exactly.
It's what we call cross context code editing.
Cross context.
What does that mean?
Well, in a real world project, fixing a bug or adding a feature often means making changes across multiple files and functions.
Oh, I see.
It's not just about fixing a single line of code.
You have to understand how everything fits together.
Right.
The AI needs to grasp how all these pieces of code relate to each other.
And how a change in one place might affect something else somewhere else.
Exactly.
It needs to have that big picture understanding.
It sounds like that would be incredibly difficult for an AI to do.
It is.
It's one of the biggest challenges in this field.
So on top of that, they have to understand the problem itself.
Right.
They have to read these long issue descriptions.
And then sift through all that code.
Exactly.
It's a lot of information to process.
So how are these language models actually doing against these challenges?
Are they able to solve these complex real world problems?
Well, the results are pretty interesting.
They show just how difficult this really is.
Even the most advanced language models, like Cod 2,
they were only able to solve about 2% of the SESTA-BUE bench issues.
2%.
Yeah.
It's a pretty stark reminder of how far we still have to go before AI can truly replace human software developers.
Wow.
That's a humbling number.
It is.
But it also makes this research all the more important.
I see what you mean.
It shows us where the gaps are, what AI still struggles with.
Exactly.
And it highlights the areas where we need to focus our efforts if we want to make progress.
So this isn't just about building cool AI toys.
No.
This is about understanding the fundamental challenges of AI and software development.
Exactly.
And this is where things get even more interesting.
The researchers didn't just test existing AI models.
They also created their own specialized model.
Oh, really?
Yeah.
They called it SWE Llama.
SWE Llama.
Yeah.
And they specifically fine-tuned it for these real world coding tasks.
So they gave it some extra training, some specialized knowledge to help it tackle these challenges.
Exactly.
They wanted to see if they could improve AI's performance by focusing on this specific domain.
That makes sense.
So instead of trying to create an AI that knows everything about everything,
they focused on creating an AI that's really good at this one specific thing.
Exactly.
And SWE Llama is designed to handle even larger amounts of code than other models.
So it can tackle those massive code bases.
Yeah.
And in some cases, it actually outperforms those more general purpose models, like Clawed 2.
That's pretty impressive.
It is.
It's a promising sign that we're moving in the right direction.
So while AI isn't ready to take over the coding world just yet.
Not just yet.
It seems like this research is pushing us closer to that goal.
Definitely.
And one of the most valuable things about this research is the insights it provides.
Like what?
Well, for example, they found that the difficulty of the problems varied a lot,
depending on the specific coding style and structure of each repository.
Oh, that's interesting.
Yeah.
It suggests that some ways of writing code might be easier for AI to understand than others.
That makes sense.
Even for humans, some code is more readable and easier to work with than others.
Exactly.
So maybe we need to start thinking about how to write code in a way that's more AI-friendly.
That's a fascinating idea.
Yeah.
So it's not just about AI learning from humans.
It's also about humans learning from AI.
Exactly.
It's this kind of back and forth, this collaborative learning process.
That's really cool.
So what other insights did they find?
Well, another intriguing finding was that giving the AI even more code to analyze didn't always help.
Really?
I would have thought more information is always better.
You'd think so right.
But it turns out that sometimes too much information can actually hurt the AI's performance.
Oh, interesting.
It can make it harder for the AI to focus on the relevant parts.
It's like trying to find a specific sentence in a giant textbook without any page numbers.
That's a great analogy.
Sometimes less is more.
So it's not just about processing power.
It's about processing information intelligently.
Exactly.
And that's something that AI is still learning to do.
So what about the solutions themselves?
Did they find any differences between the solutions generated by AI and those written by humans?
Yeah, they did.
Actually, one of the most consistent observations was that the AI solutions tended to be much simpler and shorter than the human solutions.
So the AI was finding ways to fix the problems?
Yeah.
But maybe not in the most elegant or efficient way?
Precisely.
It seemed like the AI was more focused on finding the most direct solution.
OK.
Often overlooking opportunities to make the code more elegant, efficient, or maintainable.
So it's kind of like patching a hole in a roof.
Instead of redesigning it to be more weather resistant.
That's a great way to put it.
The AI was doing the bare minimum to fix the bug.
OK.
But not necessarily going the extra mile to make the code better overall.
So it sounds like AI still lacks that finesse that comes with human experience.
It does.
It seems to be missing that kind of holistic understanding that human developers have.
That ability to see the big picture and anticipate potential problems down the line.
Exactly.
It's about more than just writing code that works.
It's about writing code that's well designed and maintainable.
So this research is really highlighting the difference between just coding and true software craftsmanship.
It is.
And it's raising some really interesting questions about how we can teach AI to not only write correct code, but also to write code that's truly well designed.
This is all fascinating.
Yeah, that is.
But I'm wondering why someone who isn't a software developer should care about this research.
What's the bigger picture here?
That's a great question.
This research is really a glimpse into the future of AI and its potential to transform how software is built.
Imagine a world where AI can automatically fix bugs.
Wow.
Freeing up human developers to focus on more creative and strategic tasks.
That sounds like a dream come true for many developers.
It could be.
And it could lead to a huge increase in productivity and innovation.
So it's not just about making developers' lives easier.
No.
It's about unlocking a whole new level of potential in software development.
Exactly.
And it could have a ripple effect across all sorts of industries.
That's pretty exciting stuff.
It is.
But it also raises some important questions.
Like what?
Well, could AI one day become so advanced that it surpasses human developers?
That's a big question.
It is.
What would that mean for the role of humans in software development?
And for society as a whole.
Exactly.
These are questions that we need to start thinking about now.
I agree.
This is definitely something we'll be exploring further in the next part of our deep dive.
Looking forward to it.
Me too.
It's a it really makes you think you know about what sets human intelligence apart.
It does.
But getting back to SWE Lama for a second.
You mentioned it can work with, you know, larger chunks of code.
Yeah.
How does that actually work?
Like what makes it different from those other models?
One of the key things about SWE Lama is its ability to handle these massive code contexts.
OK.
Traditional language models.
They often struggle with this.
Yeah.
It's like trying to read an entire encyclopedia.
Right.
Just to find the answer to a single question.
Exactly.
SWE Lama though it uses some specific techniques.
OK.
That help it zero in on the most relevant parts of the code.
Yeah.
So it's not just about brute force processing power.
It's about being more selective, more intelligent about how it processes information.
Exactly.
And one of the techniques it uses is called attention.
Attention.
Yeah.
It's basically a way for the AI to prioritize different parts of the code.
OK.
Based on how relevant they are to the problem at hand.
So it's like having a built in highlighter.
Yeah.
That marks the most important passages.
That's a great way to think about it.
It helps the AI focus on what matters most.
That makes a lot of sense.
But even with these, you know, specialized models.
Yeah.
It sounds like AI still has a lot to learn from us from human developers.
It does.
You mentioned earlier that the AI solutions tend to be simpler and shorter.
Yeah.
What are the implications of that?
It's a really interesting observation.
It suggests that the AI is maybe a little too focused on finding the most direct solution.
OK.
And it often misses opportunities to make the code more elegant or efficient.
Or even just easier to maintain down the road.
Right.
Exactly.
Human developers, we often take a more holistic approach.
Yeah.
We think about the big picture.
We consider not just the immediate fix, but also, you know, the long term health of the code base.
So it's kind of like the difference between patching a hole in a roof.
OK.
Versus actually redesigning the roof to be more weather resistant.
That's a great analogy.
One is a quick fix.
The other is a more thoughtful and long term solution.
And it sounds like AI is still stuck in that quick fix mode.
Yeah.
For now, at least.
It hasn't quite grasped those higher level design principles.
So it's doing the bare minimum to solve the problem.
Right.
But not necessarily going the extra mile to make the code truly better.
And that's a key area for future research.
Yeah.
OK.
How do we teach AI to not just fix bugs, but also to write code that's well designed.
So that's easy to understand and modify in extent.
Exactly.
It's about going beyond the mechanics of coding.
And into the realm of software crashments.
Exactly.
It's about teaching AI to think like a human developer.
That brings us to another interesting point from the research.
OK.
The fact that some coding styles seem to be more AI friendly than others.
Right.
What are some of the things that make code easier for AI to work with?
Well, one of the biggest factors seems to be consistency.
Consistency.
Yeah.
Code that follows clear conventions that uses meaningful names for variables.
OK.
And is well organized.
That's generally easier for AI to parse and understand.
It's like having a well written book.
Yeah.
With clear chapters and headings.
Exactly.
It makes it easier for the AI to follow the flow of the code.
So good coding practices that make code easier for humans to understand.
Right.
They also benefit AI.
It seems that way.
And that has implications not just for how we train AI models.
OK.
But also for how we write code in general.
Interesting.
So if we want AI to be a valuable partner in software development.
Yeah.
We need to write code that it can understand.
Exactly.
It's almost like we need to develop a shared language.
A way of writing code that's both human readable and AI compatible.
That's a really cool idea.
It's like we're building a bridge between human intelligence
and artificial intelligence.
And as AI models become more sophisticated.
Yeah.
They might even be able to help us improve our own coding practices.
Oh, so.
Well, they could point out areas where our code could be more consistent
or easier to understand.
It's like having an AI code reviewer.
Exactly.
It's this really interesting feedback loop
where AI and humans are learning from each other.
That's awesome.
So turning to the specific findings of the SWLAMA research.
OK.
What were some of the standout results or surprises
that came out of those experiments?
One of the most interesting findings
was that even with relatively limited fine tuning.
OK.
SWLAMA was able to outperform those much larger, more general purpose
models in certain scenarios.
Oh, wow.
So bigger isn't always better.
Not necessarily.
It seems like specialization is key.
OK.
If you train AI models for specific types of coding tasks,
you can get significant improvements in performance.
So instead of trying to create like a one size fits all AI
coder, it might be better to have specialized AI experts.
Exactly.
Think of it like an AI team.
OK.
With a front end specialist, a back end guru, a database whiz.
I like that analogy.
It's like assembling the ultimate coding dream team.
Exactly.
And each member of that team is highly skilled in their particular
area.
Makes sense.
Were there any other insights from the SWLAMA research
that stood out to you?
Yeah.
Another really important finding was the importance of data
quality and relevance when fine tuning these AI models.
So garbage in, garbage out still applies in the world of AI?
Absolutely.
You need to train the AI on data that's
relevant to the tasks you want it to perform.
So if you want an AI that's good at writing Python code,
you need to train it on a lot of Python code.
Exactly.
And not just any Python code.
It needs to be a high quality code that
represents the best practices in that domain.
It's like feeding an athlete the right kind of fuel.
Exactly.
If you want peak performance, you need
to provide the right kind of input.
What were some of the challenges the researchers faced
in developing and testing SWLAMA?
One of the key takeaways was that even
with this specialized training, AI models
still struggle with certain aspects of real world coding.
OK.
For example, SWLAMA, it tended to generate
these simpler, shorter patches than human developers.
OK.
It often missed opportunities to refactor code
or improve the overall structure.
So it was fixing the bug, but not necessarily making
the code better overall.
Exactly.
It was kind of like applying a Band-Aid instead
of addressing the root cause of the problem.
Like a quick fix instead of a long term solution.
Exactly.
And this highlights the need for AI systems that can not only
write correct code.
Right.
But also understand the principles
of good software design.
Like readability, maintainability, and efficiency.
Exactly.
It's about more than just getting the code to work.
It's about writing code that's elegant and robust
and easy to understand.
Exactly.
And that's a big challenge for AI.
It seems like we're still in the early stages of this journey.
We are.
There's a lot of potential for AI to transform software
development.
Yeah.
But we still have a long way to go.
Now, before we wrap up.
OK.
I'd like to get your thoughts on the broader implications
of this research.
Sure.
What does it tell us about the future of AI and software
development?
Well, I think this research is a really exciting glimpse
into a future where AI is not just a tool,
but a collaborator in software development.
The collaborator.
Yeah.
Imagine AI systems that can automatically generate code,
identify bugs, even suggest improvements.
OK.
And all while working seamlessly alongside human developers.
So it's not about replacing human developers.
It's about augmenting their abilities.
Exactly.
It's about creating a partnership where AI and humans
can work together to create amazing things.
I like that.
That's a much more optimistic vision of the future.
I think it's a more realistic vision, too.
And as these AI models continue to evolve,
we can expect to see even more profound changes in how
software is developed.
Absolutely.
We might even see AI systems that can design entire software
architectures.
Or generate code in multiple programming languages.
That would be incredible.
Or even adapt to changing requirements on the fly.
It's mind blowing to think about the possibility.
It is.
But it also raises some important questions.
Like what?
Well, what will the role of human developers
be in this future?
Right.
How will we adapt to these changes?
And what ethical considerations will we need to address?
These are questions that we'll need to grapple with as AI
becomes more integrated into software development.
I agree.
It's not just a technological challenge.
No.
It's a societal one.
Exactly.
And it's one that we need to start thinking about now.
Well, this has been an incredibly insightful discussion.
Yeah.
Thank you for sharing your expertise with us today.
It's been my pleasure.
It's definitely a lot to think about.
It's exciting, but also a little bit daunting.
Yeah.
I think that's a fair way to put it.
We've talked about the impressive capabilities
of these AI models.
Right.
But also their limitations.
Yeah.
The areas where they still fall short of human expertise.
And it's those limitations, those gaps in understanding
that really point the way forward.
Exactly.
They show us where the most interesting research questions
are.
So where do we go from here?
What are the big unanswered questions
that researchers are grappling with?
Well, one of the biggest challenges
is teaching AI to understand code, not just as text,
but as part of a larger system.
So not just the syntax, but the semantics.
Right.
We need models that can reason about the intent behind the code,
the design decisions that shaped it,
the potential consequences of making changes.
It's like moving from understanding
the grammar of a language to actually grasping
its nuances, its idioms, its cultural context.
Exactly.
We need AI that can read between the lines of code,
so to speak.
Inferring the unspoken goals and constraints
that guide human developers.
Right.
It's a really tough problem.
It sounds like it.
What kind of research approaches are being explored
to tackle this challenge?
Well, one promising avenue is to incorporate more domain
specific knowledge into these models.
OK.
So instead of just training them on code,
we can also feed them information
about software design patterns, best practices, even
the history of how certain software systems evolved over time.
So it's about giving the AI a deeper understanding of the why
behind the code.
Exactly, not just the what.
I see.
Another area of active research is developing AI models
that can learn from fewer examples.
Right.
Right now, these models require massive data sets
for training.
Yeah.
Thousands or even millions of examples.
Exactly.
But imagine an AI that could learn to code
by observing a human developer.
Oh, wow.
Just like an apprentice learning from a master craftsman.
That would be incredible.
It would make AI more accessible and adaptable
to different coding styles and domains.
And it would open up all sorts of possibilities
for personalization.
Exactly.
Imagine an AI coding assistant that
learns your specific coding preferences.
OK.
And adapts its suggestions accordingly.
So it's like having an AI pair programmer that
knows your strengths and weaknesses.
Yeah.
And can anticipate your next move.
Exactly.
It's a really exciting vision of the future.
It is.
As we look ahead, what are some of the broader implications
of this research for the field of AI as a whole?
Well, I think this work is really pushing
the boundaries of what AI can do.
OK.
It's demonstrating its potential to tackle
these complex real world problem.
Problems that were once thought to be beyond the reach
of machines.
Exactly.
And it's highlighting the importance of moving
beyond those simple benchmarks.
OK.
We need to start evaluating AI systems
in more realistic and challenging environments.
Environments that better reflect the messy and unpredictable
nature of the real world.
Exactly.
The true test of AI's intelligence
lies not in its ability to solve puzzles in isolation,
but in its ability to navigate the complexities of human
endeavor.
That's a profound thought.
Yeah.
And that's where things get really interesting, right?
Yeah.
As these AI systems become more capable,
they're going to force us to confront
some fundamental questions.
About the nature of intelligence, creativity,
even consciousness.
Exactly.
It's a journey that's sure to be filled with surprises,
challenges, and profound insights.
I agree.
Well, this has been a truly fascinating exploration.
Thank you so much for joining me on this deep dive.
It's been my pleasure.
And to our listeners, thank you for tuning in.
The world of AI and software development
is constantly evolving.
So stay curious, keep exploring, and who knows?
Maybe you'll be the one to write the next chapter
in this incredible story.