How AI is Reshaping Software Development: Insights from the 2024 Dora Report

I’ve been working in tech long enough to see all kinds of "revolutions" come and go, each promising to change everything.

But AI? This one feels different.

The way it’s woven itself into every corner of software development is wild. AI is reshaping everything from task automation to decision-making. And, yes, it's making things faster and, in many cases, smarter.

But here’s the thing: the transformation isn't as seamless as some might think. Or, so says the Dora Report.

The 2023 and 2024 Dora reports reveal that AI’s role in development pipelines is anything but straightforward. Check out 5 Free AI Coding Copilots for Developers to Be More Efficient

It’s brought impressive gains in productivity and innovation, sure, but it's also introduced new layers of complexity. Stability, reliability, and even the day-to-day developer experience—these are all impacted in ways we’re still figuring out.

So, let’s take a closer look at the real story of AI in software engineering, exploring both its transformative wins and the tricky hurdles it brings along.

Also read: The 2024 Dora Report: State of DevOps Breakdown Summary

1. AI’s Surge in Software Engineering: A New Wave of Automation

The last few years have seen AI tools evolve from basic code autocompletion to powerful automated code review and bug detection systems. The 2023 Dora report, drawing insights from over 33,000 professionals, revealed that over 60% of DevOps teams now integrate AI tools directly into their workflows, with a significant boost in speed and productivity as a result.

When AI catches errors and optimizes code on the fly, developers can focus on higher-level problem-solving instead of getting bogged down in repetitive debugging.

2. AI-Generated Code Quality: A Mixed Bag

With tools like GitHub Copilot, OpenAI Codex, and free options like Tabnine making waves, we’ve seen that while AI can speed things up, it doesn’t always produce flawless code.

Sure, AI-generated code snippets can fill in the gaps, but studies show mixed results on how reliable they are.

The 2023 Dora report hinted at a key tension: while AI-driven code review can boost productivity, it can also introduce subtle bugs and inefficiencies if developers rely too heavily on it.

For example, a GitHub Copilot study found that AI suggestions helped developers complete tasks nearly 55% faster but also increased code review time because of a higher volume of minor errors. AI is great for rapid prototyping and iterating, but it still needs a watchful human eye to ensure long-term quality.

3. Building Trust in AI Tools: A Hurdle to Overcome

AI tools are only as good as the data they’ve been trained on, and this fact introduces some real concerns around trust.

Engineers worry about AI “hallucinations” (when AI suggests solutions that seem plausible but are actually wrong), a phenomenon that adds a layer of risk to using AI for mission-critical code.

Developers are understandably cautious. The 2024 Dora Report found that about 39% of DevOps professionals worry about relying too heavily on AI tools because they don’t fully trust the code quality.

Plus, if the AI model doesn’t have access to up-to-date, high-quality datasets, it can suggest outdated or insecure practices. For AI to be truly integrated, teams need to feel they can trust its output without second-guessing every suggestion.

Example: AI in Bug Detection—Fast But Flawed?

Take bug detection, one of AI’s big promises in DevOps. Tools like DeepCode and Snyk use machine learning to flag vulnerabilities in real-time.

According to the Dora report, teams using these tools detected and fixed bugs up to 40% faster, which sounds amazing—until you realize they can also throw up a ton of false positives.

Developers then have to sift through these to find real issues, which can slow down the workflow. Building trust here means ensuring AI has enough context to distinguish between real threats and minor issues that don’t require urgent fixes.

What does the 2024 Dora Report have to say about AI in Software Development?

The 2024 Dora Report has some eye-opening findings to share about AI’s role in the coding world.

Turns out, AI isn’t just an add-on anymore; it’s becoming part of developers' everyday routines.

A massive 75.9% of developers are using AI in some way, with tasks like writing and summarizing code leading the way.

About 74.9% lean on AI to help generate code, and another 71.2% use it to sum up or simplify the code they’re working with. Tools like chatbots, embedded plugins in IDEs, and online interfaces are how most developers access AI, with nearly 80% of them using chatbots specifically.

But here’s where it gets interesting: even though AI is definitely speeding up the process and helping folks produce cleaner, more organized code (67% said AI made their code simpler), it’s not all sunshine and roses.

When it comes to trusting AI-generated code, developers have mixed feelings. While most (about 88%) show some level of trust in the quality of AI-produced code, they’re often hesitant about just how reliable it actually is. Nearly 40% expressed either low trust (27%) or no trust at all (12%) in AI-generated code.

One developer perfectly captured the challenges by comparing AI code reviews to “the early days of StackOverflow, [when] you always thought people on StackOverflow are really experienced, you know, that they will know exactly what to do. And then, you just copy and paste the stuff, and things explode.”

In fact, Dora found that AI might actually be slowing down delivery performance a bit. Every 25% increase in AI adoption showed a 1.5% dip in delivery speed and a bigger 7.2% drop in system stability. This contradiction is what’s really worth watching—AI is giving developers sharper, more polished code, but it’s not leading to the big productivity gains everyone assumed.

While AI can churn out code faster than ever, it often needs a bit of a “human touch” before it’s ready to go. Developers find themselves double-checking, adjusting, and sometimes overhauling AI-generated code.

In other words, while AI can streamline some coding tasks, it’s not always the shortcut we think it is. Developers still need to find the right balance between using AI for efficiency and making sure the quality of code stays solid over time.

Also read: Open Source AI: What’s the hype around it? Everything you need to know about it

The Future of AI in DevOps: AI as a Partner, Not a Replacement

The 2023 and 2024 Dora insights make it clear: AI isn’t here to replace developers but to empower them. For instance, AI can take on repetitive tasks like formatting code, catching basic errors, or suggesting optimizations, allowing developers to focus on complex problem-solving and innovation.

Going forward, it seems likely that AI will become a standard “partner” for developers, helping them code faster, avoid mistakes, and keep up with the pace of modern software development. But AI’s role in DevOps hinges on trust and quality control—developers need to feel confident that these tools are making their jobs easier, not more error-prone.

Wrapping Up: Embracing AI with Caution and Curiosity

AI is reshaping software development in ways that were hard to imagine a few years ago. We’re seeing huge efficiency gains, but the reality is that AI still has limitations. Developers must approach AI tools with both excitement and caution, knowing that while AI can accelerate workflows, it isn’t perfect. Building a trusted AI workflow is about blending AI’s speed and data processing power with human judgment and oversight. For developers and DevOps teams, it’s an exciting new frontier—but one that needs to be navigated thoughtfully.

AI may be good or bad; only time will tell. But the show must go on, meaning, your software updates should be shipped on time and the software delivery performance should be monitored for better perspective and quick results. For this, look nowhere else, because Middleware is by your side to help you get deep actionable insight into your software delivery process.

FAQs

1. How is AI improving software development productivity?

AI tools, like generative AI and machine learning models, automate routine coding tasks, streamline debugging, and enhance code reviews. This boosts productivity by letting developers focus on complex issues rather than repetitive tasks, making the development process faster and more efficient.

2. What role does AI play in quality assurance and testing?

AI is enhancing testing by identifying bugs and vulnerabilities faster and with greater accuracy. Generative AI assists in developing test cases and automating workflows, leading to improved code reliability and quicker error detection.

3.Is AI replacing software developers

AI complements rather than replaces developers. While AI handles repetitive tasks and supports decision-making, complex problem-solving, creative coding, and decision-making still require human expertise.

4.What are the challenges of using AI in software development?

Developers face challenges with trust in AI outputs, handling data security, and ensuring that AI models align with their coding standards. Many teams also need time to adopt and integrate these tools into their workflows effectively.

5.What does the future look like for AI in software development?

As AI tools continue to evolve, we can expect deeper integration into the development lifecycle, including more sophisticated automation, predictive analytics, and AI-driven insights to enhance every stage of software development.