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<h3>AI's Impact on the Software Development Lifecycle</h3>
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<span class="in-the-news-label">Our research was recently featured in:</span>
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<li><span class="in-the-news-source">[New York Times]</span> <a href="https://www.nytimes.com/2026/02/20/technology/ai-coding-software-jobs.html?unlocked_article_code=1.NlA.xjdj.dljateB_vLA-&smid=url-share" target="_blank" rel="noopener noreferrer">A.I. Isn't Coming for Every White-Collar Job. At Least Not Yet.</a></li>
<li><span class="in-the-news-source">[CMU SCS News Story]</span> <a href="https://www.cs.cmu.edu/news/2026/hidden-cost-ai-speed" target="_blank" rel="noopener noreferrer">The Hidden Cost of AI Speed</a></li>
<li><span class="in-the-news-source">[New York Times]</span> <a href="https://www.nytimes.com/2026/03/12/technology/axiom-ai-code-funding.html?unlocked_article_code=1.S1A.QE_W.2aXSRM9XZiEh&smid=url-share" target="_blank" rel="noopener noreferrer">A.I. Writes Buggy Code. A Silicon Valley Start-Up Wants to Fix It</a></li>
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<!-- Papers Section -->
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<!-- Paper 1 -->
<div class="paper-item">
<h2 class="paper-title">AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development</h2>
<div class="authors">
<span class="author">Shyam Agarwal<span class="affiliation-num">1</span></span>,
<span class="author">Hao He<span class="affiliation-num">1</span></span>,
<span class="author">Bogdan Vasilescu<span class="affiliation-num">1</span></span>
</div>
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Paper
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Code & Data
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<h2 class="paper-title">Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects</h2>
<div class="authors">
<span class="author">Hao He<span class="affiliation-num">1</span></span>,
<span class="author">Courtney Miller<span class="affiliation-num">1</span></span>,
<span class="author">Shyam Agarwal<span class="affiliation-num">1</span></span>,
<span class="author">Christian Kästner<span class="affiliation-num">1</span></span>,
<span class="author">Bogdan Vasilescu<span class="affiliation-num">1</span></span>
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Code & Data
</a>
</div>
</div>
<!-- Shared Affiliation -->
<div class="affiliations">
<span class="affiliation-num">1</span>Carnegie Mellon University
</div>
</section>
<div class="ai-impact-video">
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</div>
<!-- Overview -->
<section class="overview">
<h3>Overview of Research</h3>
<p>
AI coding assistants and autonomous coding agents are widely promoted as major accelerators of software development.
Developer testimonials and product marketing often report large productivity gains, but rigorous longitudinal evidence
has been limited. In two recent empirical studies, we examine these claims using large-scale GitHub data and modern
causal inference methods.
</p>
<p>
Both papers use the same core methodology: staggered difference-in-differences designs with carefully matched control
repositories, combined with repository-level quality metrics from static analysis. One study focuses on adoption of an
AI-native IDE assistant, Cursor, while the other examines autonomous coding agents that generate pull requests and
contribute code at the repository level.
</p>
<p>
Across both settings, we find strong and statistically significant short-term increases in development velocity after
adoption, measured through commits and lines of code added. In the Cursor study, velocity spikes are large but transient,
with lines added increasing several-fold in the first month and then tapering off.
</p>
<div class="figure-wrapper">
<img src="/assets/fig/ai-sdlc-impact-results.png" alt="Research findings showing the impact of AI coding tools on software development metrics">
<p class="figure-caption">
Impact of AI coding tools on software development metrics over time. The top row shows Cursor AI adoption, while the bottom row shows broader autonomous agent adoption.
Blue lines represent projects without prior AI tool usage, orange lines show projects that previously used AI IDEs.
</p>
</div>
<p>
What is less visible from anecdotal reports, but consistent across both papers, is the quality side of the story. In both
the IDE assistant and agent settings, adoption is followed by persistent increases in static analysis warnings and code
complexity, two widely used proxies for technical debt and maintainability risk. These increases do not revert during the
observation window.
</p>
<p>
The agent study further shows heterogeneous effects depending on prior AI exposure. Repositories that adopt agents as their
first AI tool see sizable velocity gains, while those already using AI IDE tools experience much smaller or short-lived
throughput improvements. Quality degradation signals, however, appear in both groups, with static analysis warnings and
cognitive complexity rising on the order of roughly twenty to forty percent on average.
</p>
<p>
Together, the two studies point to a consistent pattern: AI coding tools accelerate output in the short run, but without
stronger quality controls and review practices, they are associated with sustained increases in code complexity and technical
debt, which in turn are linked to later slowdowns in velocity.
</p>
</section>
</div>
</div>
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<p>We study complex socio-technical systems such as GitHub and Stack Overflow using data science methods.</p>
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<h4>Social Links</h4>
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Software and Societal Systems Department<br/>
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