How AI Transforms Conventional Software Development

Here’s how AI is changing the conventional software development lifecycle — with a special focus on pull requests (PRs).

The first impact of AI shows up long before a PR is opened.

1. In‑editor assistants like GitHub Copilot and similar AI coding tools proactively flag bugs, missing tests, and style inconsistencies as developers type — so code arrives at the review stage cleaner and more complete.
2. Studies show developers with AI assistants complete coding tasks up to ~55% faster than without them, indicating a real productivity uplift even before review begins.
3. By catching mundane problems early, teams are naturally pushed toward smaller, more frequent commits and PRs, because the cost of keeping code tidy is lowered by automation.

This shift influences developer habits and releases a lot of mental overhead, allowing engineers to spend less time fixing trivial errors and more time on meaningful problems.

Once a pull request is opened, AI tools step in as an automated reviewer:

1. AI‑powered review agents analyze every diff, highlighting potential bugs, security vulnerabilities, and style violations instantly. They can even auto‑summarize changes or propose refactors.
2. These systems integrate with platforms like GitHub, GitLab, or Bitbucket so feedback appears where developers already work — keeping flow smooth and context intact.
3. According to tools vendors and case studies, AI review can cut review cycle times dramatically — sometimes up to three times faster, with near‑instant feedback on routine issues.

That doesn’t mean AI replaces humans. Instead, the types of conversations shift.

With bots handling the low‑hanging fruit:

1. Human reviewers no longer waste time saying “fix formatting” or “add missing semicolons.” Instead, they focus on architecture, API design, and alignment with product intent — questions machines struggle to reason about.
2. AI handles boilerplate housekeeping while humans handle meaningful judgment calls — like whether a change fits the domain model, adheres to team principles, or anticipates future scale.

This collaboration redefines the purpose of code review: from policing syntax to evaluating intent and value.

AI’s influence on metrics is nuanced — some intuitive, others surprising.

One noteworthy trend is that average PR size tends to grow when teams adopt AI tools.

1. Teams report increased PR sizes because automated refactors, documentation updates, and test scaffolding become cheap to generate. Your “While I’m here…” commit turns into a bigger PR.
2. Some studies even show double‑digit increases in median PR lines changed when AI tools are adopted.
3. That challenges the classic rule of always make small PRs, making PR size a richer metric that should be tracked alongside context.

With AI in the loop:

1. Review cycle time usually drops when AI reviews are enabled, even if PRs are larger. Automation finds surface issues instantly, and humans can engage faster on deeper concerns.
2. But without AI, larger AI‑generated PRs alone can lengthen review times and lead to rubber‑stamp approvals — undermining quality.

So comparing raw “time to merge” figures without considering AI context can be misleading; it’s better to normalize that time by lines of code or number of files changed.

AI adoption often correlates with:

1. Higher throughput (more PRs merged per developer per week) and more lines changed per PR.
2. Better detection of bugs and vulnerabilities early — though not perfect; real‑world tests show some AI‑generated code still contains security flaws, so human vigilance remains critical.

Metrics like “PRs per week” therefore lose meaning on their own. Pair them with risk measures like defect rates and severity levels for true insight.

AI isn’t magical; it has blind spots.

1. AI often looks plausible but can generate code with bugs, security issues, or incorrect logic if requirements are unclear or incomplete.
2. Some empirical research even suggests that certain AI tools can slow down experienced developers who end up spending extra time verifying and correcting suggestions.
3. And larger AI‑generated PRs can still overwhelm human reviewers if quality guardrails aren’t enforced.

So it’s not AI vs. human — it’s AI plus humans working together, with clear standards and guardrails.

To navigate this transformation:

1. Track PR size distributions (median, 75th/90th percentiles) alongside AI usage
2. Distinguish “with AI review” vs. “without AI review” time to merge
3. Maintain soft size limits (e.g., 200–400 LOC) unless strong tests and observability justify exceptions

This way, you preserve classic engineering discipline while leveraging the strengths of AI.

At its core, AI augments the software development lifecycle by automating routine tasks and freeing up human creativity. It reshapes PR workflows, improves early quality detection, accelerates review cycles, and nudges teams toward better habits -- but only when you measure and govern its output intelligently.

As adoption grows, teams that embrace AI not just as a tool but as a collaborator -- backed by strong guardrails and human judgment -- will see the most productivity and quality gains in the years ahead.