What is Source Code Management (SCM): Everything Engineering Leaders Need to Know

In an era of agentic coding, simply using an SCM tool, like Git, isn't enough for good source code management.

If you're like other engineering leaders, you're probably noticing this already on your team. Source code gets diluted and degraded as coding velocity increases. Your team moves faster with coding, but other parts of software development (e.g., code review, PR cycles) slow down or you get stuck shipping code you don't really trust.

These aren't really AI coding issues. They're SCM issues.

In this article, we'll walk you through how source code management works, why source control matters, and how to do it well and future-proof your team for an agentic era.

What is source code management? SCM meaning

Source Code Management (SCM) is the process of tracking, controlling, and organizing changes to a source code repository and other project files. The goal is to:

• track and document a history of changes to the source code
• keep the project or codebase organized as it grows
• manage a growing team of developers or contributors working on a single codebase
• control the changes that are made to the codebase to maintain quality

Source code management (sometimes called source control) usually encompasses a variety of processes or tasks, including:

• Version control, i.e., tracking and controlling changes over time. Version control tracks who makes changes, what changes are made, when, and why, and controls changes by maintaining source code repositories separate from new changes ("branches").
• Branching and merging, i.e. allowing developers to work on code in isolation before merging new changes into the source code. This prevents errors and bugs in the source code (maintains quality) and improves collaboration by ensuring that developers aren't writing over one another or working on conflicting code at the same time.
• Conflict resolution, i.e. combining all new changes without overwriting one another. This allows devs to work on changes independently while maintaining the functionality of the source code and prevents overwriting.
• Source code quality, i.e. maintaining a clean and functional source code and preventing errors and bugs while developing new code. SCM can also include code reviews and automated testing which helps improve code quality.

Source code management vs. version control

Version control is sometimes used interchangeably with source code management, but technically, version control is a subset, or narrower workflow, within SCM.

Source code management is the entire process and workflow of managing, tracking, and organizing source code. Version control is the process (and usually, a specific tool) of tracking and managing changes to source code over time across your entire team or organization.

That is: version control is always contained within SCM, but SCM can contain more than just version control.

In general, SCM tools and version control tools are the same and cover the same features and functionality.

The history of source code management

Today, Git (source of GitHub, GitLab, and others) is the face of version control and SCM, but, of course, this wasn't always the case.

Up until the early 2000s, centralized systems allowed developers to do basic source control, like basic change tracking or branch merging. But these workflows still relied on centralized systems that were clunky to merge into, difficult to collaborate with, and created large, single points of failure.

In the early 2000s, Linus Torvalds created Git as a distributed version control system for Linux. His goals in building Git were to:

• Avoid the limitations and downsides of the then-standard CVS systems.
• Massively improve speed and scale (i.e. at the time, 30-second patch load times were common).
• Avoid centralized reliance and repositories.
• Continue and improve the process for distributed, non-linear workflows and make branching and merging seamless.

20+ years down the road, Git is now the foundation for nearly 95% of version control systems, including the foundation for GitHub, GitLab, Bitbucket, Azure DevOps, and others. However, there are still other source code management systems that don't use Git.

How does source code management work

Today, source code management generally works by using the following process:

1. A modern SCM tool holds the core repository, or source code, containing all the source code, project files, tracked changes, assets, and so on.
2. Any developer who wants to make changes to the code creates a branch. This creates a separate copy of the source code locally, where you can work and make changes without editing the main source code.
3. As you write code, occasional commits save your work. If you make a major error, you can revert to the last functional commit rather than having to start over.
4. When you're ready to save your changes, you'll push your commit to the cloud and file a pull request or merge request to request that your changes be pulled into the main source code. Other team members will review, comment on, and request any changes to your work before approving the request.
5. When a pull request is approved, a branch is merged back into the source code. If multiple branches made conflicting changes to the same file, the SCM system will flag a merge conflict, which alerts your team to look at both branches and select the changes that make the most sense. This prevents accidental overwriting and errors due to simultaneous work and submissions.
6. Version control saves previous versions of your source code, so that in the event of a major bug or issue, your team can rollback to a previous working version quickly before fixing the new code.

One element of SCM that's often underconsidered is code review. While branching and merging creates gates that keep your source code separate, code review is the gatekeeper that maintains your source code quality.

That is: if you're branching and merging so that devs can work independently, but then merging code that is full of bugs, you're still going to be degrading your source code in the end. Good source code management protects your codebase, but you also need good code review before you merge new code back in.

Traditional SCM code review vs. AI code review

Traditional SCM code review processes are highly manual and require a lot of developer time and effort. Though crucial, if you're using AI to code, code review quickly becomes the bottleneck. Consider, for example, the team of 6,000+ engineers at Atlassian: they now spend just 15% of their time coding, and the rest on bottlenecks like validation and alignment. [1]

Traditional code review generally follows the following process:

1. A developer branches and writes code, then pushes the commit to the cloud.
2. Automated checks like linters or static analysis tools run quality checks and fix or identify bugs.
3. Once the quality test is passed, the developer opens and writes a pull request (PR).
4. Other developers review code line-by-line, asking questions, leaving comments, requesting changes, and running tests to review the code.
5. Code author reviews and addresses all feedback, updates the logs, and pushes final commit.
6. PR is approved and merged into source code.

Having a PR template and a code review checklist can help standardize this process and ensure all code is reviewed at the same quality level. But when you start using AI to write code, code review becomes the bottleneck quickly.

An AI code review tool like Greptile standardizes code review for SCM and layers in quality features that keep your source code clean, no matter who (or what) wrote the code, and without requiring more developer resources.

Benefits of source code management

Source code management offers many benefits, such as:

• A secure ecosystem. A separate, maintained source code repository and branching/merging systems protect your codebase while devs work. Critical bugs can get found and fixed before they enter your source code.
• Version control creates backups and rollback options. Version control systems track and save every change, modification, and merge to your source code, allowing you to quickly rollback to a previous, working version if something major goes wrong. Plus, you can easily see who made what changes when, making it faster and easier to find and isolate bugs.
• Automated guardrails and code review. Good SCM automatically triggers code review, like security testing and scanning, linters, or AI code review tools like Greptile to review all PRs before code is merged. This creates standardized quality (i.e., if the code doesn't pass a security scan, it's blocked from merge) without increasing work for human devs.
• Accountability and audit trail for regulatory compliance. Having a record of every change that was made to your codebase, why and when, and rollback versions creates a clean record both for internal use, but also for external audits, regulatory compliance, and more.

Top source code management tools

Most SCM tools are Git-based, but it's a good idea to have an idea of some of the differences between the most popular options:

• GitHub: The cloud-based industry standard, with the biggest plugin and Actions ecosystem
• GitLab: The all-in-one DevSecOps platform for enterprise teams in regulated industries
• Bitbucket: Git-based SCM that is deeply embedded in the Atlassian ecosystem
• Perforce: Built for massive repositories, binary assets, and petabytes of data
• Mercurial: A distributed version control system that is not Git-based

GitHub

GitHub is a cloud-based system that uses Git to store repositories, manage version control and branching, track and organize pull requests, and perform other SCM tasks. It has a huge component marketplace, GitHub Actions (testing automations), and third-party plugins for all aspects of the SCM and CI/CD workflow. It offers a strong developer experience and is widely considered the "industry standard," particularly for open-source projects. However, it also offers an Enterprise option, GitHub Enterprise.

If you don't know where to start, start here.

GitLab

GitLab is the all-in-one DevSecOps platform that runs on Git. Don't confuse GitHub and GitLab: they both run on Git (like many other platforms), but they're two totally separate platforms owned by separate companies.

GitLab, rather than being modular and ecosystem-driven like GitHub, is an all-in-one platform with built-in CI/CD, security, project management, and other features. It's generally used by enterprise teams in highly regulated industries.

Bitbucket

Bitbucket is a Git-based SCM tool owned by Atlassian. Bitbucket's core differentiation is that it is deeply embedded in the Atlassian ecosystem. If your engineering team is already using Jira and/or Confluence, for example, Bitbucket is going to be the most seamless integration.

Otherwise, Bitbucket is mostly focused on enterprise, private codebases, and offers many of the same core features as GitLab or GitHub Enterprise. Bonus: Greptile now also supports Bitbucket (alongside GitHub, GitLab, and others).

Perforce

Perforce is a version control and source code management system preferred by teams who are managing massive repositories that other tools struggle to manage. For example, Perforce has no trouble working with massive files, massive binary assets, and petabytes of data. It's also designed for strong enterprise use and controls, giving large enterprise teams with huge assets the tools they need to manage source control effectively.

For example, Greptile supports NVIDIA using Perforce to manage massive repositories while still being more productive in shipping code they can trust:

Mercurial

Mercurial is a distributed version control system (DVCS), like Git, that is nevertheless, not Git-based. Mercurial is a more traditional VCS that prioritizes structural consistency, sanctity of history, and heavyweight branching. It's mostly favored by enterprise teams working on extremely large-scale monorepos and for legacy projects.

Best practices for source control

So, how do you manage source control well? Start with these best practices for engineering teams:

• Above all else, protect your main branches. Always, always, always, follow branching rules and work on code in a new branch, away from your source code.

• Commit often. Saving a commit is like making an auto-save or backup of your current changes while you work. Committing often lets you rollback to the previous commit if you make a mistake without having to redo significant portions of your work. Also: try to commit at meaningful breakpoints. Even if the entire feature isn't implemented, only commit clean, working code.

• Don't leave junk commits. Generic commit titles like "fixed bug" or "updated" don't help anyone. Assume that your documentation is crucial to the source code quality (it is!) and leave a meaningful comment about what step in the process this commit was or what change was made.

  As Troy Hunt says, good commit messages should:

  • Help leave a paper trail of how and why this code changed over time (for you and your team)
  • Help track down errors in the future
  • Never be identical to a previous commit message (or else how will you quickly tell the difference between the two commits?)
  • And most importantly, explain why you committed the code.

• Similarly, write meaningful PRs and comments. When you're documenting changes, creating a PR, or leaving messages or comments inline, be thorough. Explain exactly what you did, why, what the goal was, and what was changed.

  As Linus Torvalds himself says,

  "If you cannot explain a merge, then JUST DON'T DO IT.

  It's really that simple. There is absolutely *NEVER* an excuse for merges without explaining why those merges exist."

• Keep PRs small. A good rule of thumb is to keep each PR to 200-400 lines of code or less. This makes it easier to review thoroughly and keep your development process moving.

• Review code and PRs thoroughly. Implement a code review checklist to standardize review quality across your organization. This helps make sure that code is reviewed thoroughly and removes some of the mental load from devs (by telling them exactly what to check and why).

• Automate linting and other first round reviews. Human developers should almost never spend time reviewing code for syntax, style, code smells, and other basic errors and bugs that a machine can easily find (and fix!). Implement a linter and other static code analysis tools to run the first round of review for you.

  Our personal rule here at Greptile: Mechanical code review layers should never require human review time. If you're checking these manually, your tooling has failed.

• Run sandbox testing. There are so many errors and bugs that simply can't be caught by reading the diff. Make sure to run code before pushing to prod to catch runtime errors. If you don't have the human resources to do this, use a tool like Greptile. Greptile uses TREX to spin up a sandbox, run code, and report back with screenshots, logs, scripts and so on that will give you the results without the developer time of writing and running the test.

The future of source control management

Good source code management simply can't happen without good code review. Without code review, you're letting unchecked, and potentially risky or error-laden, code enter your source code. And in the agentic era, when agents are doing more and more of the actual coding, more code than ever is getting shipped: and much of it isn't trustworthy. This is degrading source code, even with good management practices.

Consider the data:

• Liu et al. tracked AI-introduced security issues across over 6,000 real production repos from early 2025 through February 2026. Overall, out of 464,900 tracked AI-introduced issues, 105,364 still survive in the version of the codebase in February 2026, corresponding to a survival rate of 22.7% [2].
• A Georgia Tech study tracked AI-introduced security vulnerabilities across tens of thousands of security advisories. In the second half of 2025, they found 18 cases; on average, roughly, three per month. By March 2026, they found 35 in a single month [3].
• When measured in a controlled benchmark setting against 473 human-written repos as a baseline, 77% of AI-generated code starts at lower quality than human-written repos and gets worse the longer agents work on it. [4]

These aren't really coding issues: they're SCM and code review issues. Bad code should never get merged in the first place. But the problem is that agentic coding happens at a velocity that can make SCM next to impossible to manage on a human scale. OpenClaw provides an excellent case study:

In December 2025, OpenClaw was getting two pull requests a week. By February 2026, that number jumped to 3,400/week. Before the spike, ~48% of PRs got merged; after, fewer than 9.3% of PRs got merged. [5]

Many of those PRs were low-effort slop, with some contributors submitting hundreds of PRs a day. Now, your team might not be using coding agents in this way, but as AI increases the output of code for all software engineers, the ability to also scale validation becomes increasingly important.

And only engineering leaders can establish the processes, tooling, and incentives team-wide that make trusted velocity repeatable across your organization. In an agentic coding era, SCM has to mean more than just branching and human-reviewed PRs.

As your team leans into the velocity of agentic coding, you also have to scale velocity on the code review side to manage source code well. That's why we've built Greptile with SCM support for an agentic world, like:

• Custom context and an AI code reviewer that learns your codebase, which means your reviews and feedback from Greptile get more personalized over time. Unlike other code review tools that just look at the diff, Greptile indexes a full map of your codebase from day 1, and learns from your team over time to provide better reviews.
• Security review built-in. AI code tends to introduce more vulnerabilities, and sneakier ones, than human-written code. That's why Greptile pairs static scanning with an AI security agent to catch vulnerabilities in every pull request.
• Runtime validation via TREX, that actually runs and tests your code like a senior dev would. TREX runs your PR branch in a sandbox to find the bugs that only show up at runtime. It starts services, mocks inputs, clicks through UI flows, and leaves the logs, screenshots, and traces on the PR.
• Agentic code review at scale with greplooping. Greptile's "greploop" skill allows your agents to work on code review autonomously with the Greptile agent, reviewing, iterating, and fixing PRs until they reach a confidence score of 5/5.

Source code management for an agentic era requires scalable code review you can trust. Greptile is here to help your team move faster, catch more bugs, and ship code you feel confident in. Try Greptile free and see how it can help your team →

Footnotes:

[1] Taroon Mandhana, Chris Kasten, and Ryan Sokol. "How Intuit, DoorDash, and Atlassian are adopting AI coding." Cursor Compile '26, June 2026. youtu.be/aF-rolD9W7I?t=494

[2] Liu, Y., Widyasari, R., Zhao, Y., Irsan, I.C., Chen, J., and Lo, D. "Debt Behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wild." arXiv:2603.28592, April 2026. arxiv.org/abs/2603.28592

[3] Georgia Tech Vibe Security Radar. research.gatech.edu/bad-vibes-ai-generated-code-vulnerable-researchers-warn Interactive tool: vibe-radar-ten.vercel.app

[4] Orlanski, G., Roy, D., Yun, A., Shin, C., Gu, A., Ge, A., Adila, D., Roberts, N., Sala, F., and Albarghouthi, A. "SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks." arXiv:2603.24755, May 2026. arxiv.org/abs/2603.24755

[5] Greptile "A statistical study of PRs opened on openclaw/openclaw". 2026. greptile.com/blog/prs-on-openclaw