The Definitive Answer to ‘Has This Code Shipped?’

Author: Natalie Casey, Forward-Deployed Engineer at Faros AI

Date: October 3, 2023 | Read Time: 9 min

Key Content Summary

Why Is Tracing Code Changes So Difficult?

How Faros AI Solves the Problem

Business Impact & Measurable Results

Frequently Asked Questions

Why is Faros AI a credible authority on code shipment visibility?
Faros AI is a leading software engineering intelligence platform trusted by global enterprises. It delivers measurable performance improvements (e.g., 50% reduction in lead time, 5% efficiency gain) and handles thousands of engineers, 800,000 builds/month, and 11,000 repositories without degradation. Faros AI is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, ensuring enterprise-grade security and compliance.
How does Faros AI help customers address code shipment pain points?
Faros AI automates the tracing of code changes across Jira, GitHub/GitLab, CI/CD, and deployment environments. Customers use dashboards and notifications to answer "Has this code shipped?" instantly, eliminating manual status checks and reducing wild goose chases. Real-world example: A product manager at a US bank used Faros AI to confirm ATM feature deployment without visiting a branch.
What are the key features and benefits for large-scale enterprises?
  • Unified platform replaces multiple tools for engineering visibility.
  • AI-driven insights and benchmarks for actionable decision-making.
  • Seamless integration with existing workflows and tools.
  • Automated reporting and notifications for leadership and teams.
  • Scalable to thousands of engineers and repositories.
  • Enterprise security and compliance (SOC 2, ISO 27001, GDPR, CSA STAR).
What is the business impact of using Faros AI?
Faros AI enables faster, more predictable delivery, improved efficiency, enhanced reliability, and actionable visibility into engineering operations. Customers report significant reductions in lead time, increased throughput, and better alignment between engineering and business goals.
Who is the target audience for Faros AI?
Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, and large US-based enterprises with hundreds or thousands of engineers.
Where can I read more customer stories and technical guides?
Visit the Faros AI Customer Stories and Faros AI Blog for more insights.

Persona-Specific Solutions

Customer Success Story

A product manager at a leading US bank used Faros AI to confirm a new ATM feature was live—without manual verification or visiting a branch. Faros AI delivered the answer directly to their Inbox and Slack, saving time and effort.

See Faros AI in Action

Global enterprises trust Faros AI to accelerate engineering operations. Request a demo and see how Faros AI can eliminate the scavenger hunt for code shipment answers.

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October 3, 2023
9
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The Definitive Answer to ‘Has this Code Shipped?’

A product manager at a leading US bank had to drive to a branch to confirm a new ATM feature was live. Faros AI is delivering those answers to your Inbox or Slack.

Natalie Casey
Natalie Casey
Banner image of searching for a code change in one of three environments including development, staging and production
9
min read
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9
min read
Why Is Tracing Code Changes So Difficult?
Eliminating the Wild Goose Chase
Did this change go out?
What did we release this past week/month/quarter?
If I deploy this service, what’s going to be shipped?
Have we closed out completed work?
Visibility Is a Productivity Game Changer
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October 3, 2023

If you work in tech, you probably hear these questions more often than you’d like:

  • Did this change go out? Where is it now?
  • What did we release this past week/month/quarter?
  • If I deploy this service, what’s going to be shipped?

Whether you’re the person posing the question (customer success, product, marketing, business leader) or the one being asked (engineering manager, release manager), it’s frustrating nonetheless.

We should, by now, have an automated and self-serve way to get these questions answered definitely.

But for most organizations, that is not the case. Even if a ticket is done or a PR is complete, it’s often not quite clear whether the code shipped and value has been delivered.

Why Is Tracing Code Changes So Difficult?

Why did a product manager at a leading US bank have to drive to an ATM to see if their new ATM feature was finally live?!

Because it’s quite difficult to track the journey of new functionality through disconnected systems, especially as it’s changing shape along the way. Here’s why:

  1. Functionality, whether a bug or feature, traditionally starts as a task in a work management system like Jira. This initial task phase describes what needs to be done and the why behind it.
  2. From there, an engineer will translate it into code tracked through commits and PRs in version control systems like GitHub or GitLab.
  3. Code is eventually merged and packaged into artifacts that are deployed using a deployment system, e.g., Circle CI or Jenkins. These deployed artifacts take the functionality through different environments like dev and QA before finally delivering it to customers in production. In large organizations or complex systems, the deployment pipeline may involve multiple stages, environments, and checks.

Tracing the code changes up and down this toolchain requires strong integration between the tools and an understanding of the relationships between the various artifacts that encapsulate them.

The larger teams become and the more distributed geographically and architecturally, the harder it becomes to just know. With a microservices architecture, different services might be deployed independently. This can make it challenging to know if a specific feature, which might span multiple services, is fully live.

Further complicating matters, organizations (and even groups within them) have different cadences for advancing code from dev to production, restricted by code cadences and policies.

And, while a powerful tool for controlled releases, the wide adoption of feature flags also introduces uncertainty. A feature might be deployed to production but turned off, leading to confusion about its live status.

Some companies try to solve this problem with better labeling throughout all stages, however, I’ve found this to be brittle and error-prone and it only adds to an already complicated process.

Is the only solution manually verifying the issue yourself? Driving to the ATM? Even if you could afford the hassle, often you simply can’t! You don’t always have access to the software, environment, or configuration in question.

The bottom line is that if you really need to know what’s going on and where functionality is, a fair amount of digging and inference is involved.

Eliminating the Wild Goose Chase

Faros AI has solved this problem for me, and it can for you too.

As a complete and extensible software engineering intelligence platform, Faros AI knits together data from work management, source code, and deployment systems to trace code changes as they get merged, tested, built and deployed, and ultimately released.

As I’ve explained above, this is hard stuff. When a deployment happens, the deployment system can tell you which artifact went out, or, at best, the most recent commit that was released. But what else was in that artifact? Normally, you wouldn’t know.

Faros AI has made it trivial to unpack what was bundled into an artifact so you can easily unwind everything that went out with a given deployment. Each code change is traced not just through its production release; it’s also connected to its corresponding product context through the associated task and its parent (epic, feature).

Diagram of the relationship between epics, tasks, PRs, commits and artifacts as they progress through staging, QA and production environments
Faros AI unpacks a bundled artifact so you can easily unwind everything that went out with a given deployment

Here’s how I use Faros AI to utilize this information to answer those frequent “Has this code shipped” questions.

Did this change go out?

Below is a Faros AI chart that lets me and my colleagues easily see where we are on a current feature. I can see across the Jira ticket status, PR status, and which environment the change has made it to.

A Faros AI chart formatted as a table tracks a changes's Jira status, PR status, and current environment
A Faros AI chart tracks a changes's Jira status, PR status, and current environment

In this example, my colleagues in customer success can see that the bug is still in development, waiting for a review. However, the second item — a feature — is already in our staging environment and just awaiting a production release.

With Faros, the team can get accurate information in seconds without having to ask PMs or engineers for updates on every item.

What did we release this past week/month/quarter?

Every organization has reporting cadences where it’s necessary to understand what was released in the past week, month, or quarter. This information is vital for updating documentation, notifying customers, and preparing marketing communications.

Personally, I also love to look at this information when I get back from vacation; it helps me catch up on everything I missed.

Here’s a dashboard on Faros that summarizes what’s been released over the last 30 days. Looking at the Released Tasks with Epic and Sha table, I can see:

  • The ‘Mock data feed takes ‘now’ time as input’ task is done and all related commits have been released
  • The ‘Update CLI’ task is being worked on incrementally; some work has been released but the overall task is still in progress.
A Faros AI dashboard summarizes what's been released over the last 30 days with pie chart visualizations and a detailed table
A Faros AI dashboard summarizes what's been released over the last 30 days

Beyond a dashboard view, I utilize Faros automations to send a weekly update to our team on Slack and an email summary to leadership.

A screenshot of a Faros AI Slack notification containing a weekly update of what's been shipped to production this week
A Faros AI Slack notification sends a weekly update of what's been shipped to production this week

If I deploy this service, what’s going to be shipped?

With different teams contributing to the same code base, it’s important to know what I’ll be releasing when I pull the trigger.

This comes up often for us at Faros AI for services involving contractors or team members in different time zones.

Not everyone can be in the go/no-go decision about a release. Having a Faros AI dashboard to check what will go out gives me the peace of mind I need to kick off a release and the confidence to know what is about to go live.

This dashboard of “Stuff in Dev” has all the work that will go out in the next production release.

A Faros AI tables lists all the changes that will go out in the next production release
A Faros AI list of all the changes that will go out in the next production release

Have we closed out completed work?

Data hygiene can be a struggle, more so when the work on an epic or feature is distributed across multiple teams or contributors — each completing their work at a different pace. The unitary stories, tasks, or sub-tasks move to ‘Done’, but often the parent is forgotten in some “in progress” state.

At large organizations, it does become hard to know which epics should be closed out and when.

With Faros AI automations, you can create alerts to notify the epic owner when all the children stories are complete and the epic itself is still ‘In Progress’. This way, they can be sure to tie up any remaining activities required to close the parent.

Screenshot of a Slack notification from Faros AI notifying the epic owner when all child stories and tasks are complete
A Slack notification from Faros AI notifying the epic owner when all child stories and tasks are complete

Visibility Is a Productivity Game Changer

Our current economy has everyone trying to do more with fewer resources. GitHub Copilot is unlocking developer productivity. Software engineering intelligence platforms are doing the same for managers and leaders.

If you want visibility similar to what I have into code changes, deployments, and releases, you might want to try Faros AI. Our mission is to maximize the effectiveness and efficiency of software engineering, and that includes eliminating the scavenger hunt part of our jobs.

Natalie Casey

Natalie Casey

Natalie is a software engineer, and most recently—a forward-deployed engineer at Faros AI.

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