Accessibility Audit Findings with CI/CD with GitHub Actions: A 2027 Project Guide

A convincing CI/CD with GitHub Actions portfolio project explains the result as well as showing it. Here, a student interface tester asks: How much retest effort belongs to verified findings by interface area? You will build a verification pipeline and follow the relevant records from a small fixture to an independently checked answer.

This is a 2027 study-edition project brief. All example records and operational situations are fictional. Start with basic CI/CD with GitHub Actions knowledge, the ability to inspect structured data and a local practice environment. The complete first version should answer the stated question before adding accounts, real integrations or additional screens.

Define the project rule

Verified retest minutes are activity data, not a claim of complete accessibility compliance. Reopened findings must return to the active queue without deleting the previous retest evidence. For this exercise, one row represents one finding. The stable key is record_id; interface_area is the grouping or filtering label. The numeric retest_minutes field uses minutes. Accepted states are open, verified, reopened.

The summary selects status verified and applies sum to the selected numeric values. Keep this definition visible in the interface, response or report so another learner can reproduce it. Use positive finite values in the starter fixture and define any future zero or missing-value policy explicitly.

Choose the CI/CD with GitHub Actions approach

Run the same validation command locally and in automation. Separate checking, packaging and deployment so a failed check cannot be mistaken for a release. Grant only the permissions needed for the exercise and avoid printing secrets in logs. Start without a deployment stage, then document the conditions under which a release could be approved.

The three design decisions to explain in your project notes are:

• Reproducible validation commands. Apply this choice to finding records and explain its effect on retest_minutes.
• Separate jobs and artifacts. Apply this choice to finding records and explain its effect on retest_minutes.
• Minimal workflow permissions. Apply this choice to finding records and explain its effect on retest_minutes.

Work through a small sample

The three records below are deliberately small enough to inspect by hand. They are a compact specification for the data shape and expected result, and can later be expanded into a larger synthetic fixture.

[
  {
    "record_id": "accessibility-audits-01",
    "interface_area": "Navigation",
    "retest_minutes": 25,
    "status": "verified",
    "recorded_on": "2027-01-04"
  },
  {
    "record_id": "accessibility-audits-02",
    "interface_area": "Booking Form",
    "retest_minutes": 40,
    "status": "open",
    "recorded_on": "2027-01-05"
  },
  {
    "record_id": "accessibility-audits-03",
    "interface_area": "Results Table",
    "retest_minutes": 15,
    "status": "verified",
    "recorded_on": "2027-01-06"
  }
]

Only the first and third records match verified. Their retest_minutes values are 25 and 15; the full-fixture result is 40 minutes, with 2 matching records. Selecting interface_area = Navigation leaves the first record, so the result becomes 25 minutes, with 1 match. This provides two independent checks for the filter and aggregation.

The full-fixture output contract is:

{
  "matching_count": 2,
  "operation": "sum",
  "value": 40,
  "unit": "minutes"
}

Check the result by interface_area

The overall figure answers the all-records version of the question. Grouping by interface_area produces the following separate expectations, using the same verified rule. The middle group has a record in the source fixture but no qualifying observation.

Use this breakdown when answering the group-level question. Compare like-for-like units and keep the matching count visible, especially for averages. A different filter changes which records enter the calculation; it does not change the meaning of the metric.

The code blocks describe sample data and an expected result. Implement the surrounding feature using the installed version of your chosen tools, then save the actual run evidence separately from these expectations.

Build the feature in three passes

1. Define the application contract

Use an existing local fixture application that processes finding records. Write down its startup command, configuration, health signal and storage policy. The sample sum of retest_minutes for verified records is an output check, not the whole deployment.

2. Make the environment repeatable

Package or configure the application using a named local lab. Keep fixture data and secrets separate. Run a verification that reads the interface_area filter and returns the expected minutes result. Record the revision and configuration used for that run.

3. Observe one controlled failure

Stop an instance, introduce a failing fixture check or invalidate a local configuration value. Inspect the error evidence, restore the previous working state and run the same verification again. Document which data survives and how the lab can be cleaned up safely.

Find the mistake before polishing the result

A useful defect to introduce deliberately is: A verified count is used to claim full accessibility compliance. First reproduce it against the fixture, then identify the smallest rule or state transition that explains the mismatch. Fix that boundary and rerun the full-fixture and selected-label examples. This creates a stronger explanation than changing several unrelated parts until the screen looks right.

The scenario-specific acceptance check is: Keep the summary scoped to tested findings and show reopened items. Preserve before-and-after evidence. Add a missing interface_area, a negative retest_minutes, an unrecognised status and a duplicate record_id as separate invalid cases. Define the rejection or duplicate-handling policy before testing, and verify that invalid input does not silently change valid existing records.

Check the CI/CD with GitHub Actions concerns as well:

• A broken fixture must fail the check.
• An artifact must identify its source revision.
• A pull-request run must not deploy by accident.

For an empty filter match, the expected sum value is 0. Keep matching_count at zero. This distinction prevents an empty dataset from being interpreted as a measured observation.

Explain the finished work in a portfolio

Show the student interface tester question, the record definition and the expected calculation first. Then demonstrate a interface_area filter and the acceptance check. Include the fixture, setup instructions, chosen tool versions, actual test output and one remaining limitation in the repository. If you show a screenshot, pair it with the input that produced it. Another learner should be able to repeat the same result from a clean checkout.

For a focused extension, add a second interface_area selection and compare the two groups using the same rule. Specify how missing groups appear and keep the original fixture as a regression case. Expand scope only after the current calculation, state handling and recovery path remain understandable.

Questions learners often ask

Which finding records belong in the result?

Select records whose status is exactly verified and that satisfy the chosen interface_area filter. Other states remain in the source data and can have separate summaries. Follow the domain rule above when a record changes state.

What should happen when no records match?

Return a zero total with a matching count of zero and a clear empty-result message. Keep the applied filter visible so the user can change it. Avoid using a loading indicator for a completed request that simply has no matching data.

How can I prove the retest_minutes result is correct?

Reconcile the first and third sample records, then repeat the calculation for Navigation alone. Save both expected and actual outputs, and rerun the scenario-specific acceptance check after making a change.

Continue learning with Groot Academy

Use this exercise as a concrete discussion point when choosing a learning path. Browse the current Groot Academy course directory and ask about the current syllabus, learning mode and project practice. Course availability and batch details should be confirmed directly with the academy.

For implementation details, consult the official CI/CD with GitHub Actions documentation matching the tools you install. This project defines an original practice brief; it does not depend on a forecast about 2027 software releases.

Continue with Computer Lab Inventory using CI/CD with GitHub Actions, or compare Accessibility Audit Findings using Git and GitHub Workflow. These companion practice guides are part of the 2027 learning collection.