Peer Feedback Tracker with CI/CD with GitHub Actions: A 2027 Project Guide

The starting point for this CI/CD with GitHub Actions exercise is a project team member working with feedback_entry records. Their question is specific: How many submitted suggestions cover each feedback area? Building a verification pipeline around that question gives the project a manageable scope and makes the result possible to verify.

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

Submitted suggestions count feedback items, not accepted improvements. Retracted feedback is preserved for traceability and excluded from the current submitted count. For this exercise, one row represents one feedback_entry. The stable key is record_id; feedback_area is the grouping or filtering label. The numeric suggestion_count field uses suggestions. Accepted states are draft, submitted, retracted.

The summary selects status submitted 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 feedback_entry records and explain its effect on suggestion_count.
• Separate jobs and artifacts. Apply this choice to feedback_entry records and explain its effect on suggestion_count.
• Minimal workflow permissions. Apply this choice to feedback_entry records and explain its effect on suggestion_count.

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": "peer-feedback-01",
    "feedback_area": "Usability",
    "suggestion_count": 25,
    "status": "submitted",
    "recorded_on": "2027-01-04"
  },
  {
    "record_id": "peer-feedback-02",
    "feedback_area": "Validation",
    "suggestion_count": 40,
    "status": "draft",
    "recorded_on": "2027-01-05"
  },
  {
    "record_id": "peer-feedback-03",
    "feedback_area": "Documentation",
    "suggestion_count": 15,
    "status": "submitted",
    "recorded_on": "2027-01-06"
  }
]

Only the first and third records match submitted. Their suggestion_count values are 25 and 15; the full-fixture result is 40 suggestions, with 2 matching records. Selecting feedback_area = Usability leaves the first record, so the result becomes 25 suggestions, 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": "suggestions"
}

Check the result by feedback_area

The overall figure answers the all-records version of the question. Grouping by feedback_area produces the following separate expectations, using the same submitted 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 feedback_entry records. Write down its startup command, configuration, health signal and storage policy. The sample sum of suggestion_count for submitted 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 feedback_area filter and returns the expected suggestions 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 retracted suggestion still appears in current submitted feedback. 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: Retract one entry and inspect both the active count and the record state. Preserve before-and-after evidence. Add a missing feedback_area, a negative suggestion_count, 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 project team member question, the record definition and the expected calculation first. Then demonstrate a feedback_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 feedback_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 feedback_entry records belong in the result?

Select records whose status is exactly submitted and that satisfy the chosen feedback_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 suggestion_count result is correct?

Reconcile the first and third sample records, then repeat the calculation for Usability 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 Bug Triage Board using CI/CD with GitHub Actions, or compare Peer Feedback Tracker using Git and GitHub Workflow. These companion practice guides are part of the 2027 learning collection.