Portfolio Review Queue with Git and GitHub Workflow: A 2027 Project Guide

The starting point for this Git and GitHub Workflow exercise is a peer-review organizer working with review records. Their question is specific: What is the average time spent on completed portfolio reviews? Building a reviewable change history 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 Git and GitHub Workflow 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

Average completed review duration is an operations metric, not a judgement of the learner. A withdrawn review must not count as a completed assessment. For this exercise, one row represents one review. The stable key is record_id; project_category is the grouping or filtering label. The numeric review_minutes field uses minutes. Accepted states are queued, completed, withdrawn.

The summary selects status completed and applies mean 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 Git and GitHub Workflow approach

Break the work into changes that a reviewer can understand: fixture, validation, implementation and tests. Inspect the diff before committing and keep generated outputs out unless they are deliberate deliverables. Practice resolving a conflict on a disposable branch. Explain the difference between a local commit, a remote push and a deployed application.

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

• Small coherent commits. Apply this choice to review records and explain its effect on review_minutes.
• Diff review before commit. Apply this choice to review records and explain its effect on review_minutes.
• Deliberate merge-conflict resolution. Apply this choice to review records and explain its effect on review_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": "portfolio-reviews-01",
    "project_category": "Web Application",
    "review_minutes": 25,
    "status": "completed",
    "recorded_on": "2027-01-04"
  },
  {
    "record_id": "portfolio-reviews-02",
    "project_category": "Data Report",
    "review_minutes": 40,
    "status": "queued",
    "recorded_on": "2027-01-05"
  },
  {
    "record_id": "portfolio-reviews-03",
    "project_category": "Mobile Prototype",
    "review_minutes": 15,
    "status": "completed",
    "recorded_on": "2027-01-06"
  }
]

Only the first and third records match completed. Their review_minutes values are 25 and 15; the full-fixture result is 20 minutes, with 2 matching records. Selecting project_category = Web Application 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": "mean",
  "value": 20,
  "unit": "minutes"
}

Check the result by project_category

The overall figure answers the all-records version of the question. Grouping by project_category produces the following separate expectations, using the same completed 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. Create a reviewable project boundary

Start a disposable branch for the review exercise. Add the fixture and a short explanation of project_category, review_minutes and allowed states. Keep environment-specific data out of the change so another learner can reproduce the project.

2. Develop through coherent revisions

Commit the validation, the completed-only mean, and its tests as understandable changes. Review the diff after each step. The result in minutes should be traceable to a particular fixture and source revision, making a regression easier to locate.

3. Practice review and recovery

Make two small conflicting edits to the same rule on local practice branches. Resolve the conflict by preserving the documented acceptance test. Verify the final result from a fresh checkout and explain what remains local, pushed or deployed.

Find the mistake before polishing the result

A useful defect to introduce deliberately is: Withdrawn reviews are assigned a fabricated zero duration. 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: Leave withdrawn duration outside the completed-review average. Preserve before-and-after evidence. Add a missing project_category, a negative review_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 Git and GitHub Workflow concerns as well:

• A fresh checkout must reproduce the exercise.
• The diff must not include secrets or unrelated files.
• A conflict resolution must preserve the intended rule.

For an empty filter match, the expected mean value is undefined (no matching observations). 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 peer-review organizer question, the record definition and the expected calculation first. Then demonstrate a project_category 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 project_category 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 review records belong in the result?

Select records whose status is exactly completed and that satisfy the chosen project_category 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?

Treat the empty result as undefined and show a clear no-observations 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 review_minutes result is correct?

Reconcile the first and third sample records, then repeat the calculation for Web Application 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 Git and GitHub Workflow 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 Peer Feedback Tracker using Git and GitHub Workflow, or compare Portfolio Review Queue using Linux Administration. These companion practice guides are part of the 2027 learning collection.