Assignment Submission with Manual Software Testing: A 2027 Project Guide

Use a familiar campus workflow to practise Manual Software Testing: assignment submission. The user is a teaching assistant, and the first question is: What is the highest submitted revision in the selected assignment? The deliverable is a risk-based test report with a visible rule and repeatable evidence behind its output.

This is a 2027 study-edition project brief. All example records and operational situations are fictional. Start with basic Manual Software Testing 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

Revision numbers are positive integers, but the maximum revision is a version indicator rather than a measure of student ability. A withdrawn submission must not become the current submitted revision. For this exercise, one row represents one submission. The stable key is record_id; assignment_name is the grouping or filtering label. The numeric revision_number field uses revisions. Accepted states are draft, submitted, withdrawn.

The summary selects status submitted and applies max 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 Manual Software Testing approach

Translate the project rule into observable behaviour before opening the interface. Write a small set of high-value examples with input, action and expected outcome. Explore boundaries and recovery rather than counting repetitive test cases. A useful defect report gives a reproducible setup, actual result, expected result and evidence that another person can verify.

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

• Observable acceptance criteria. Apply this choice to submission records and explain its effect on revision_number.
• Boundary and state-transition tests. Apply this choice to submission records and explain its effect on revision_number.
• Reproducible defect evidence. Apply this choice to submission records and explain its effect on revision_number.

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": "assignment-submissions-01",
    "assignment_name": "Validation Exercise",
    "revision_number": 1,
    "status": "submitted",
    "recorded_on": "2027-01-04"
  },
  {
    "record_id": "assignment-submissions-02",
    "assignment_name": "Query Review",
    "revision_number": 3,
    "status": "draft",
    "recorded_on": "2027-01-05"
  },
  {
    "record_id": "assignment-submissions-03",
    "assignment_name": "Interface Prototype",
    "revision_number": 2,
    "status": "submitted",
    "recorded_on": "2027-01-06"
  }
]

Only the first and third records match submitted. Their revision_number values are 1 and 2; the full-fixture result is 2 revisions, with 2 matching records. Selecting assignment_name = Validation Exercise leaves the first record, so the result becomes 1 revisions, with 1 match. This provides two independent checks for the filter and aggregation.

The full-fixture output contract is:

{
  "matching_count": 2,
  "operation": "max",
  "value": 2,
  "unit": "revisions"
}

Check the result by assignment_name

The overall figure answers the all-records version of the question. Grouping by assignment_name 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. Turn the rule into assertions

Write an observable expectation for submitted submission records and the max of revision_number. Include the selected assignment_name and revisions label in the expected result. Define a clean setup so each case starts from known data.

2. Cover a change and a rejected input

Run the supplied scenario-specific acceptance check, then try a missing assignment_name, an invalid revision_number and a repeated identifier. Inspect both the returned response and resulting state. A rejection should explain the correction and preserve valid existing records.

3. Make the evidence reproducible

Record exact inputs, actions, expected outcomes and actual outcomes. Automate checks only at the boundary supported by the chosen tool. Keep fixtures independent and capture useful failure evidence without replacing a precise assertion with a screenshot alone.

Find the mistake before polishing the result

A useful defect to introduce deliberately is: The screen reports the number of rows as the latest revision. 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: Add a second revision and check the maximum rather than the row count. Preserve before-and-after evidence. Add a missing assignment_name, a negative revision_number, 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 Manual Software Testing concerns as well:

• A passed case needs a recorded outcome.
• A failure needs reproducible steps.
• Test data must not depend on a previous tester's run.

For an empty filter match, the expected max 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 teaching assistant question, the record definition and the expected calculation first. Then demonstrate a assignment_name 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 assignment_name 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 submission records belong in the result?

Select records whose status is exactly submitted and that satisfy the chosen assignment_name 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 revision_number result is correct?

Reconcile the first and third sample records, then repeat the calculation for Validation Exercise 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 Manual Software Testing 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 Project Milestone using Manual Software Testing, or compare Assignment Submission using API Testing. These companion practice guides are part of the 2027 learning collection.