Regression Impact Analysis

Regression Test Case Impact Analysis

When to use this skill

• When a developer submits a PR or code change and you need to know which manual test cases to run.
• When you provide a "JIRA ticket" or "Git Diff" and an "Existing test case" Excel/CSV file.
• When there is a need to optimize testing time by pruning unnecessary test runs.

Workflow

1. Change Detection:
   • Git Diff: Parse the git diff to identify modified files, function names, and UI components.
   • JIRA Ticket: Extract "Module", "Description", and "Fixed in" fields to understand the functional change.
2. Impact Mapping:
   • Correlate the changed files/modules with the "Module" or "Feature" column in the test case Excel.
   • Search for keywords from the code change in the "Test Description" or "Step" columns of the test cases.
3. Selection & Analysis:
   • Identify "Direct Impact" (cases covering the exact module changed).
   • Identify "Indirect Impact" (cases covering downstream dependencies or shared utilities).
4. Reporting:
   • Generate a "Module Changed vs. Impacted Test Cases" table.
   • Provide a list of "High Priority" regression cases.

Instructions

1. Analyzing Code Changes

Look for modified paths:

• If src/auth/* changed -> Map to Login, Register, MFA test cases.
• If components/shared/Button.js changed -> Map to ALL UI test cases using buttons.

2. Matching with Test Cases

import pandas as pd
# Load test cases
df = pd.read_excel('test_cases.xlsx')
# Search for keyword 'Login' in 'Component' or 'Module' column
impacted = df[df['Module'].str.contains('Login', case=False)]

3. Output Format

Checklist

• Identify modified files/functions from Diff/JIRA.
• Scan Test Case Excel for matching Modules/Features.
• Group by "Direct" vs "Indirect" impact.
• Generate the optimized regression list.

Resources

• Sample Impact Report
• Diff Parser Helper