Automation Testing Realities: Debunking Misconceptions and Bridging Knowledge Gaps

In the realm of automation testing, there's a persistent misconception that finding defects equates to preventing them. However, this overlooks a crucial distinction: proactive defect identification doesn't inherently prevent issues—it's a step towards ensuring quality. During discussions with cross-vendor teams, it became evident that some teams may not find defects due to a lack of understanding of the differences between Waterfall and Agile methodologies. This article will highlight these misconceptions and educate to bridge knowledge gaps in regression testing to uncover dormant issues and prevent post-production leaks.

Common Misconceptions in Automation Regression

1. Static Regression Count: Teams maintain a fixed set of regression tests across releases, assuming it provides comprehensive coverage without adapting to project dynamics.
2. Passing Regression Suite Assumption: There’s a misconception that a passing regression suite guarantees 100% test coverage, potentially missing critical areas.
3. Production Issues as Maintenance: Despite passing tests, teams often encounter production issues categorized as routine maintenance rather than preventable problems.

Identifying the root cause of these misconceptions reveals that "some teams still regress in a waterfall manner for incrementing projects", which was surprising to me. With the majority of projects now adopting Agile methodologies, it's crucial to understand the distinct approaches to regression testing in both Waterfall and Agile frameworks.

Addressing the Regression Test Count Disparity

The number of regression tests in Waterfall and Agile methodologies can vary significantly based on several factors, including project size, complexity, and specific practices followed. To illustrate this, consider the example of a student registration form in a university application and the ability to sign in and do some transaction.

Waterfall Regression Testing

In the Waterfall model, all the regression testing typically happens after the development phase, before the deployment. Given the project's size, here’s an example test count:

• Initial Test Cases: Let's assume 500 test cases are created to cover all functionalities.
• Regression Test Cases: These test cases are reused for regression testing. Number of Regression Test Cycles: Typically, there may be 2-3 full regression test cycles before the final release. Total Tests Executed per Cycle: All 500 test cases.

Thus, for 3 regression cycles:

• Total Regression Tests Executed: 500?tests×3?cycles=1500?

Agile Regression Testing

In the Agile model, regression testing is continuous and happens at the end of each sprint. Here's how it could look:

• Initial Test Cases: Let's assume 200 test cases for the initial features in the first sprint.
• Incremental Test Cases: Each sprint adds new features and potentially new test cases. Assume each sprint adds 50 new test cases on average.

Sprint-wise Test Case Addition:

• Sprint 1: 200 tests (initial)
• Sprint 2: 200 + 50 = 250 tests
• Sprint 3: 250 + 50 = 300 tests
• Sprint 4: 300 + 50 = 350 tests
• Sprint 5: 350 + 50 = 400 tests
• Sprint 6: 400 + 50 = 450 tests
• Sprint 7: 450 + 50 = 500 tests
• Sprint 8: 500 + 50 = 550 tests
• Sprint 9: 550 + 50 = 600 tests
• Sprint 10: 600 + 50 = 650 tests

Total Tests Executed per Sprint:

• Assuming all previous test cases are run in each sprint's regression cycle

• ∑: Represents the summation symbol.
• i: Iterates through each sprint.
• T0: Initial number of test cases.
• Delta ΔT: Incremental test cases added per sprint.
• n: Total number of sprints.

Summary:

• Waterfall: 1500 tests across 3 regression cycles.
• Agile: 4250 tests across 10 sprints, with incremental addition of test cases.

As a consultant specializing in automation testing, I consistently achieve higher issue detection rates during testing cycles. This success stems from my proactive approach of writing tests based on increments—an approach not universally adopted. By aligning closely with incremental developments, I ensure comprehensive coverage and early issue detection. This contrasts sharply with traditional methods where testing often occurs as a single-phase activity post-development.

This example illustrates how Agile methodologies typically involve a larger number of regression tests executed incrementally over time, whereas the Waterfall approach consolidates regression testing toward the end of the development cycle.

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