Interface Testing | Is High Automation Coverage Becoming a Strategic Burden?

1. The Paradox of "Automation First"

In today's era of pursuing "efficiency improvement," "cost reduction," and "automation first," almost every technical team is discussing automated testing. From UI scripts and interface regression to CI/CD integration and unattended releases, a complete automation chain has seemingly become the standard for modern testing projects.

But the reality is often frustrating:

• More automated test scripts are written, yet execution becomes slower and slower.

• Testers are trapped in an endless loop of "fixing scripts — failing runs — filling in logs."

• Actual defects are primarily discovered through manual exploratory testing.

• The more automated the project, the more anxious the delivery pace becomes.

Instead of bringing the expected efficiency liberation, automated testing has become a "sunk cost trap." The problem often lies not with the tools or technology, but with a flawed automated testing strategy that quietly drains team efficiency.

2. Misunderstanding 1: Treating "Automation Rate" as a KPI

Many teams fall into the trap of prioritizing numbers:

• UI test automation coverage: 80%

• Interface use case automation: 300+ items

• Daily builds automatically executing a full set of test cases.

While these numbers look beautiful, they ignore a fundamental question: Do these scripts truly improve quality and release efficiency?

The Strategic Shift:
Automation is a tool to increase testing value and rhythm, not a goal in itself. If automation does not improve decision-making confidence, reduce redundant work, or shorten return cycles, it loses its meaning. We must shift from "How much automation to do" to "What problems can automation solve for us?":

• Can it reduce release regression time?

• Can it support parallel development by multiple people?

• Can high-risk paths be stably covered?

3. Misunderstanding 2: Choosing the Wrong Automation Objects

Many teams equate automation with "full coverage" or "high-frequency path priority," often selecting the wrong targets:

• UI Layer: Piling automation on form clicks, dropdowns, and page jumps leads to high maintenance costs due to frequent changes.

• Interface Layer: Limiting automation to edge cases and negative verification results in coverage costs far exceeding the value.

• Omissions: Log analysis and data comparison of backend systems are often not automatically assisted.

Prioritization Guide:

• Prioritize Automation: Modules with high regression frequency, stable logic, and clear paths.

• Cautious Automation: Frequently changing UI pages, animation interactions, multi-language adaptations, and complex state dependencies.

• Auxiliary Automation: Log verification, data preparation, and Mocking upstream/downstream dependencies.

Remember: Not all testing should be automated, and not all automation should be tested.

4. The Maintenance Trap: "Easy to Launch, Hell to Maintain"

The most common automation trap is technical debt. If the strategy does not prioritize maintainability, the team will be tied down:

• Field and interface changes in every version cause daily script errors.

• Changes in UI element IDs cause thousands of XPaths to fail.

• Undecoupled data dependencies require "manual initialization" before every run.

• Unstable build environments render automated results useless.

Refinement Principles:

1. Test code is product code: Scripts should emphasize structure and readability as much as service code.

2. Abstraction Layers: Strengthen the PageObject/APIObject modes to build stable abstraction layers.

3. Modularity: Ensure data, environment, and statuses are modular, configurable, and mockable.

4. Diagnostic Mechanisms: Unify error logs to prevent situations where "locating errors is more difficult than repairing them."

5. The "All Green" Illusion vs. Real Risks

Many teams confidently claim: "We run hundreds of use cases every build, and the results are all green!" Yet, bugs still appear frequently after going online. This happens because these use cases:

• Do not contain new requirement scenarios.

• Do not cover non-functional factors (multi-threading, concurrency, network fluctuations).

• Lack coverage for data combinations and status transfers.

• Are not integrated into the product quality process (e.g., triggering regressions during PR stages or reviewing branches before merging).

Dynamic Strategy:
Automated testing only verifies "known paths" and cannot replace the exploration of unknown risks. It must be embedded into the entire lifecycle and supplemented by exploratory testing, chaos testing, and user behavior modeling.

6. Empowering Humans, Not Just Replacing Them

A common default belief is that "everything done by humans can be replaced by automation." This results in:

• Testers becoming "tool workers" maintaining scripts rather than quality designers.

• Squeezed growth space and reduced collaboration between development and testing.

The True Goal:
The goal of automated testing is to release the ability of testers to do higher-level, systematic test design.

• Free engineers from repetition to focus on test strategy, architecture review, and user perspective exploration.

• Introduce AI capabilities and low-code tools to make script writing lighter and smarter.

• Encourage Joint Ownership: Developers write testable code, while testers drive use-case design to jointly build quality responsibility.

7. Strategic Comparison Table

8. Conclusion: The Efficient Automation Strategy

Real test automation is not just "scripts running fast," but a strategic system that enhances product confidence and human creativity. It should feature:

1. Value Orientation: Prioritizing pain points over blind coverage.

2. System Embeddedness: Integrated into development, testing, and O&M.

3. Evolutionary Adaptability: Continuous optimization using AI/Agents and intelligent means.