Why Software Testing Is Really About Risk Avoidance: Key Insights

ChatGPT has sparked both technological revolutions and anxiety, leaving many learners uncertain about the future.

I once wanted to discuss “the essence of software testing,” but couldn’t articulate it clearly; after nearly a year, I offer a provisional answer: risk avoidance .

Indeed, the essence of software testing is to avoid risk . When we choose not to test, the underlying reason is to accept risk .

Testing can’t be finished? The project timeline is fixed, so just release.

There are 500 regression test cases? Test as much as possible.

Need more testers? Skip hiring and add them to development.

These three scenarios illustrate countless situations where teams are forced to accept risk.

Under the foundation of “risk avoidance,” how can we make a product bug‑free?

Typically there are two starting points:

Reduce bug generation.

Discover bugs effectively.

Before tackling these, we must define what a bug is—a philosophical question even more challenging than epistemology.

Recent news reported a ChatGPT bug where it couldn’t limit answers to ten characters, prompting many to suggest better constraints or model training.

Thus, bugs often originate from users, and companies create products to satisfy users; any unmet user need is perceived as a bug.

Why do users switch to competitors? Likely because bugs affect speed and stability.

Why do users complain about slowness or lag? Not just network issues.

Returning to the two starting points:

Reduce bug generation – from a developer’s perspective, practices such as unit testing, pair programming, code review, and static analysis help prevent bugs.

Discover bugs – from a tester’s perspective, approaches like left‑shift testing, automated regression, and intensive daily builds are common.

We won’t argue which method is superior; instead, we’ll use them as a basis to discuss the three problems mentioned earlier.

First, the proportion of testing investment in a company

Assess testing investment from three angles: manpower, time, and money.

Manpower: ratio of developers to testers.

Time: how much testing time is allocated within a release cycle.

Money: personnel costs plus hardware/software procurement.

“Whenever a company needs to invest, testing is rarely the priority.”

Often, product revenue drives investment; even if a product has issues, it still sells, as seen in early Shopee experiences where bugs didn’t prevent massive growth.

Many bugs are not code problems but business‑model issues; they can be mitigated by exposing bugs early or communicating maintenance windows to users.

Second, the recursion of testing

Even if a company invests in AI for testing, a recursive problem arises: who tests the automated tests themselves? This mirrors SRE’s pursuit of ultra‑high availability, where each additional “9” incurs greater cost and complexity.

Third, testability

Assuming recursion can be solved, testability remains a challenge. Different technologies (polling, long‑polling, WebSocket) require distinct test points, demanding broader expertise from testers.

When testability is limited, humans must still perform final checks; automation aims to reduce manual effort, not eliminate it entirely.

These three issues form a loop: reducing automation saves labor, but deeper automation demands higher investment, creating a dilemma.

Therefore, I advocate for semi‑automated testing that maintains human involvement rather than pursuing full automation.

When challenged that “people still need to test, won’t they make mistakes?” I ask: “Are your product’s users humans or machines?”

Even if robots replace many tasks, we can become robot‑maintenance engineers; a broken robot still requires work.

Remember the truth: every bug creates a job opportunity .