Software Testing Antipatterns: Misconceptions, Coverage, and Metrics

Continuing from the previous post that introduced basic testing concepts, this article presents three additional testing misconceptions (testing the wrong functionality, testing internal implementation, and over‑emphasizing coverage) and explains why they hinder productivity.

It argues that 100% test coverage is unrealistic and often counter‑productive; instead, teams should prioritize testing critical and core code, aiming for high coverage on those areas while accepting lower coverage on less impactful code.

A mental model is introduced that classifies code into three layers: critical code (most error‑prone and business‑impactful), core code (moderately important), and other code (stable and low impact). Test effort should focus on the first two layers.

The article proposes several custom testing metrics (PDWT, PBCNT, PTVB, PTD) to assess testing health, emphasizing the importance of developers writing tests, the ratio of bugs turned into tests, and the proportion of behavior‑driven tests versus implementation‑driven tests.

Practical advice includes writing tests that verify expected behavior rather than internal structures, using mocks for dependencies, centralising test setup in helper functions such as createSampleCustomer() , and avoiding excessive refactoring of test code when production code changes.

It warns against treating code‑coverage as a fixed target, recommending a realistic benchmark around 20% based on the Pareto principle, and stresses that achieving 100% coverage often wastes time that could be spent delivering real functionality.

Overall, the article encourages teams to focus on meaningful, business‑driven testing, to measure testing effectiveness with appropriate metrics, and to resist pressure from management to chase meaningless coverage numbers.