Building Software
Engineering Fundamentals for the Agent Era
Contents Section 2, Verification
Code that passes every hand-written example and fails on an input nobody thought of, such as an empty string, a huge number or combining characters.
High line coverage with weak assertions, so deliberately introduced bugs go unnoticed.
A parser or decoder that crashes or hangs on malformed input.
Checking invariants across thousands of generated inputs, and measuring whether your tests would actually catch a bug.
An agent wrote slug = s => s.toLowerCase().replace(/ /g, '-').replace(/[^a-z0-9-]/g, '') with eight passing example tests, including 'Hello World' becoming 'hello-world'. Write three properties: a slug contains only [a-z0-9-], never starts or ends with a hyphen, and slugging a slug changes nothing. Without running anything, find an input that breaks each property that can be broken, and explain why the others cannot be.
Watch
Moves from example tests to properties and names the reusable property patterns, including round-trip, invariant, idempotence and test oracle, that the slug-function drill asks for.
The creator of PIT explains how mutation testing seeds small bugs to show which tests would miss them, and why line coverage alone cannot tell you that.
The designer of Go's native fuzzing explains how coverage-guided fuzzing mutates inputs to find crashes in parsers, and introduces differential fuzzing as a way to get an oracle.
The most thorough book-length treatment of how to think in properties, write custom generators, read shrunk counterexamples and do stateful testing. The ideas carry over directly to fast-check and Hypothesis.
A free, executable textbook that builds random, mutation-based, grammar-based and coverage-guided fuzzers from scratch, and explains what each one finds in malformed-input handling.
Defines coverage criteria (statement, branch, logic, mutation) formally, making clear what each criterion guarantees and what a high coverage number leaves untested.
Paper
The original property-based testing paper: properties as executable specifications, random generators, and the observation that the properties often find errors in the specification itself.
Paper
Shows how Google made mutation testing practical by showing surviving mutants to developers during code review, and whether developers found those mutants useful.