Feedback-Directed Random Test Generation

TLDR

The authors propose a feedback‑directed random test generation technique that improves test quality by using execution feedback during input construction. The method incrementally constructs test inputs by randomly selecting method calls, reusing arguments from earlier inputs, executing each input immediately, and using contract and filter checks to decide whether to discard or reuse the input for further generation. Experiments show that the technique produces unit test suites that preserve contracts, detect errors, and achieve coverage and error‑detection performance equal to or better than model checking and undirected random generation, finding numerous previously unknown bugs in large libraries.

Abstract

We present a technique that improves random test generation by incorporating feedback obtained from executing test inputs as they are created. Our technique builds inputs incrementally by randomly selecting a method call to apply and finding arguments from among previously-constructed inputs. As soon as an input is built, it is executed and checked against a set of contracts and filters. The result of the execution determines whether the input is redundant, illegal, contract-violating, or useful for generating more inputs. The technique outputs a test suite consisting of unit tests for the classes under test. Passing tests can be used to ensure that code contracts are preserved across program changes; failing tests (that violate one or more contract) point to potential errors that should be corrected. Our experimental results indicate that feedback-directed random test generation can outperform systematic and undirected random test generation, in terms of coverage and error detection. On four small but nontrivial data structures (used previously in the literature), our technique achieves higher or equal block and predicate coverage than model checking (with and without abstraction) and undirected random generation. On 14 large, widely-used libraries (comprising 780KLOC), feedback-directed random test generation finds many previously-unknown errors, not found by either model checking or undirected random generation.