Stateful Testing: Finding More Errors in Code and Contracts

TL;DR

Stateful testing is an automated technique that enhances existing test suites by generating test cases that challenge inferred contracts, leading to more error detection and more accurate contracts with minimal overhead.

Contribution

The paper introduces stateful testing, a novel method that improves test input diversity and contract accuracy by targeting dynamically inferred invariants.

Findings

Found 68.4% new errors in data structure classes

Improved contract accuracy to over 99%

Achieved only 7% additional testing time

Abstract

Automated random testing has shown to be an effective approach to finding faults but still faces a major unsolved issue: how to generate test inputs diverse enough to find many faults and find them quickly. Stateful testing, the automated testing technique introduced in this article, generates new test cases that improve an existing test suite. The generated test cases are designed to violate the dynamically inferred contracts (invariants) characterizing the existing test suite. As a consequence, they are in a good position to detect new errors, and also to improve the accuracy of the inferred contracts by discovering those that are unsound. Experiments on 13 data structure classes totalling over 28,000 lines of code demonstrate the effectiveness of stateful testing in improving over the results of long sessions of random testing: stateful testing found 68.4% new errors and improved the accuracy of automatically inferred contracts to over 99%, with just a 7% time overhead.