Automated Test-Case Generation for REST APIs Using Model Inference Search Heuristic

TL;DR

This paper introduces MISH, a novel search heuristic for automated REST API test case generation that leverages automaton learning to better capture system-wide behaviors, improving testing effectiveness for microservices.

Contribution

The paper proposes MISH, a new automaton-based search heuristic that enhances EvoMaster by capturing system-level behaviors during REST API testing.

Findings

MISH outperforms existing heuristics in test coverage on real-world microservices.

Automaton learning improves the relevance of generated test cases.

MISH demonstrates promising results compared to state-of-the-art techniques.

Abstract

The rising popularity of the microservice architectural style has led to a growing demand for automated testing approaches tailored to these systems. EvoMaster is a state-of-the-art tool that uses Evolutionary Algorithms (EAs) to automatically generate test cases for microservices' REST APIs. One limitation of these EAs is the use of unit-level search heuristics, such as branch distances, which focus on fine-grained code coverage and may not effectively capture the complex, interconnected behaviors characteristic of system-level testing. To address this limitation, we propose a new search heuristic (MISH) that uses real-time automaton learning to guide the test case generation process. We capture the sequential call patterns exhibited by a test case by learning an automaton from the stream of log events outputted by different microservices within the same system. Therefore, MISH learns a representation of the systemwide behavior, allowing us to define the fitness of a test case based on the path it traverses within the inferred automaton. We empirically evaluate MISH's effectiveness on six real-world benchmark microservice applications and compare it against a state-of-the-art technique, MOSA, for testing REST APIs. Our evaluation shows promising results for using MISH to guide the automated test case generation within EvoMaster.