Test Case Generation

Generating Highly-structured Input Data by Combining Search-based Testing and Grammar-based Fuzzing

Software testing is an important and time-consuming task that is often done manually. In the last decades, researchers have come up with techniques to generate input data (e.g., fuzzing) and automate the process of generating test cases (e.g., search-based testing). However, these techniques are known to have their own limitations: search-based testing does not generate highly-structured data; grammar-based fuzzing does not generate test case structures. To address these limitations, we combine these two techniques. By applying grammar-based mutations to the input data gathered by the search-based testing algorithm, it allows us to co-evolve both aspects of test case generation. We evaluate our approach by performing an empirical study on 20 Java classes from the three most popular JSON parsers across multiple search budgets. Our results show that the proposed approach on average improves branch coverage for JSON related classes by 15% (with a maximum increase of 50%) without negatively impacting other classes.

Crash Reproduction Using Helper Objectives

Evolutionary-based crash reproduction techniques aid developers in their debugging practices by generating a test case that reproduces a crash given its stack trace. In these techniques, the search process is typically guided by a single search objective called Crash Distance. Previous studies have shown that current approaches could only reproduce a limited number of crashes due to a lack of diversity in the population during the search. In this study, we address this issue by applying Multi-Objectivization using Helper-Objectives (MO-HO) on crash reproduction. In particular, we add two helper-objectives to the Crash Distance to improve the diversity of the generated test cases and consequently enhance the guidance of the search process. We assessed MO-HO against the single-objective crash reproduction. Our results show that MO-HO can reproduce two additional crashes that were not previously reproducible by the single-objective approach.

EvoSuite at the SBST 2020 Tool Competition

EvoSuite is a search-based tool that automatically generates executable unit tests for Java code (JUnit tests). This paper summarizes the results and experiences of EvoSuiteā€™s participation at the eighth unit testing competition at SBST 2020, where EvoSuite achieved the highest overall score (406.14 points) for the seventh time in eight editions of the competition.

Automated Test Generation for Unit Testing Beyond

Testing with Fewer Resources: An Adaptive Approach to Performance-Aware Test Case Generation

Abstract: Automated test case generation is an effective technique to yield high-coverage test suites. While the majority of research effort has been devoted to satisfying coverage criteria, a recent trend emerged towards optimizing other non-coverage aspects.

Cling

Test Case Generation for Class Integration Testing

EvoSuite at the SBST 2019 Tool Competition

Beyond Unit-Testing in Search-based Test Case Generation: Challenges and Opportunities

Search-based Multi-Vulnerability Testing of XML Injections in Web Applications

A Large Scale Empirical Comparison of State-of-the-art Search-based Test Case Generators