一句话：提出用模糊测试推断类规格的方法，以可扩展地支持不同断言语言与算子。
类规格以可执行约束形式表达，对测试生成、缺陷发现和自动调试等任务很重要，但开发者很少编写。现有从代码推断规格的技术往往只支持特定断言类型，难以适配不同断言语言，例如加入量词或 membership/containment 等比较算子。针对这一可扩展性问题，论文提出以模糊测试为核心的类规格推断方法：通过 fuzzing 驱动执行并收集行为，生成可执行约束，从而降低对固定断言形式的依赖。摘要未报告具体实验数据；评估应关注推断规格的质量、在下游任务中的效用，以及扩展新断言语言与算子时的可适配性。
Expressing class specifications via executable constraints is important for various software engineering tasks such as test generation, bug finding and automated debugging, but developers rarely write them. Techniques that infer specifications from code exist to fill this gap, but they are designed to support specific kinds of assertions and are difficult to adapt to support different assertion languages, e.g., to add support for quantification, or additional comparison operators, such as membership or containment.
DOI 原文 ·
@inproceedings{paperbot6905,
title = {Fuzzing class specifications},
author = {Facundo Molina and Marcelo d'Amorim and Nazareno Aguirre},
booktitle = {ICSE},
year = {2022},
doi = {10.1145/3510003.3510120}
}