Advanced White-Box Heuristics for Search-Based Fuzzing of REST APIs

Author:

Arcuri Andrea1ORCID,Zhang Man2ORCID,Galeotti Juan3ORCID

Affiliation:

1. Kristiania University College and Oslo Metropolitan University, Oslo, Norway

2. Beihang University, Beijing, China

3. University of Buenos Aires, CONICET and Kristiania University College, Buenos Aires, Argentina

Abstract

Due to its importance and widespread use in industry, automated testing of REST APIs has attracted major interest from the research community in the last few years. However, most of the work in the literature has been focused on black-box fuzzing. Although existing fuzzers have been used to automatically find many faults in existing APIs, there are still several open research challenges that hinder the achievement of better results (e.g., in terms of code coverage and fault finding). For example, under-specified schemas are a major issue for black-box fuzzers. Currently, EvoMaster is the only existing tool that supports white-box fuzzing of REST APIs. In this paper, we provide a series of novel white-box heuristics, including for example how to deal with under-specified constrains in API schemas, as well as under-specified schemas in SQL databases. Our novel techniques are implemented as an extension to our open-source, search-based fuzzer EvoMaster . An empirical study on 14 APIs from the EMB corpus, plus one industrial API, shows clear improvements of the results in some of these APIs.

Funder

European Research Council

European Union’s Horizon 2020 research and innovation programme

UBACYT-2020

Publisher

Association for Computing Machinery (ACM)

Reference100 articles.

1. [n.d.]. APIs.guru. Online Accessed March 26 2024 https://apis.guru/

2. [n.d.]. EvoMaster. Online Accessed March 26 2024 https://github.com/EMResearch/EvoMaster

3. [n.d.]. EvoMaster Benchmark (EMB). Online Accessed March 26 2024 https://github.com/EMResearch/EMB

4. [n.d.]. Fuzz-lightyear: Stateful Fuzzing Framework. Online Accessed March 26 2024 https://github.com/Yelp/fuzz-lightyear

5. [n.d.]. GraphQL Foundation. Online Accessed March 26 2024 https://graphql.org/foundation/

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3