A Survey of Compiler Testing

Author:

Chen Junjie1ORCID,Patra Jibesh2,Pradel Michael2ORCID,Xiong Yingfei3,Zhang Hongyu4,Hao Dan3,Zhang Lu3

Affiliation:

1. College of Intelligence and Computing, Tianjin University, Tianjin, China

2. Department of Computer Science, University of Stuttgart, Stuttgart, Germany

3. Key Laboratory of High Confidence Software Technologies (Peking University), MoE, Beijing, China

4. School of Electrical Engineering and Computing, University of Newcastle, NSW, Australia

Abstract

Virtually any software running on a computer has been processed by a compiler or a compiler-like tool. Because compilers are such a crucial piece of infrastructure for building software, their correctness is of paramount importance. To validate and increase the correctness of compilers, significant research efforts have been devoted to testing compilers. This survey article provides a comprehensive summary of the current state-of-the-art of research on compiler testing. The survey covers different aspects of the compiler testing problem, including how to construct test programs, what test oracles to use for determining whether a compiler behaves correctly, how to execute compiler tests efficiently, and how to help compiler developers take action on bugs discovered by compiler testing. Moreover, we survey work that empirically studies the strengths and weaknesses of current compiler testing research and practice. Based on the discussion of existing work, we outline several open challenges that remain to be addressed in future work.

Funder

German Research Foundation within the ConcSys and Perf4JS projects

BMWF/Hessen within CRISP

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Reference131 articles.

1. Generating focused random tests using directed swarm testing

2. M. Amodio S. Chaudhuri and T. Reps. 2017. Neural attribute machines for program generation. ArXiv e-prints (May 2017). arxiv:cs.AI/1705.09231 M. Amodio S. Chaudhuri and T. Reps. 2017. Neural attribute machines for program generation. ArXiv e-prints (May 2017). arxiv:cs.AI/1705.09231

Cited by 91 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Syntax-Aware Mutation for Testing the Solidity Compiler;Computer Security – ESORICS 2023;2024

2. SLIM: A Secure and Lightweight Multi-Authority Attribute-Based Signcryption Scheme for IoT;IEEE Transactions on Information Forensics and Security;2024

3. A language-parametric test coverage framework for executable domain-specific languages;Journal of Systems and Software;2024-01

4. Generation-based Differential Fuzzing for Deep Learning Libraries;ACM Transactions on Software Engineering and Methodology;2023-12-23

5. Detection of Optimizations Missed by the Compiler;Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering;2023-11-30

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