DTD: Comprehensive and Scalable Testing for Debuggers

Author:

Lu Hongyi1ORCID,Liu Zhibo2ORCID,Wang Shuai2ORCID,Zhang Fengwei3ORCID

Affiliation:

1. Southern University of Science and Technology, Shenzhen, China / Hong Kong University of Science and Technology, Hong Kong, China

2. Hong Kong University of Science and Technology, Hong Kong, China

3. Southern University of Science and Technology, Shenzhen, China

Abstract

As a powerful tool for developers, interactive debuggers help locate and fix errors in software. By using debugging information included in binaries, debuggers can retrieve necessary program states about the program. Unlike printf-style debugging, debuggers allow for more flexible inspection and modification of program execution states. However, debuggers may incorrectly retrieve and interpret program execution, causing confusion and hindering the debugging process. Despite the wide usage of interactive debuggers, a scalable and comprehensive measurement of their functionality correctness does not exist yet. Existing works either fall short in scalability or focus more on the “compiler-side” defects instead of debugger bugs. To facilitate a better assessment of debugger correctness, we first propose and advocate a set of debugger testing criteria, covering both comprehensiveness (in terms of debug information covered) and scalability (in terms of testing overhead). Moreover, we design comparative experiments to show that fulfilling these criteria is not only theoretically appealing, but also brings major improvement to debugger testing. Furthermore, based on these criteria, we present DTD, a differential testing (DT) framework for detecting bugs in interactive debuggers. DTD compares the behaviors of two mainstream debuggers when processing an identical C executable — discrepancies indicate bugs in one of the two debuggers. DTD leverages a novel heuristic method to avoid the repetitive structures (e.g., loops) that exist in C programs, which facilitates DTD to achieve full debug information coverage efficiently. Moreover, we have also designed a Temporal Differential Filtering method to practically filter out the false positives caused by the uninitialized variables in common C programs. With these carefully designed techniques, DTD fulfills our proposed testing requirements and, therefore, achieves high scalability and testing comprehensiveness. For the first time, it offers large-scale testing for C debuggers to detect debugger behavior discrepancies when inspecting millions of program states. An empirical comparison shows that DTD finds 17× more error-triggering cases and detects 5× more bugs than the state-of-the-art debugger testing technique. We have used DTD to detect 13 bugs in the LLVM toolchain (Clang/LLDB) and 5 bugs in the GNU toolchain (GCC/GDB). One of our fixes has already landed in the latest LLDB development branch.

Funder

National Natural Science Foundation of China

Shenzhen Science and Technology Program

Publisher

Association for Computing Machinery (ACM)

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