Efficient Binary-Level Coverage Analysis (Teaser, ESEC/FSE 2020)
Автор: ACM SIGSOFT
Загружено: 2020-11-02
Просмотров: 295
"Efficient Binary-Level Coverage Analysis (Teaser, ESEC/FSE 2020)
M. Ammar Ben Khadra, Dominik Stoffel, and Wolfgang Kunz
(TU Kaiserslautern, Germany; TU Kaiserslautern, Germany; TU Kaiserslautern, Germany)
Abstract: Code coverage analysis plays an important role in the software testing process. More recently, the remarkable effectiveness of coverage feedback has triggered a broad interest in feedback-guided fuzzing. In this work, we introduce bcov, a tool for binary-level coverage analysis. Our tool statically instruments x86-64 binaries in the ELF format without compiler support. We implement several techniques to improve efficiency and scale to large real-world software. First, we bring Agrawal’s probe pruning technique to binary-level instrumentation and effectively leverage its superblocks to reduce overhead. Second, we introduce sliced microexecution, a robust technique for jump table analysis which improves CFG precision and enables us to instrument jump table entries. Additionally, smaller instructions in x86-64 pose a challenge for inserting detours. To address this challenge, we aggressively exploit padding bytes and systematically host detours in neighboring basic blocks.
We evaluate bcov on a corpus of 95 binaries compiled from eight popular and well-tested packages like FFmpeg and LLVM. Two instrumentation policies, with different edge-level precision, are used to patch all functions in this corpus - over 1.6 million functions. Our precise policy has average performance and memory overheads of 14% and 22% respectively. Instrumented binaries do not introduce any test regressions. The reported coverage is highly accurate with an average F-score of 99.86%. Finally, our jump table analysis is comparable to that of IDA Pro on gcc binaries and outperforms it on clang binaries.
Article: https://doi.org/10.1145/3368089.3409694
Supplementary archive: https://doi.org/10.5281/zenodo.3876048 (Badges: Artifacts Available, Artifacts Evaluated — Reusable, Artifacts Evaluated — Functional)
Submitted to the conference by M. Ammar Ben Khadra on 2020-10-31
Video Tags: code coverage analysis, jump table analysis, binary instrumentation, fse20main-p208-p, DOI: 10.1145/3368089.3409694, DOI: 10.5281/zenodo.3876048, Artifacts Available, Artifacts Evaluated — Reusable, Artifacts Evaluated — Functional
Presentation at the ESEC/FSE 2020 conference, November 8–13, 2020, https://2020.esec-fse.org/
Sponsored by ACM SIGSOFT, https://www.sigsoft.org/
Twitter: / fseconf
Reddit: / esecfse "
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