HyDiff: Hybrid Differential Software Analysis

Yannic Noller, Corina Păsăreanu, Marcel Böhme, Youcheng Sun, Hoang Lam Nguyen, Lars Grunske

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Downloads (Pure)

Abstract

Detecting regression bugs in software evolution, analyzing side-channels in programs and evaluating robustness in deep neural networks (DNNs) can all be seen as instances of differential software analysis, where the goal is to generate diverging executions of program paths. Two executions are said to be diverging if the observable program behavior differs, e.g., in terms of program output, execution time, or (DNN) classification. The key challenge of differential software analysis is to simultaneously reason about multiple program paths, often across program variants.

This paper presents HyDiff, the first hybrid approach for differential software analysis. HyDiff integrates and extends two very successful testing techniques: Feedback-directed greybox fuzzing for efficient program testing and shadow symbolic execution for systematic program exploration. HyDiff extends greybox fuzzing with divergence-driven feedback based on novel cost metrics that also take into account the control flow graph of the program. Furthermore HyDiff extends shadow symbolic execution by applying four-way forking in a systematic exploration and still having the ability to incorporate concrete inputs in the analysis. HyDiff applies divergence revealing heuristics based on resource consumption and control-flow information to efficiently guide the symbolic exploration, which allows its efficient usage beyond regression testing applications. We introduce differential metrics such as output, decision and cost difference, as well as patch distance, to assist the fuzzing and symbolic execution components in maximizing the execution divergence.

We implemented our approach on top of the fuzzer AFL and the symbolic execution framework Symbolic PathFinder. Weillustrate HyDiff on regression and side-channel analysis for Java bytecode programs, and further show how to use HyDiff for robustness analysis of neural networks.
Original languageEnglish
Title of host publication International Conference on Software Engineering
PublisherACM
Pages1273-1285
ISBN (Print)978-1-4503-7121-6
DOIs
Publication statusEarly online date - 01 Jun 2020
EventInternational Conference on Software Engineering - Seoul, Korea, Republic of
Duration: 23 May 202029 May 2020
https://conf.researchr.org/home/icse-2020

Conference

ConferenceInternational Conference on Software Engineering
Abbreviated titleICSE
CountryKorea, Republic of
CitySeoul
Period23/05/202029/05/2020
Internet address

Fingerprint Dive into the research topics of 'HyDiff: Hybrid Differential Software Analysis'. Together they form a unique fingerprint.

  • Cite this

    Noller, Y., Păsăreanu, C., Böhme, M., Sun, Y., Nguyen, H. L., & Grunske, L. (2020). HyDiff: Hybrid Differential Software Analysis. In International Conference on Software Engineering (pp. 1273-1285). ACM. https://doi.org/10.1145/3377811.3380363