research-article

Automated repair of binary and assembly programs for cooperating embedded devices

Authors:

Jonathan DiLorenzo,

Westley Weimer,

Stephanie ForrestAuthors Info & Claims

ACM SIGARCH Computer Architecture News, Volume 41, Issue 1

Pages 317 - 328

https://doi.org/10.1145/2490301.2451151

Published: 16 March 2013 Publication History

Abstract

We present a method for automatically repairing arbitrary software defects in embedded systems, which have limited memory, disk and CPU capacities, but exist in great numbers. We extend evolutionary computation (EC) algorithms that search for valid repairs at the source code level to assembly and ELF format binaries, compensating for limited system resources with several algorithmic innovations. Our method does not require access to the source code or build toolchain of the software under repair, does not require program instrumentation, specialized execution environments, or virtual machines, or prior knowledge of the bug type.

We repair defects in ARM and x86 assembly as well as ELF binaries, observing decreases of 86% in memory and 95% in disk requirements, with 62% decrease in repair time, compared to similar source-level techniques. These advances allow repairs previously possible only with C source code to be applied to any ARM or x86 assembly or ELF executable. Efficiency gains are achieved by introducing stochastic fault localization, with much lower overhead than comparable deterministic methods, and low-level program representations.

When distributed over multiple devices, our algorithm finds repairs faster than predicted by naive parallelism. Four devices using our approach are five times more efficient than a single device because of our collaboration model. The algorithm is implemented on Nokia N900 smartphones, with inter-phone communication fitting in 900 bytes sent in 7 SMS text messages per device per repair on average.

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Cited By

Eldh S(2022)On Technical Debt in Software Testing - Observations from IndustryLeveraging Applications of Formal Methods, Verification and Validation. Software Engineering10.1007/978-3-031-19756-7_17(301-323)Online publication date: 22-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-19756-7_17
Kim HKim SLee JJee KCha SCalandrino JTroncoso C(2023)Reassembly is hardProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620320(1469-1486)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620320
Lu PRuchkin ICleaveland MSokolsky OLee I(2023)Causal Repair of Learning-Enabled Cyber-Physical Systems2023 IEEE International Conference on Assured Autonomy (ICAA)10.1109/ICAA58325.2023.00009(1-10)Online publication date: Jun-2023
https://doi.org/10.1109/ICAA58325.2023.00009
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Index Terms

Automated repair of binary and assembly programs for cooperating embedded devices
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
2. Software and its engineering
  1. Software notations and tools

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Published In

cover image ACM SIGARCH Computer Architecture News

ACM SIGARCH Computer Architecture News Volume 41, Issue 1

ASPLOS '13

March 2013

540 pages

ISSN:0163-5964

DOI:10.1145/2490301

Issue’s Table of Contents

ASPLOS '13: Proceedings of the eighteenth international conference on Architectural support for programming languages and operating systems
March 2013
574 pages
ISBN:9781450318709
DOI:10.1145/2451116
General Chair:
Vivek Sarkar
Rice University, USA
,
Program Chair:
Rastislav Bodik
University of California, Berkeley, USA

Copyright © 2013 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 March 2013

Published in SIGARCH Volume 41, Issue 1

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Cited By

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https://dl.acm.org/doi/10.1007/978-3-031-19756-7_17
Kim HKim SLee JJee KCha SCalandrino JTroncoso C(2023)Reassembly is hardProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620320(1469-1486)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620320
Lu PRuchkin ICleaveland MSokolsky OLee I(2023)Causal Repair of Learning-Enabled Cyber-Physical Systems2023 IEEE International Conference on Assured Autonomy (ICAA)10.1109/ICAA58325.2023.00009(1-10)Online publication date: Jun-2023
https://doi.org/10.1109/ICAA58325.2023.00009
Langdon W(2023)Response to comments on “Jaws 30”Genetic Programming and Evolvable Machines10.1007/s10710-023-09474-y24:2Online publication date: 22-Nov-2023
https://dl.acm.org/doi/10.1007/s10710-023-09474-y
Leach KTimperley CAngstadt KNguyen-Tuong AHiser JPaulos APal PHurley PThomas CDavidson JForrest SGoues CWeimer W(2022)START: A Framework for Trusted and Resilient Autonomous Vehicles (Practical Experience Report)2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE55969.2022.00018(73-84)Online publication date: Oct-2022
https://doi.org/10.1109/ISSRE55969.2022.00018
Renzullo JWeimer WForrest S(2021)Multiplicative Weights Algorithms for Parallel Automated Software Repair2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS49936.2021.00107(984-993)Online publication date: May-2021
https://doi.org/10.1109/IPDPS49936.2021.00107
Guizzo GPetke JSarro FHarman M(2021)Enhancing Genetic Improvement of Software with Regression Test Selection2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)10.1109/ICSE43902.2021.00120(1323-1333)Online publication date: May-2021
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Liou JWang XForrest SWu C(2020)GEVOACM Transactions on Architecture and Code Optimization10.1145/341805517:4(1-28)Online publication date: 25-Nov-2020
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Liou JWang XForrest SWu CCoello Coello C(2020)GEVO-MLProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3398139(1849-1856)Online publication date: 8-Jul-2020
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Yuan YBanzhaf W(2020)Toward Better Evolutionary Program RepairACM Transactions on Software Engineering and Methodology10.1145/336000429:1(1-53)Online publication date: 30-Jan-2020
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