Maximum stack memory monitoring method assisted by static analysis of the stack usage profile

Kiho Choi, Seongseop Kim, Moon Gi Seok, Jeonghun Cho, Daejin Park

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As IoT permeates through industry in general, the safety assurances of IoT will become a major issue. One of the major safety issues, stack overflow, is a bothersome and difficult problem because it is hard to discover during design and to prevent. Many related studies for preventing stack overflow have used two general methods. The static analysis method is employed before a program runs and estimates the program’s probable maximum stack memory usage. The dynamic analysis method is used to monitor for stack overflows during run-time. Based on those prior works, this paper introduces a method for monitoring stack memory based on static analysis of the maximum stack memory usage profile. We anticipate that applying the proposed approach will prevent stack overflow in an efficient manner.

Original languageEnglish
Title of host publicationAdvances in Computer Science and Ubiquitous Computing - CSA-CUTE 17
EditorsGangman Yi, Yunsick Sung, James J. Park, Vincenzo Loia
PublisherSpringer Verlag
Pages756-765
Number of pages10
ISBN (Print)9789811076046
DOIs
StatePublished - 2018
EventInternational Conference on Computer Science and its Applications, CSA 2017 - Taichung, Taiwan, Province of China
Duration: 18 Dec 201720 Dec 2017

Publication series

NameLecture Notes in Electrical Engineering
Volume474
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on Computer Science and its Applications, CSA 2017
Country/TerritoryTaiwan, Province of China
CityTaichung
Period18/12/1720/12/17

Keywords

  • Profile-based analysis
  • Stack overflow
  • Stack usage

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