Usage log-based testing of embedded software and identification of dependencies among environmental components

Sooyong Jeong, Sungdeok Cha, Woo Jin Lee

Research output: Contribution to journalArticlepeer-review


Embedded software often interacts with multiple inputs from various sensors whose dependency is often complex or partially known to developers. With incomplete information on dependency, testing is likely to be insufficient in detecting errors. We propose a method to enhance testing coverage of embedded software by identifying subtle and often neglected dependencies using information contained in usage log. Usage log, traditionally used primarily for investigative purpose following accidents, can also make useful contribution during testing of embedded software. Our approach relies on first individually developing behavioral model for each environmental input, performing compositional analysis while identifying feasible but untested dependencies from usage log, and generating additional test cases that correspond to untested or insufficiently tested dependencies. Experimental evaluation was performed on an Android application named Gravity Screen as well as an Arduino-based wearable glove app. Whereas conventional CTM-based testing technique achieved average branch coverage of 26% and 68% on these applications, respectively, proposed technique achieved 100% coverage in both.

Original languageEnglish
Pages (from-to)2011-2014
Number of pages4
JournalIEICE Transactions on Information and Systems
Issue number11
Publication statusPublished - 2021

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (NRF-2017R1D1A3B04035880 and NRF-2018R1A6A1A030251 09).

Publisher Copyright:
Copyright © 2021 The Institute of Electronics, Information and Communication Engineers


  • Embedded software testing
  • Environmental modeling
  • Test coverage

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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