A novel industry grade dataset for fault prediction based on model-driven developed automotive embedded software

Harald Altinger, Sebastian Siegl, Yanja Dajsuren, Franz Wotawa

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

Abstract

In this paper, we present a novel industry dataset on static software and change metrics for Matlab/Simulink models and their corresponding auto-generated C source code. The data set comprises data of three automotive projects developed and tested accordingly to industry standards and restrictive software development guidelines. We present some background information of the projects, the development process and the issue tracking as well as the creation steps of the dataset and the used tools during development. A specific highlight of the dataset is a low measurement error on change metrics because of the used issue tracking and commit policies.

Original languageEnglish
Title of host publicationProceedings - 12th Working Conference on Mining Software Repositories, MSR 2015
PublisherIEEE Computer Society
Pages494-497
Number of pages4
ISBN (Electronic)9780769555942
DOIs
Publication statusPublished - 4 Aug 2015
Externally publishedYes
Event12th Working Conference on Mining Software Repositories, MSR 2015 - Florence, Italy
Duration: 16 May 201517 May 2015

Publication series

NameIEEE International Working Conference on Mining Software Repositories
Volume2015-August
ISSN (Print)2160-1852
ISSN (Electronic)2160-1860

Conference

Conference12th Working Conference on Mining Software Repositories, MSR 2015
CountryItaly
CityFlorence
Period16/05/1517/05/15

Keywords

  • Automotive
  • Fault prediction
  • Industry dataset
  • Model metrics
  • Source metrics

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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