A Novel Industry Grade Dataset for Fault Prediction based on Model-Driven Developed Automotive Embedded Software

被引：13

作者：

Altinger, Harald ^{[1
]}

Siegl, Sebastian ^{[1
]}

Dajsuren, Yanja ^{[2
]}

Wotawa, Franz ^{[3
]}

机构：

[1] Audi Elect Venture GmbH, D-85080 Gaimersheim, Germany

[2] Eindhoven Univ Technol, Eindhoven, Netherlands

[3] Graz Univ Technol, Inst Software Technol, A-8010 Graz, Austria

来源：

12TH WORKING CONFERENCE ON MINING SOFTWARE REPOSITORIES (MSR 2015) | 2015年

关键词：

D O I：

10.1109/MSR.2015.72

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：494 / 497

页数：4

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