a novel industry grade dataset for fault prediction based ...€¦ · loc_add 1.0 0.807 0.434 0.132...
TRANSCRIPT
Altinger Harald Audi Electronics Venture GmbH
Sachsstrasse 20, 85080 Gaimersheim [email protected]
A Novel Industry Grade Dataset for Fault Prediction based on Model-Driven Developed
Automotive Embedded Software
Sebastian Siegl Audi Electronics Venture GmbH
Sachsstrasse 20, 85080 Gaimersheim [email protected]
Yanja Dajsuren Software Engineering and Technology Group
Eindhoven University of Technology [email protected]
Franz Wotawa Institute for Software Technology
Graz University of Technology [email protected]
Fig 1: Development Workflow
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Project A 304 13 12465 185 4 45 26 1782 78 logic, timing dependent behaviour yes no
Project L 600 8 10113 680 3 20 47 2892 73 logic, timing dependent behaviour yes yes
Project K 900 24 36526 695 5 53 48 2481 329 mainly logic operations and branching yes yes
Project Overview
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author 1.0 0.005 -0.0 0.015 0.001 0.01 0.06 0.043 -0.004 0.045
sloc 1.0 0.783 0.909 0.852 0.922 0.389 0.383 0.712 0.232
McCab 1.0 0.766 0.739 0.775 0.407 0.41 0.805 0.262
Hv 1.0 0.838 0.94 0.366 0.359 0.7 0.241
Hd 1.0 0.898 0.372 0.366 0.701 0.23
He 1.0 0.375 0.368 0.702 0.242
loc_add 1.0 0.807 0.434 0.132
loc_remove 1.0 0.435 0.13
nfunctions 1.0 0.213
bug 1.0
Kendalls t correlation analysis (Project K, all revisions)
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 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.
References
Data Quality As visualized in Fig. 1, the models have been developed using Matlab/Simulink and were commited to our repository system “PTC Integrity”. Using “dSpace TargetLink” the C-source code has been generated and commited to the repository too. Bugs have been filed at every development and testing stage. Restrictive commit policies ensure the link between every issue ticket and the coreseponding bug fix commit.
Get the Audi Dataset:
http://www.ist.tugraz.at/_attach/Publish/
AltingerHarald/MSR_2015_dataset_automotive.zip
Dataset Creation Workflow