TY - GEN
T1 - Novel insights on cross project fault prediction applied to automotive software
AU - Altinger, Harald
AU - Herbold, Steffen
AU - Grabowski, Jens
AU - Wotawa, Franz
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Defect prediction is a powerful tool that greatly helps focusing quality assurance efforts during development. In the case of the availability of fault data from a particular context, there are different ways of using such fault predictions in practice. Companies like Google, Bell Labs and Cisco make use of fault prediction, whereas its use within auto- motive industry has not yet gained a lot of attraction, although, modern cars require a huge amount of software to operate. In this paper, we want to contribute the adoption of fault prediction techniques for automotive software projects. Hereby we rely on a publicly available data set comprising fault data from three automotive software projects. When learning a fault prediction model from the data of one particular project, we achieve a remarkably high and nearly perfect prediction performance for the same project. However, when applying a cross-project prediction we obtain rather poor results. These results are rather surprising, because of the fact that the underlying projects are as similar as two distinct projects can possibly be within a certain application context. Therefore we investigate the reasons behind this observation through correlation and factor analyses techniques. We further report the obtained findings and discuss the consequences for future applications of Cross-Project Fault Prediction (CPFP) in the domain of automotive software.
AB - Defect prediction is a powerful tool that greatly helps focusing quality assurance efforts during development. In the case of the availability of fault data from a particular context, there are different ways of using such fault predictions in practice. Companies like Google, Bell Labs and Cisco make use of fault prediction, whereas its use within auto- motive industry has not yet gained a lot of attraction, although, modern cars require a huge amount of software to operate. In this paper, we want to contribute the adoption of fault prediction techniques for automotive software projects. Hereby we rely on a publicly available data set comprising fault data from three automotive software projects. When learning a fault prediction model from the data of one particular project, we achieve a remarkably high and nearly perfect prediction performance for the same project. However, when applying a cross-project prediction we obtain rather poor results. These results are rather surprising, because of the fact that the underlying projects are as similar as two distinct projects can possibly be within a certain application context. Therefore we investigate the reasons behind this observation through correlation and factor analyses techniques. We further report the obtained findings and discuss the consequences for future applications of Cross-Project Fault Prediction (CPFP) in the domain of automotive software.
KW - Automotive
KW - Cross project fault prediction
KW - Principal component analysis
KW - Project fault prediction
UR - http://www.scopus.com/inward/record.url?scp=84952777603&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-25945-1_9
DO - 10.1007/978-3-319-25945-1_9
M3 - Conference paper
AN - SCOPUS:84952777603
SN - 9783319259444
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 141
EP - 157
BT - Testing Software and Systems - 27th IFIP WG 6.1 International Conference, ICTSS 2015, Proceedings
A2 - Yevtushenko, Nina
A2 - El-Fakih, Khaled
A2 - Barlas, Gerassimos
PB - Springer-Verlag Italia
T2 - 27th IFIP WG 6.1 International Conference on Testing Software and Systems
Y2 - 23 November 2015 through 25 November 2015
ER -