Abstract
Autonomous cars have become increasingly popular in the last decade
because of their numerous benefits, such as lower travel time, increased
safety, and improved fuel economy. Many car manufacturing companies and
tech giants are working on this technology to make fully autonomous
automobiles or strengthen their existing driver-less cars. These cars
use very complex, advanced, and sophisticated hardware technologies.
However, the software is an equally important feature because it must
operate all functions seamlessly while working in sync with other
vehicle components. The software must analyze a large amount of data to
make quick real-time decisions, so any vulnerabilities or bugs can be a
severe problem to the vehicle and the passengers riding in it. Many
researchers have proposed various software defect prediction schemes for
different projects and applications, but most of them have focused on
specific software issues and excluded others. Thus, their methods cannot
be applied to the software of autonomous cars. In this paper, we propose
an improved Artificial Neural Network (ANN) model, called
Dropout-Artificial Neural Network (D-ANN), to solve this problem of
defect prediction in autonomous cars. This inclusive model can consider
all the parameters simultaneously for effective bugs prediction. The
proposed model can be used for the software of any autonomous cars, and
it is trained and evaluated using standard methods. The results obtained
show that the proposed model predicts software defects with higher
accuracy than other models.