3 research outputs found
Cost-effective Simulation-based Test Selection in Self-driving Cars Software
Simulation environments are essential for the continuous development of
complex cyber-physical systems such as self-driving cars (SDCs). Previous
results on simulation-based testing for SDCs have shown that many automatically
generated tests do not strongly contribute to identification of SDC faults,
hence do not contribute towards increasing the quality of SDCs. Because running
such "uninformative" tests generally leads to a waste of computational
resources and a drastic increase in the testing cost of SDCs, testers should
avoid them. However, identifying "uninformative" tests before running them
remains an open challenge. Hence, this paper proposes SDCScissor, a framework
that leverages Machine Learning (ML) to identify SDC tests that are unlikely to
detect faults in the SDC software under test, thus enabling testers to skip
their execution and drastically increase the cost-effectiveness of
simulation-based testing of SDCs software. Our evaluation concerning the usage
of six ML models on two large datasets characterized by 22'652 tests showed
that SDC-Scissor achieved a classification F1-score up to 96%. Moreover, our
results show that SDC-Scissor outperformed a randomized baseline in identifying
more failing tests per time unit.
Webpage & Video: https://github.com/ChristianBirchler/sdc-scisso
AI-enabled Automation for Completeness Checking of Privacy Policies
Technological advances in information sharing have raised concerns about data
protection. Privacy policies contain privacy-related requirements about how the
personal data of individuals will be handled by an organization or a software
system (e.g., a web service or an app). In Europe, privacy policies are subject
to compliance with the General Data Protection Regulation (GDPR). A
prerequisite for GDPR compliance checking is to verify whether the content of a
privacy policy is complete according to the provisions of GDPR. Incomplete
privacy policies might result in large fines on violating organization as well
as incomplete privacy-related software specifications. Manual completeness
checking is both time-consuming and error-prone. In this paper, we propose
AI-based automation for the completeness checking of privacy policies. Through
systematic qualitative methods, we first build two artifacts to characterize
the privacy-related provisions of GDPR, namely a conceptual model and a set of
completeness criteria. Then, we develop an automated solution on top of these
artifacts by leveraging a combination of natural language processing and
supervised machine learning. Specifically, we identify the GDPR-relevant
information content in privacy policies and subsequently check them against the
completeness criteria. To evaluate our approach, we collected 234 real privacy
policies from the fund industry. Over a set of 48 unseen privacy policies, our
approach detected 300 of the total of 334 violations of some completeness
criteria correctly, while producing 23 false positives. The approach thus has a
precision of 92.9% and recall of 89.8%. Compared to a baseline that applies
keyword search only, our approach results in an improvement of 24.5% in
precision and 38% in recall