Doppelganger Test Generation for Revealing Bugs in Autonomous Driving Software
Vehicles controlled by autonomous driving software (ADS) are expected to bring many social and economic benefits, but at the current stage not being broadly used due to concerns with regard to their safety. Virtual tests, where autonomous vehicles are tested in software simulation, are common practices because they are more efficient and safer compared to field operational tests. Specifically, search-based approaches are used to find particularly critical situations. These approaches provide an opportunity to automatically generate tests; however, systematically producing bug-revealing tests for ADS remains a major challenge. To address this challenge, we introduce DoppelTest, a test generation approach for ADSes that utilizes a genetic algorithm to discover bug-revealing violations by generating scenarios with multiple autonomous vehicles that account for traffic control (e.g., traffic signals and stop signs). Our extensive evaluation shows that DoppelTest can efficiently discover 123 bug-revealing violations for a production-grade ADS (Baidu Apollo) which we then classify into 8 unique bug categories.
Fri 19 MayDisplayed time zone: Hobart change
15:45 - 17:15 | Cyber-physical systems testingSEIP - Software Engineering in Practice / Technical Track / Journal-First Papers at Meeting Room 106 Chair(s): Shahar Maoz Tel Aviv University | ||
15:45 15mTalk | Data-driven Mutation Analysis for Cyber-Physical Systems Journal-First Papers Enrico Viganò University of Luxembourg, Oscar Cornejo SnT Centre, University of Luxembourg, Fabrizio Pastore University of Luxembourg, Lionel Briand University of Luxembourg; University of Ottawa Link to publication Pre-print | ||
16:00 15mTalk | Finding Causally Different Tests for an Industrial Control System Technical Track Chris Poskitt Singapore Management University, Yuqi Chen ShanghaiTech University, China, Jun Sun Singapore Management University, Yu Jiang Tsinghua University DOI Pre-print File Attached | ||
16:15 15mTalk | Doppelganger Test Generation for Revealing Bugs in Autonomous Driving Software Technical Track Yuqi Huai University of California, Irvine, Yuntianyi Chen University of California, Irvine, Sumaya Almanee University of California, Irvine, Tuan Ngo VNU University of Engineering and Technology, Xiang Liao University of California, Irvine, Ziwen Wan University of California, Irvine, Qi Alfred Chen University of California, Irvine, Joshua Garcia University of California, Irvine Pre-print | ||
16:30 15mTalk | Generating Realistic and Diverse Tests for LiDAR-Based Perception Systems Technical Track Garrett Christian University of Virginia, Trey Woodlief University of Virginia, Sebastian Elbaum University of Virginia Pre-print | ||
16:45 15mTalk | Automated Test Case Generation for Safety-Critical Software in Scade SEIP - Software Engineering in Practice Elson Kurian University of Milano Bicocca, Pietro Braione University of Milano-Bicocca, Daniela Briola University of Milano Bicocca, Dario D'Avino , Matteo Modonato , Giovanni Denaro University of Milano-Bicocca, Italy | ||
17:00 7mTalk | Single and Multi-objective Test Cases Prioritization for Self-driving Cars in Virtual Environments Journal-First Papers Christian Birchler Zurich University of Applied Sciences, Sajad Khatiri USI-Lugnao & Zurich University of Applied Sciences, Pouria Derakhshanfar JetBrains Research, Sebastiano Panichella Zurich University of Applied Sciences, Annibale Panichella Delft University of Technology | ||
17:07 7mTalk | Parameter Coverage for Testing of Autonomous Driving Systems Under Uncertainty Journal-First Papers Thomas Laurent JSPS@National Institute of Informatics, Japan, Stefan Klikovits Johannes Kepler University, Linz, Paolo Arcaini National Institute of Informatics
, Fuyuki Ishikawa National Institute of Informatics, Anthony Ventresque Trinity College Dublin & Lero, Ireland Link to publication DOI | ||