SEAMS 2020
Mon 29 June - Fri 3 July 2020
co-located with ICSE 2020

Recent approaches in testing autonomous driving systems (ADS) are able to generate a scenario in which the autonomous car collides , and a different ADS configuration that avoids the collision. However, such test information is too low level to be used by engineers to improve the ADS. In this paper, we consider a path planner component provided by our industry partner, that can be configured through some weights. We propose a technique to automatically re-engineer the path planner in terms of a self-adaptive path planner (SAPP) following the MAPE loop reference architecture. The Knowledge Base (KB) of SAPP contains descriptions of collision scenarios discovered with testing, and the corresponding alternative weights that avoid the collisions. We forecast two main usages of SAPP. First of all, designers are provided with a prototype that should facilitate the re-implementation of the path planner. As second usage, SAPP can be useful for improving the diversity of testing, as performing test case generation on SAPP will guarantee to find dangerous situations different from those used to build SAPP. Preliminary experiments indicate that SAPP can effectively adapt on the base of the solutions stored in KB.

Tue 30 Jun

Displayed time zone: (UTC) Coordinated Universal Time change

06:00 - 07:30
Session 2: Testing, Analysis, Reasoning, and MonitoringSEAMS 2020 at SEAMS
Chair(s): Sona Ghahremani Hasso Plattner Institute, University of Potsdam
06:00
5m
Talk
Leveraging Test Logs for Building a Self-Adaptive Path PlannerNIER
SEAMS 2020
Kun Liu Peking University, China, Xiao-Yi Zhang National Institute of Informatics, Japan, Paolo Arcaini National Institute of Informatics , Fuyuki Ishikawa National Institute of Informatics, Wenpin Jiao Peking University, China
Pre-print Media Attached
06:05
5m
Talk
Supporting Viewpoints to Review the Lack of Requirements in Space Systems with Machine LearningExperience
SEAMS 2020
Kenji Mori Japan Aerospace Exploration Agency, Japan, Naoko Okubo Japan Aerospace Exploration Agency, Japan, Yasushi Ueda Japan Aerospace Exploration Agency, Japan, Masafumi Katahira Japan Aerospace Exploration Agency, Toshiyuki Amagasa University of Tsukuba, Japan
Media Attached
06:10
5m
Talk
DATESSO: Self-Adapting Service Composition with Debt-Aware Two Levels Constraint ReasoningTechnicalBest Student Paper
SEAMS 2020
Satish Kumar University of Birmingham, United Kingdom, Tao Chen Loughborough University, Rami Bahsoon University of Birmingham, Rajkumar Buyya University of Melbourne, Australia
DOI Pre-print Media Attached
06:15
5m
Talk
Towards Bridging the Gap between Control and Self-Adaptive System PropertiesNIER
SEAMS 2020
Javier Camara University of York, Alessandro Vittorio Papadopoulos Mälardalen University, Thomas Vogel Humboldt-Universität zu Berlin, Danny Weyns KU Leuven, David Garlan Carnegie Mellon University, Shihong Huang Florida Atlantic University, Kenji Tei Waseda University / National Institute of Informatics, Japan
DOI Pre-print Media Attached
06:20
5m
Talk
Explanation for Human-on-the-loop: a probabilistic model checking approachNIER
SEAMS 2020
NIANYU LI Peking University, China, Sridhar Adepu Singapore University of Technology and Design, Singapore, Eunsuk Kang Carnegie Mellon University, David Garlan Carnegie Mellon University
Pre-print Media Attached
06:25
65m
Other
Q&A and Discussion (Session 2)
SEAMS 2020