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

Two of the main paradigms used to build adaptive software employ different types of properties to capture relevant aspects of the system’s run-time behavior. On the one hand, control systems consider properties that concern static aspects like stability, as well as dynamic properties that capture the transient evolution of variables such as settling time. On the other hand, self-adaptive systems consider mostly non-functional properties that capture concerns such as performance, reliability, and cost. In general, it is not easy to reconcile these two types of properties or identify under which conditions they constitute a good fit to provide run-time guarantees. There is a need of identifying the key properties in the areas of control and self-adaptation, as well as of characterizing and mapping them to better understand how they relate and possibly complement each other. In this paper, we take a first step to tackle this problem by: (1) identifying a set of key properties in control theory, (2) illustrating the formalization of some of these properties employing temporal logic languages commonly used to engineer self-adaptive software systems, and (3) illustrating how to map key properties that characterize self-adaptive software systems into control properties, leveraging their formalization in temporal logics. We illustrate the different steps of the mapping on an exemplar case in the cloud computing domain and conclude with identifying open challenges in the area.

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