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Nianyu Li won best student paper award of SEAMS 2021

Date: 2021-05-22   Click:

16th International Symposium on Software Engineering for Adaptive and Self-Managing Systems was hold in May 18-21, 2021 online. Nianayu Li, a PhD student, has a first-authored paper accepted and won the best paper award. This paper is entitled as "Hey! Preparing Humans to do Tasks in Self-adaptive Systems". This is a collaborative work between Prof. Zhi Jin’s team in PKU-SEI and Prof. David Garlan’s team in CMU-SEI and is co-authored by Nianyu Li, Javier Camara, David Garlan, Bradley Schmerl and Zhi Jin.

The main idea of this work is as follows. Many self-adaptive systems benefit from human involvement, where human operators can complement the capabilities of systems (e.g., by supervising decisions, or performing adaptations and tasks involving physical changes that cannot be automated). However, insufficient preparation (e.g., lack of task context comprehension) may hinder the effectiveness of human involvement, especially when operators are unexpectedly interrupted to perform a new task. Preparatory notification of a task provided in advance can sometimes help human operators focus their attention on the forthcoming task and understand its context before task execution, hence improving effectiveness. Nevertheless, deciding when to use preparatory notification as a tactic is not obvious and entails considering different factors that include uncertainties induced by human operator behavior (who might ignore the notice message), human attributes (e.g., operator training level), and other information that refers to the state of the system and its environment. In this paper, informed by work in cognitive science on human attention and context management, we introduce a formal framework to reason about the usage of preparatory notifications in self-adaptive systems involving human operators. Our framework characterizes the effects of managing attention via task notification in terms of task context comprehension. We also build on our framework to develop an automated probabilistic reasoning technique able to determine when and in what form a preparatory notification tactic should be used to optimize system goals. We illustrate our approach in a representative scenario of human-robot collaborative goods delivery.

Modern and emerging software systems, such as industrial Internet of Things, Cyber-Physical Systems, cloud and edge computing, robotics, and smart environments have to operate without interruption. Self-adaptation and self-management enable these systems to adapt themselves at runtime to preserve and optimize their operation in the presence of uncertain changes in their operating environment, resource variability, new user needs, attacks, intrusions, and faults. Approaches to complement software-based systems with self-managing and self-adaptive capabilities are an important area of research and development, offering solutions that leverage advances in fields such as software architecture, fault-tolerant computing, programming languages, run-time program analysis and verification, among others. Additionally, research in this field is informed by related areas such as control systems, machine learning, artificial intelligence, agent-based systems, and biologically inspired computing. The SEAMS symposium focuses on applying software engineering to these approaches, including methods, techniques, processes and tools that can be used to support self-* properties like self-protection, self-healing, self-optimization, and self-configuration.


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