Monday, March 27, 2023, 2:00pm - 5:30pm
For questions or inquiries about the ACM SAC tutorials, please contact the Tutorials Chair(s):
Tallinn University, Estonia
Digital interventions for behavior change (DBCIs) can help people to form/alter behaviors using technologies such as mobile applications, wearable technology, games etc. Designing DBCIs requires a multidisciplinary approach. First, it requires understanding behavior change theories to understand what to change and how to change behaviors, usually guided by intervention design. Second, it is essential to understand users' context, needs, and expectations from the technology. Interaction design helps to design the user's interaction with the product according to the user's context and needs to achieve goals. Currently, intervention and interaction design are separate bodies of knowledge and are not combined as needed for designing DBCIs. It is unclear how to optimally integrate behavior change theory within the digital design of the DBCIs, and design according to the user's context and needs. This tutorial will include a brief introduction to intervention and interaction design, the potential limitations of current practices of designing DBCIs, and an approach and guidelines that can help combine intervention and interaction design. The presentation will conclude with a discussion of the design-related challenges of DBCIs and the research scope for future work.
Farhat-ul-Ain holds a master's in health psychology. She is interested in digital health interventions for behavior change due to their potential to intervene in patients' natural settings, continuously monitor patients' health status, and make better decisions. Currently, her doctoral work is focused on developing guidelines to combine design-based research methods and interaction design methodologies with existing knowledge of behavior change. Farhat has been delivering courses related to designing for behavior change for the previous two years.
Alexander Bakhtin, and Prof. Davide Taibi
The University of Oulu
It is well-recognized that design patterns improve system development and maintenance in many aspects. While we commonly recognize these patterns in monolithic systems, many patterns emerged for cloud computing, specifically microservices. Unfortunately, while various patterns have been proposed, available quality assessment tools often do not recognize many. This tutorial presents the current catalog of available tools to detect microservice API patterns. It reasons about mechanisms that can be used to detect these patterns. Furthermore, our findings indicate gaps and opportunities for improvements in quality assessment tools. There are available tools commonly used by practitioners that offer centralized logging, tracing, and metric collection for microservices. We assess the opportunity to combine current dynamic analysis tools with graph analysis and anomaly detection in the form of patterns and anti-patterns. We present a tool prototype that we use to assess a large microservice system benchmark demonstrating the feasibility and potential of such an approach.
Alexander Bakhtin received his Bachelor's degree in Engineering (Mathematics and Physics) in 2021 and Master's degree in Technology (Machine Learning) from Tampere University in Finland in May 2021 and December 2022, respectively. He is now starting his doctoral studies at the University of Oulu, Finland, under the supervision of prof. Davide Taibi. During his studies at Tampere University, Alexander worked in three research groups, where he applied Machine Learning methods to different problems
Sandeep K.S. Gupta, and Ayan Banerjee
Arizona State University (ASU),
Nikola Motor Company (NMC)
This tutorial aims at introducing the audience to the arising safety issues of AI-enabled cyber-physical systems (CPSs) and how is affects dependable and safe software development for real life deployments. It will introduce a new software development lifecycle that is geared towards assured certifiability while reducing data sharing between the CPS manufacturer and certifier. We will provide a landscape of informal and formal approaches in ensuring AI-based CPS safety at every phase of the software development lifecycle, defining the gaps, current research to fill those gaps, and tools for detection of commonly occurring software failures such as doping. This tutorial also aims at emphasizing the need for operational safety of AI-based CPS and highlight the importance of explainability at every stage for enhancing trustworthiness. There has been significant research in the domain of model-based engineering that are attempting to solve this design problem. Observations from the deployment of a CPS are used to: a) ascertain whether the CPS used in practice match the proposed safety assured design, b) explain reasons for a mismatch in CPS operation and the safety assured design, c) generate evidence to establish the trustworthiness of a CPS, d) generate novel practical scenarios where a CPS is likely to fail.
Sandeep K. S. Gupta is the Associate Dean for Research in Fulton School of Engineering and a Professor of Computer Science and Engineering in the School for Computing and Augmented Intelligence (SCAI), ASU Tempe, AZ. Dr. Gupta heads the IMPACT Lab (http://impact.lab.asu.edu). IMPACT Lab has significant experience in hosting tutorials. Previously we have hosted tutorials at the Body Sensor Network conference, and at the Food and Drug Administration (FDA) on safe mobile medical control systems.
Ayan Banerjee is an Assistant Research Professor at ASU. His research interest lies in safe, secure and sustainable AI enabled CPS. His expertise include model based analysis and design of CPS, implementation of CPS with embedded computing, and applications of wearable sensor based control systems in domains such as medical control systems, or gesture recognition.
Imane Lamrani is a ADAS engineer at NMC. She received a PhD in Computer science from ASU. Her research goal is to develop rigorous
Tomas Cerny and and his research team members (Patrick Harris, Mia Gortney and Amr El-Sayed)
As microservices become more popular, more drawbacks become apparent to developers. One issue that many teams face today is the failure to visualize an entire microservice system architecture holistically. Without a full view, the architecture can become convoluted as teams add and subtract from their system without reconciling their changes. On top of this, a software system can degrade over time through the introduction of technical debt. This degradation can be combated by reconstructing a view of the system using information gathered through static or dynamic analysis. This tutorial will discuss, in detail, methodologies to reconstruct and visualize a microservice-based system and present a prototype capable of providing interactive two-dimensional and three-dimensional visualizations from data gathered through the static analysis of a codebase.
Tomas Cerny is a Professor of Computer Science at Baylor University. His area of research is software engineering, cloud systems, and code analysis. He received his Master's, and Ph.D. degrees from the Faculty of Electrical Engineering at the Czech Technical University in Prague, and an M.S. degree from Baylor University. He authored over 100 publications, mostly related to code analysis and enterprise systems. Among his awards are best papers at Microservices 2022, IEEE SOSE 2022, Closer 2022, LXNLP 2022, the Outstanding Service Award ACM SIGAPP 2018 and 2015, or the 2011 ICPC Joseph S. DeBlasi Outstanding Contribution Award. He served on the committee of multiple conferences in the past few years, including program or conference chairs at ACM SAC, ACM RACS, and ICITCS.