Mon 15 AprDisplayed time zone: Lisbon change
09:00 - 10:30 | |||
09:00 30mTalk | SEAMS 2024 Opening Research Track | ||
09:30 60mKeynote | Keynote: Advances on Symbolic Machine Learning and Recent Applications to Software Engineering Research Track Alessandra Russo Imperial College London | ||
11:00 - 12:30 | Session 2: UncertaintyResearch Track at Luis de Freitas Branco Chair(s): Ivana Dusparic Trinity College Dublin, Ireland | ||
11:00 25mTalk | Formal Synthesis of Uncertainty Reduction ControllersFULL Research Track Marc Carwehl Humboldt-Universität zu Berlin, Calum Imrie University of York, Thomas Vogel Humboldt-Universtität zu Berlin, Genaína Nunes Rodrigues University of Brasília, Radu Calinescu University of York, UK, Lars Grunske Humboldt-Universität zu Berlin | ||
11:25 25mTalk | Automated Planning for Adaptive Cyber-Physical Systems under Uncertainty in Temporal Availability ConstraintsFULL Research Track Raquel Sanchez University of Malaga, Javier Troya Universidad de Málaga, Spain, Javier Camara University of Málaga | ||
11:50 25mTalk | Handling uncertainty in the specification of autonomous multi-robot systems through mission adaptationFULL Research Track Gianluca Filippone University of L'Aquila, Italy, Juan Antonio Piñera García Gran Sasso Science Institute, Marco Autili University of L'Aquila, Italy, Patrizio Pelliccione Gran Sasso Science Institute, L'Aquila, Italy | ||
12:15 15mTalk | Uncertainty Flow Diagrams: Towards a Systematic Representation of Uncertainty Propagation and Interaction in Adaptive SystemsSHORT Research Track Javier Camara University of Málaga, Sebastian Hahner Karlsruhe Institute of Technology (KIT), Diego Perez-Palacin Linnaeus University, Antonio Vallecillo University of Málaga, Spain, Maribel Acosta Technical University of Munich, Nelly Bencomo Durham University, Radu Calinescu University of York, UK, Simos Gerasimou University of York | ||
Tue 16 AprDisplayed time zone: Lisbon change
09:00 - 10:30 | |||
09:00 60mKeynote | Keynote: Towards Always Law-Abiding Self-Driving Research Track Jun Sun Singapore Management University | ||
10:00 20mAwards | Most Influential Paper Award 2014 Research Track | ||
10:20 10mAwards | SEAMS Best Paper and Artifact Awards Research Track | ||
11:00 - 12:30 | Session 6: Self-Recovery & Evaluation Research Track / Artifact Track at Luis de Freitas Branco Chair(s): Dalal Alrajeh Imperial College London | ||
11:00 25mTalk | Raft Protocol for Fault Tolerance and Self-Recovery in Federated LearningFULL Research Track | ||
11:25 25mTalk | Integrating Graceful Degradation and Recovery through Requirement-driven AdaptationFULL Research Track Simon Chu Carnegie Mellon University, Justin Koe The Cooper Union, David Garlan Carnegie Mellon University, Eunsuk Kang Carnegie Mellon University | ||
11:50 25mTalk | Learning Recovery Strategies for Dynamic Self-healing in Reactive SystemsFULL Research Track Mateo Sanabria Universidad de los Andes, Ivana Dusparic Trinity College Dublin, Ireland, Nicolás Cardozo Universidad de los Andes Pre-print | ||
12:15 15mTalk | SWITCH: An Exemplar for Evaluating Self-Adaptive ML-Enabled SystemsARTIFACT Artifact Track Pre-print Media Attached | ||
Keynote Title: Advances on Symbolic Machine Learning and Recent Applications to Software Engineering
Abstract: Learning interpretable models from data is one of the main challenges of AI. Symbolic Machine Learning, a field of Machine Learning, offers algorithms and systems for learning models that explain data in the context of a given domain knowledge. In contrast to statistical learning, models learned by Symbolic Machine Learning are interpretable: they can be translated into natural language and understood by humans. In this talk, I will overview our state-of-the-art symbolic machine learning system (ILASP) capable of learning different classes of models, (e.g., non-monotonic, non-deterministic and preference-based) for real-world problems, in a manner that is data efficient, scalable, and robust to noise. I will show how such system can be integrated with statistical and deep learning to provide neuro-symbolic AI solutions for learning complex interpretable knowledge from unstructured data. I will then illustrate how these advances can be applied to areas such agent learning, run-time adaptation of security for unmanned arial vehicles, and online learning of policies for explainable security.
Bio: Alessandra Russo is a Professor on Applied Computational Logic, at the Department of Computing, Imperial College London, Deputy director of the UKRI Centre for Doctoral Training in “Safe and Trusted AI”, and promoter of the Imperial-X inter-disciplinary research initiative “Intelligible AI” on explainable, safe and trustworthy AI. She leads the “Structured and Probabilistic Intelligent Knowledge Engineering (SPIKE)” research group at the Department of Computing. She has pioneered several state-of-the-art symbolic machine learning systems, Including the recent state-of-the-art LAS (Learning from Answer Sets) system for learning interpretable knowledge from labelled data. More recently she has explored novel methodologies for neuro-symbolic learning that integrate machine learning and probabilistic inference with symbolic learning to support generalisation and transfer learning from multimodal unstructured data. She has published over 200 articles in flagship conferences and high impact journals in Artificial Intelligence and Software Engineering, and led various projects funded by the EPSRC, the EU and Industry.
Title: Towards Always Law-Abiding Self-Driving
Abstract: How should an autonomous vehicle behave on the road besides causing no accidents and reaching the destination? Fortunately, rich sets of criteria for how a vehicle should undertake a journey already exist: the various national traffic laws. In addition to avoiding collisions, an autonomous vehicle should satisfy the traffic laws of the country it operates in. Until we design new traffic laws specifically for autonomous vehicles, existing traffic laws remain the gold standard for ensuring road safety. The question is then: how do we systematically make sure that an autonomous vehicle almost always abides the traffic laws? In this work, I will introduce our recent effort on formalizing national traffic laws and use it to adaptively enforce desirable self-driving automatically.
Bio: Sun, Jun is currently a professor at Singapore Management University (SMU). He received Bachelor and PhD degrees in computing science from National University of Singapore (NUS) in 2002 and 2006. He has been a faculty member since 2010. He was a visiting scholar at MIT from 2011-2012. Jun’s research interests include AI safety, formal methods, program analysis and cyber-security. He is the co-founder of the PAT model checker. He has published many journal articles or peer-reviewed conference papers, many of which are published at top-tier venues. He serves as the technical consultant for multiple companies.
Accepted Papers
Call for Artifacts
SEAMS continues to encourage its community members to build dedicated artifacts that support driving, communicating, comparing, and evaluating their research on software engineering for adaptive and self-managing systems. In this spirit, the SEAMS 2024 artifacts track exists to review, promote, share, and catalog research artifacts that bring value to the community.
NOTE: While this call is about dedicated artifacts, we also encourage authors to submit artifacts used for their long research papers to SEAMS 2024. If authors have submitted their long research papers to SEAMS 2024, they only need to submit their artifact and a 2 pages (max) abstract in PDF format describing the artifact. Of course, these two submissions are independent, meaning the artifact can also have its own separate artifact paper. If both are accepted, the badges will be awarded to the research paper.
Types of Artifacts
According to ACM’s "Artifact Review and Badging (Version 1.1)” policy, an artifact is “a digital object that was either created by the authors to be used as part of the study or generated by the experiment itself. For example, artifacts can be software systems, scripts used to run experiments, input datasets, raw data collected in the experiment, or scripts used to analyze results.” A formal review of such artifacts promotes artifacts of high quality that promotes reproducibility and replicability of research results and that drive the research of the whole SEAMS community. Hence, artifacts of interest for SEAMS include but are not limited to:
- Testbeds / Exemplars, which are implementations or detailed specifications of systems that pose and highlight fundamental or characteristic challenges in this community, and that self-adaptive systems should address.
- Datasets, which are data (e.g., logging data, sensor data, system traces, survey raw data) that can be used to develop, evaluate, and compare self-adaptation approaches.
- Frameworks, which are tools and services illustrating and implementing self-adaptation techniques or algorithms that are potentially useful in different contexts and that other researchers could use and customize to specific contexts.
- Thus, frameworks primarily support developing self-adaptation approaches whereas testbeds / exemplars and datasets particularly support evaluating and comparing different approaches. However, this list is not exhaustive. If your proposed artifact is not on this list, please email the Artifacts Chairs before submitting.
Quality of Artifacts
According to the ACM’s "Artifact Review and Badging (Version 1.1)” policy, SEAMS aims for artifacts that are available and reusable so that other researchers can access and built upon the artifacts to drive research in the overall SEAMS community. Thus, artifacts have to be made permanently available—latest after acceptance and before publication of the artifact—using publisher repositories (ACM or IEEE), institutional repositories, or open (commercial) repositories (e.g., figshare, Zenodo, or GitHub/Zenodo) that provide permanent and unique identifiers for the artifacts. Personal web pages are not acceptable for this purpose. Moreover, artifacts should be reusable by other researchers. To facilitate reuse, they should be carefully documented, well-structured, complete, exercisable, as well as appropriately verified and validated (e.g., use of the artifact in a study).
Review Process and Selection Criteria
All submitted artifacts will be reviewed by at least three members of the program committee. Each artifact will be evaluated in relation to the expectations set by the artifact paper. In addition to just running the artifact, the evaluators will read the paper and may try to tweak provided inputs and create new ones, to test the limits of the system.
Artifacts will be evaluated using the following criteria:
- Community value: Does the artifact bring value to the SEAMS community? Is this value clearly explained in the paper? Can the artifact be readily used by other researchers?
- Insightfulness: Does the artifact address or identify a gap in previous work?
- Timeliness: Does the artifact address a problem that is timely? Usability: Is it complete and easy to understand? Is it carefully documented and well-structured? Is it accompanied by tutorial notes? and other documentation? Is it exercisable? If the artifact is executable, is it easy to download, install, and execute? Has it been validated? Is it functional? Is it reusable?
Submission
SUBMISSION LINK: Abstracts and papers must be submitted via HotCRP: https://seams24artifacts.hotcrp.com
Submission includes an artifact paper and the artifact itself. The paper should be submitted via HotCRP (see above). The paper should provide a link from where the reviewers can access and download the artifact (it does not need to be a link to the permanent repository at this stage). Artifacts must not have been previously published or concurrently submitted elsewhere.
Artifact Paper
An artifact paper is of max 6 pages + 1 page references. It should include a synopsis or description of the problem that is being addressed, a description of the context(s) in which the artifact would be useful, a list of the challenges that it poses for self-adaptation, the link to the artifact, and examples of its use in at least one area of self-adaptive systems. Accepted artifact papers will be included in the proceedings, and authors will be given an opportunity to present their artifact at SEAMS 2024.
Artifact papers must conform to the formatting instructions and template of the SEAMS Research Track.
The SEAMS 2024 Artifact Track will use a lightweight double-blind review process. No submission may reveal its authors’ identities. In particular:
- Authors’ names must be omitted from the submission.
- All references to the author’s prior work should be in the third person.
- While authors have the right to upload preprints on ArXiV or similar sites or repositories, they must avoid specifying that the manuscript was submitted to SEAMS 2024.
- During review, authors should not publicly use the submission title. They should thus use a different artifact title for any pre-print in arxiv, repository or similar websites.
- Additional material published online should be anonymized and should not provide references to the paper’s authors.
There will be a best artifact award recognizing the work of authors who contribute the most useful artifact to the community. Moreover, we will do our best to work with the IEEE Xplore and ACM Portal administrator to add badges to the electronic versions of the authors’ paper(s).
Artifact
Authors must perform the following steps to submit an artifact:
Packaging the Artifact
When packaging your artifact, it is important to keep in mind: a) how accessible the artifact is to other researchers, and b) the fact that the artifact evaluators have very limited time to assess each artifact. The setup for your artifact should take less than 30 minutes or it is unlikely to be endorsed simply because the committee will not have sufficient time to evaluate it. If you envision difficulties, please provide your artifact with a working environment in a VirtualBox VM image or a Docker container image so that the artifact can be run and exercised. Otherwise, the artifact can be packaged in a single archive file (zip or tar.gz) or its code base can be provided by a public repository. In either case, the artifact should be exercisable and appropriately validated.
Documenting the Artifact
To facilitate reuse, an artifact should be complete and carefully documented. Therefore, it must:
- be self-contained, that is, it contains the artifact itself, which may include source code, executables, data, a virtual machine image, documents and other content deemed relevant by the authors. Please use open formats for documents (e.g., csv for data). Publicly available external tools or libraries used to exercise and use the artifact do not have to be included in the artifact;
- include documentation that describes the artifact. It is acceptable to refer to the artifact paper for a more detailed description of the artifact. The entry point to the documentation should be easy to identify (e.g., README.md or index.html file in the top-level directory of the artifact). The documentation should contain:
- a Getting Started section that should stress the key elements of your artifact and that should enable the reviewers to run, execute or analyze your artifact without any technical difficulty. In this context, requirements and side effects of running the artifact should be discussed.
- step-by-step instructions on how to download, install, run, and “play” the artifact, and how to check the results of the execution. These instructions should both show how you propose to evaluate your artifact, and be useful for a new user of your artifact to get started.
- where appropriate, descriptions of and links to files (included in the archive or generated by executing the artifact) that represent expected outputs (e.g., the log files expected to be generated by the artifact on the given inputs).
- if applicable, descriptions of how the artifact can be customized and extended to be reused in a different research context of self-adaptive systems.
- include a license file or statement describing the distribution rights. Note that a reusable artifact requires some kind of open source license.
- Optionally, the authors are encouraged to include in the documentation a link to a short video (YouTube, max. 5 minutes) demonstrating the artifact.
Making the Artifact Available
Regardless of packaging, the artifact should be made available to the artifact reviewers through a link to a public repository (e.g., GitHub) or to a single archive file.
Artifacts accepted to the SEAMS 2024 program have to be made permanently available to the public by the time the camera ready version of the paper is due. This must be done using an archival repository such as publisher repositories at ACM or IEEE, institutional repositories, or open (commercial) repositories such as figshare and Zenodo. For instance, Zenodo supports archiving snapshots of GitHub repositories and provides DOIs for such snapshots.
| Sun 28 Jan 2024 Camera Ready |
| Fri 12 Jan 2024 Notification |
| Fri 15 Dec 2023 Artifact Submission |
| Fri 8 Dec 2023 Abstract Submission |
Shanshan LiArtifact Evaluation Co-Chair
National University of Defense Technology
China
Christos TsigkanosArtifact Evaluation Co-Chair
University of Bern, Switzerland
Marc Carwehl
Humboldt-Universität zu Berlin
Germany
Sandra Greiner
University of Bern, Switzerland
Switzerland
Axel Klarmann
Leipzig University of Applied Sciences
Germany
NIANYU LI
ZGC Lab, China
China
Yan Lei
Chongqing University
China
Jialong Li
Waseda University, Japan
Japan
Jie Liang
Tsinghua University
China
Jiakun Liu
Singapore Management University
Anastasia Mavridou
KBR / NASA Ames Research Center
Colin Paterson
University of York
Stefano Sebastio
Collins Aerospac
Ireland
Li Tsz On
The Hong Kong University of Science and Technology
China
Ying Wang
Northeastern University
China
Rebekka Wohlrab
Chalmers University of Technology
Sweden
Tao Zhang
Macau University of Science and Technology
China
Yuanliang Zhang
National University of Defense Technology