The 1st IEEE International Workshop on Quality of Learning-enabled Autonomous Systems

Call for Papers

The QuaLAS workshop aims to involve researchers and practitioners to exchange ideas and results on how to incorporate software engineering practices for quality assessment and improvement into the lifecycle of Learning-enabled Autonomous Systems (AS) in various applications domains (Autonomous Driving, Internet of Things, Web Services, Mobile Applications). One recent paradigm where such practices are required is MLOps, which fosters the collaboration between developers/data scientists and operations engineers in the lifecycle of AS.

Emerging standards (e.g., ISO/IEC DIS 5338, ISO/IEC DIS 25059) confirm the need for novel contributions concerning the AS lifecycle and related quality assessment and improvement techniques.

The objective of QuaLAS is to enable academia, industry, and practitioners to share their experiences in fundamental and applied research on the quality of Learning-enabled Autonomous Systems, and on the most challenging issues they currently face:

• Integrate software engineering practices into the Autonomous Systems lifecycle according to the MLOps paradigm.
• Measure Autonomous Systems quality (including reliability, safety, and security).
• Leverage monitored (unlabeled) data for quality assessment and improvement.
• Apply continuous quality assessment and/or improvement actions before and after the release in operation.
• Understand which actions can be automated and how.

Submissions

QuaLAS solicits submissions in two categories:

• Regular papers (up to 8 pages including references) should describe a novel contribution to the quality of learning-enabled autonomous systems. Novelty should be argued via experimental validation and appropriate positioning within the state of the art.
• Short papers (up to 5 pages including references) should describe novel ideas and reflections on the quality of learning-enabled autonomous systems. Contributions should be supported by preliminary experimental validation and/or well-argued motivations with concrete future plans.

Submissions must adhere to the IEEE Computer Society Format Guidelines as implemented by the following LaTeX/Word templates:

• LaTex Package (ZIP)
• Word Template (DOCX)

Papers must be written in English and submitted as a single Portable Document Format (PDF) file. All fonts must be embedded. Please take a note of the following:

• The first page must include the title of the paper and a maximum 200-word abstract.
• The first page is not a separate page, but is a part of the paper (i.e., it has technical material in it). Thus, this page counts toward the total page budget for the paper.
• The use of color for figures and graphs is allowed only if the paper turns out to be readable if printed in grayscale
• Symbols and labels used in the graphs should be readable as printed, without requiring on-screen magnification.

Accepted papers will be published in a supplemental volume of the ISSRE conference proceedings by the IEEE Computer Society, and will appear on IEEE Xplore.

Each accepted paper must have at least one author who registers for the ISSRE conference and gives the paper's workshop presentation. Papers must be submitted via Easychair at the following URL by selecting the 1st International Workshop on Quality of Learning-enabled Autonomous Systems: https://easychair.org/conferences/?conf=issre23.

Important Dates

Paper submission deadline: July 31, 2023 August 6, 2023

Paper notification: August 18, 2023

Camera ready papers: August 25, 2023

Topics of interest

Topics of interest concern quality assessment and improvement methods throughout the Learning-enabled Autonomous Systems (AS) lifecycle, including - but not limited - to:

• Quality attributes (e.g., performance, reliability, safety, security) impacting AS
• The role of Data Scientists, Machine Learning Engineers, and/or Domain Experts in quality assessment and improvement for AS
• Quality threats (e.g., the Oracle problem, incorrect design, unbalanced data)
• Quality of data (e.g., training, testing, operational data)
• Quality of the training process
• Quality of the AS models
• Quality assessment through testing
• Quality assessment through monitoring
• Quality prediction/forecasting
• Quality improvement of AS
• Automation of lifecycle steps (e.g., data processing, learning, testing, deployment, monitoring, and so on)
• Quality of AS for Image classification
• Quality of AS for Autonomous Driving
• Quality of AS for AIoT
• Operational context elicitation
• Solutions for green AS
• Social and ethical aspects