WAIN'21 - 1st Workshop on AI Engineering – Software Engineering for AI

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Welcome to WAIN’21 - 1st Workshop on AI Engineering – Software Engineering for AI

The aim of the workshop is to bring together researchers and practitioners in software engineering, in data-science and AI, and to build up a community that will target the new challenges emerging in Software Engineering that AI/data-science engineers and software engineers are facing in development of AI-based systems. The workshop will be highly interactive: In addition to the invited keynotes and short paper presentations, there will be several discussion sessions. We plan to combine local and remote participation.

Keynote: Lionel Briand, Trustworthy Machine Learning-Enabled Systems, Sun May 30

This talk will provide a personal perspective on the state of art regarding the automated testing and analysis of software systems enabled by machine learning. Such systems typically contain components relying on machine learning, whose behavior is not specified or coded but driven by training data, but which interact with other components in the system and play a critical role. Typical examples include cyber-physical systems that rely on machine learning in their perception (e.g., analyzing camera images) and control (e.g., sending commands to actuators) layers. In my reflections, I will rely both on my analysis of the state of the art and personal experience in research projects carried out with industrial partners in the automotive domain. Lionel C. Briand is professor of software engineering and has shared appointments between (1) School of Electrical Engineering and Computer Science, University of Ottawa, Canada and (2) The SnT centre for Security, Reliability, and Trust, University of Luxembourg. He is the head of the SVV department at the SnT Centre and a Canada Research Chair in Intelligent Software Dependability and Compliance (Tier 1). He holds an ERC Advanced Grant, the most prestigious European individual research award, and has conducted applied research in collaboration with industry for more than 25 years, including projects in the automotive, aerospace, manufacturing, financial, and energy domains. He is a fellow of the IEEE and ACM. He was also granted the IEEE Computer Society Harlan Mills award (2012) and the IEEE Reliability Society Engineer-of-the-year award (2013) for his work on model-based verification and testing. More details can be found on: http://www.lbriand.info.

Keynote: Errol Koolmeister, Engineering AI at H&M group, Mon May 31

Talk.H&M has invested heavily in AI the last few years and have gone from shattered decentralized projects into a large central effort focusing on amplifying all core operational decisions with AI. This effort requires them to rearchitect many of the core systems to support horizontal scaling. The keynote will go through the journey and share some of the key insights from this successful endeavor. As head of AI Foundation for H&M group, Errol Koolmeister is currently working on setting up and overseeing the AI projects in the group. Prior to H&M, Errol worked as a Director of Data Science for ThinkBig Analytics, a Teradata company where he was responsible for setting up and delivering on AI projects across the Nordics, Eastern Europe and Russia. Prior to that he was in London as a Lead Data Scientist for the Vodafone group, but he originally started his career in Nordea bank where he spent about 10 years in various analytics roles.

Schedule Overview

Accepted Papers

The accepted papers (full and short)

In development and implementation of AI-based systems , the main challenge is not to develop the best models/algorithms, but to provide support for the entire lifecycle – from a business idea, through collection and management of data, software development managing both data and code, product deployment and operation, and to its evolution. There is a clear need for specific support of Software Engineering for AI.

The aim of the workshop is to bring together researchers and practitioners in software engineering, in data-science and AI, and to build up a community that will target the new challenges emerging in Software Engineering that AI/data-science engineers and software engineers are facing in development of AI-based systems. The workshop will be highly interactive: In addition to the invited keynotes and short paper presentations, there will be several discussion sessions. We plan to combine local and remote participation.

Call for submission

You are invited to submit

• A research or experience full paper with 8 pages max. Papers describing the challenges, starting results, vision papers, or the experience papers from or in cooperation with the practitioners are encouraged.
• A short research or experience paper with 4 pages max. The same topics as for long papers.
• Position paper with expressed interest, 1 page.

The full and short paper submissions will undergo a review process with three independent reviews and a virtual PC decision meeting. The acceptance criteria include novelty, research and industrial relevance, soundness, experiences, and preliminary results. The accepted full and short papers will be published in IEEE Proceedings as a workshop proceedings at the ICSE conference. The position papers will be published on the workshop web page.

Topis of interests

The overall area is Software Engineering for AI, i.e. means to improve development of software AI-based systems and software-intensive systems, including topics relevant for the entire lifecycle. The suggested topics are (but not limited to):

• System and software requirements and their relations AI/ML modelling;
• Data management ensuring relevance and efficiency related to business goals;
• System and software architecture of AI-based systems;
• Integration of AI-development process and software development processes, including continuous and federated ML, continuous deployment, system and software evolution;
• Ensuring and managing system and software nonfunctional properties and their relation to AI/ML properties, including run-time properties such as performance, safety, security, reliability, and life-cycle properties including reusability, maintainability and evolution;
• Development teams, organizational and management issues for a successful development of AI-systems.

Submission form

Submissions must conform to the IEEE formatting instructions IEEE Conference Proceedings Formatting Guidelines. The official publication date of the workshop proceedings is the date the proceedings are made available by IEEE. This date may be up to two weeks prior to the first day of ICSE 2021. The official publication date affects the deadline for any patent filings related to published work.

Please note, the submissions should NOT be double blind, i.e. in the submission the authors should be specified.

The papers should be submitted to EasyChair web page. The submission deadline is firm.