SSBSE 2022
Thu 17 - Fri 18 November 2022 Singapore
co-located with ESEC/FSE 2022
Thu 17 Nov 2022 09:00 - 10:30 at ERC SR 9 - Plenary + Keynote 1 Chair(s): Mike Papadakis

Increasingly, many systems, including critical ones, rely on machine learning (ML) components to achieve autonomy or adaptiveness. Such components, having no specifications or source code, impact the way we develop but also verify such systems. This talk will report on experiences and lessons learned in applying search-based solutions to test and analyse such ML-enabled systems. Indeed, our results have shown that metaheuristic search plays a key role in enabling the effective test automation of ML models and the systems they are integrated into. Though other techniques are also required to achieve scalability and enable safety analysis, for example, the black-box nature of ML components naturally lends itself to search-based solutions.

Thu 17 Nov

Displayed time zone: Beijing, Chongqing, Hong Kong, Urumqi change

09:00 - 10:30
Plenary + Keynote 1Keynotes at ERC SR 9
Chair(s): Mike Papadakis University of Luxembourg, Luxembourg
Applications of Search-based Software Testing to Trustworthy Artificial Intelligence
Lionel Briand University of Luxembourg; University of Ottawa
Media Attached