Fri 19 Aug 2022 19:30 - 19:50 at Dibbler - Artificial Intelligence for RE Chair(s): Rifat Ara Shams

In the studies of self-adaptive systems (SAS), requirement relaxation is a widely discussed approach for managing the system’s requirements when dealing with the runtime environment changes (e.g., ignoring low-priority requirements to guarantee high-priority requirements). Guaranteeable requirement analysis (GRA) is recently proposed to determine the relaxation by checking the feasibility of all requirement combinations, enabling the SAS to realize the relaxation autonomously. However, a critical problem of GRA is the trade-off between analysis/relaxation precision and computation time at different granularity levels of requirements. Specifically, the analysis may not be precise enough if the requirements are coarse-grained (i.e., high granularity level), while the analysis may take a too long time if the requirements are fine-grained (i.e., low granularity level). This paper proposed a method, namely iterative adaptation via multi-grained requirement relaxation, to achieve the advantages of high precision and short computation time. Specifically, the SAS first deploys a rapid (but imprecise) relaxation using high granularity-level requirements. It then repeatedly iterates to a preciser (but slower) relaxation with a progressive decrease in the granularity level. An experiment based on the warehouse robot system demonstrates the validity of our proposal.

Fri 19 Aug

Displayed time zone: Hobart change

19:00 - 20:10
Artificial Intelligence for RERE@Next! Papers / Research Papers at Dibbler
Chair(s): Rifat Ara Shams CSIRO's Data61
Automatic Terminology Extraction and Ranking for Feature Modeling
Research Papers
Jianzhang Zhang Alibaba Business School, Hangzhou Normal University, Sisi Chen Alibaba Business School, Hangzhou Normal University, Hangzhou, China, Jinping Hua Alibaba Business School, Hangzhou Normal University, Hangzhou, China, Nan Niu University of Cincinnati, Chuang Liu Alibaba Business School, Hangzhou Normal University, Hangzhou, China
Done is better than perfect: Iterative Adaptation via Multi-grained Requirement Relaxation
RE@Next! Papers
Jialong Li Waseda University, Japan, Kenji Tei Waseda University
Retraining a BERT Model for Transfer Learning in Requirements Engineering: A Preliminary Study
RE@Next! Papers
Muideen Ajagbe The University of Manchester, Liping Zhao University of Manchester