SEAMS 2019
Sat 25 - Sun 26 May 2019 Montreal, QC, Canada
co-located with ICSE 2019
Sat 25 May 2019 11:00 - 11:25 at Duluth - Learning Chair(s): Rogério de Lemos

When a self-adaptive system detects that its adap-tation goals may be compromised, it needs to determine how to adapt to ensure its goals. To that end, the system can analyze the possible options for adaptation, i.e., the adaptation space, and pick the best option that achieves the goals. Such analysis can be resource and time consuming, in particular when rigorous analysis methods are applied. Hence, exhaustively analyzing all options may be infeasible for systems with large adaptation spaces. This problem is further complicated as the adaptation options typically include uncertainty parameters that can only be resolved at runtime. In this paper, we present a machine learning approach to tackle this problem. This approach enhances the traditional MAPE-K feedback loop with a learning module that selects subsets of adaptation options from a large adaptation space to support the analyzer with performing efficient analysis. We instantiate the approach for two concrete learning techniques, classification and regression, and evaluate the approaches for two instances of an Internet of Things application for smart environment monitoring with different sizes of adaptation spaces. The evaluation shows that both learning approaches reduce the adaptation space significantly without noticeable effect on realizing the adaptation goals.

Sat 25 May

11:00 - 12:25: SEAMS 2019 - Learning at Duluth
Chair(s): Rogério de LemosUniversity of Kent, UK
seams-2019-papers11:00 - 11:25
Federico QuinKatholieke Universiteit Leuven, Danny WeynsKU Leuven, Thomas BamelisKatholieke Universiteit Leuven, Sarpreet Singh ButtarLinnaeus University, Sam MichielsKatholieke Universiteit Leuven
seams-2019-papers11:25 - 11:50
Mirko D'AngeloLinnaeus University, Sweden, Simos Gerasimou, Sona GhahremaniHasso Plattner Institute, University of Potsdam, Johannes GrohmannUniversity of Wurzburg, Ingrid NunesUniversidade Federal do Rio Grande do Sul (UFRGS), Brazil, Evangelos PournarasETH Zurich, Switzerland, Sven TomfordeUniversitat Kassel
seams-2019-papers11:50 - 12:10
Colin Paterson, Radu CalinescuUniversity of York, UK, Suresh ManandharUniversity of York, UK, Di WangUniversity of York, UK
seams-2019-papers12:10 - 12:25
Ilias GerostathopoulosTechnical University of Munich, Evangelos PournarasETH Zurich, Switzerland