Improving Model Learning by Inferring Separating Sequences from Traces
Models that can represent the behavior of systems, such as a Finite State Machine (FSM), are crucial for software development and maintenance as they serve as a base for several automated activities like testing, verification, validation, and refinement of systems. Contrasting their importance and value, models are usually complex and costly to obtain. Model inference algorithms can help with this task. In this paper, we propose a method to improve the learning process of FSMs by inferring separating sequences from traces and using them in characterization sets. We conducted a case study to assess the impact of the proposed method on an FSM learning algorithm called hw-inference. We observed that the proposed method was capable of improving by 24% the learning process.
Thu 20 AprDisplayed time zone: Dublin change
16:00 - 17:30 | |||
16:00 30mTalk | Improving Model Learning by Inferring Separating Sequences from Traces A-MOST Rafael Braz ICMC/USP, Adenilso Simão University of São Paulo, Roland Groz LIG/UGA, Catherine Oriat LIG/UGA | ||
16:30 30mTalk | MUPPAAL: Reducing and Removing Equivalent and Duplicate Mutants in UPPAAL A-MOST Jaime Cuartas Universidad del Valle, Jesus Aranda Universidad del Valle, Maxime Cordy University of Luxembourg, Luxembourg, James Ortiz Université de Namur, Gilles Perrouin Fonds de la Recherche Scientifique - FNRS & University of Namur, Pierre Yves Schobbens University of Namur | ||
17:00 15mDay closing | Closing A-MOST 23 and Welcoming A-MOST 24! A-MOST Florian Lorber Aalborg University, Cristina Seceleanu Mälardalen University, Uraz Cengiz Türker Lancaster University (UK) |