Towards Feature-based ML-enabled Behaviour Location

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférenceArticle dans les actes d'une conférence/un colloque

2 Téléchargements (Pure)

Résumé

Mapping behaviours to the features they relate to is a prerequisite for variability-intensive systems (VIS) reverse engineering. Manually providing this whole mapping is labour-intensive. In black-box scenarios, only execution traces are available (e.g., process mining). In our previous work, we successfully experimented with variant-based mapping using supervised machine learning (ML) to identify the variants responsible of the production of a given execution trace, and demonstrated that recurrent neural networks (RNNs) work well (above 80% accuracy) when trained on datasets in which we label execution traces with variants. However, this mapping (i) may not scale to large VIS because of combinatorial explosion and (ii) makes the internal ML representation hard to understand. In this short paper, we discuss the design of a novel approach: feature-based mapping learning.
langue originaleAnglais
titreProceedings of the 18th International Working Conference on Variability Modelling of Software-Intensive Systems (VaMoS 2024)
Lieu de publicationBern, Switzerland
EditeurACM Press
Nombre de pages3
Les DOIs
Etat de la publicationPublié - 7 févr. 2024
Evénement18th International Working Conference on Variability Modelling of Software-Intensive Systems (VaMoS 2024) - Bern, Suisse
Durée: 7 févr. 20249 févr. 2024
Numéro de conférence: 18
https://vamos2024.inf.unibe.ch

Série de publications

NomProceedings of the 18th International Working Conference on Variability Modelling of Software-Intensive Systems

Une conférence

Une conférence18th International Working Conference on Variability Modelling of Software-Intensive Systems (VaMoS 2024)
Pays/TerritoireSuisse
La villeBern
période7/02/249/02/24
Adresse Internet

Empreinte digitale

Examiner les sujets de recherche de « Towards Feature-based ML-enabled Behaviour Location ». Ensemble, ils forment une empreinte digitale unique.

Contient cette citation