A software measurement framework guided by Support Vector Machines - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

A software measurement framework guided by Support Vector Machines

Résumé

The quality of software engineering has always been of high importance for many actors. With the complexity of the platforms and its components, this is nowadays becoming crucial at each level in order to detect the eventual defects. Due to that complexity, the current measurement and analysis processes become heavier. This work aims at improving the software monitoring processes and its analysis. Based on a learning-aided analysis, we intend to suggest and select metrics that should be applied at runtime to increase the quality of the measurement plan and to target metrics that could raise relevant information on the measureand. Our approach proposes a data model that allows highlighting the monitored activity of a characteristic according to the data values of the model. We focus on complex metrics that are formally modeled using the OMG standard SMM. Some experiments are performed to exemplify our methodology
Fichier non déposé

Dates et versions

hal-01575616 , version 1 (21-08-2017)

Identifiants

Citer

Sarah Dahab, Stephane Maag, Xiaoping Che. A software measurement framework guided by Support Vector Machines. 31st International Conference on Advanced Information Networking and Applications Workshops, Mar 2017, Taipei, Taiwan. pp.397 - 402, ⟨10.1109/WAINA.2017.66⟩. ⟨hal-01575616⟩
60 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More