Automated suggesting software metrics framework - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Automated suggesting software metrics framework

Résumé

Due to the complexity of the current software, the measurement processes become crucial activities. However, due to the quantity of aspects to be measured, the software measurement plans are heavy to manage. It leads to very complex measurement plans engendering eventual losses of time and performance. The main objective of our paper is the improvement of the measurement plans by making the metrics use more flexible. This is an important requirements for the project managers. This allows to tackle specific useful metrics in avoiding measures that are not always relevant during an identified measured period of time. We propose to analyze and classify the measurements at runtime using a learning approach (Support Vector Machine, SVM) in order to define the relevant metrics that should be used at a specific time t. We designed a suggestion process that selects metrics from a current measurement plan or reorient (suggest) that measurement plan by proposing to execute other metrics. We implemented our framework on an efficient platform and successfully ran several experiments that we discuss and comment
Fichier non déposé

Dates et versions

hal-01799805 , version 1 (25-05-2018)

Identifiants

  • HAL Id : hal-01799805 , version 1

Citer

Sarah Dahab, Juan Jose Hernandez Porras, Stephane Maag. Automated suggesting software metrics framework. LATINCOM 2017 : 9th IEEE Latin-American Conference on Communications Workshop, Nov 2017, Guatemala City, Guatemala. ⟨hal-01799805⟩
17 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More