Authors:
Franciele Beal
;
Patricia Rucker de Bassi
and
Emerson Cabrera Paraiso
Affiliation:
Pontifícia Universidade Católica do Paraná, Brazil
Keyword(s):
User Modelling, Machine Learning, Quality Metrics, Supervised Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Industrial Applications of Artificial Intelligence
;
Information Systems Analysis and Specification
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Software Metrics and Measurement
;
Tools, Techniques and Methodologies for System Development
Abstract:
Software development has become an essential activity for organizations that increasingly rely on these to
manage their business. However, poor software quality reduces customer satisfaction, while high-quality
software can reduce repairs and rework by more than 50 percent. Software development is now seen as a
collaborative and technology-dependent activity performed by a group of people. For all these reasons,
choosing correctly software development members teams can be decisive. Considering this motivation,
classifying participants in different profiles can be useful during project management team’s formation and
tasks distribution. This paper presents a developer modeling approach based on software quality metrics.
Quality metrics are dynamically collected. Those metrics compose the developer model. A machine
learning-based method is presented. Results show that it is possible to use quality metrics to model
developers.