A Process to Calculate the Uncertainty of Software Metrics-based Models Using Bayesian Networks

Abstract

Software metrics are essential resources in software enterprises. They can be used to support decision-making and, consequently, reduce costs, improve the productivity of the team and the quality of products delivered. On the other hand, this is only possible if the metrics are valid. Although there are studies related to software metrics validity, none present a solution to represent the uncertainties of the metrics selected to measure the attributes of the entities. In this paper, we present a process to build Bayesian networks to represent the uncertainties of software metrics-based models. The proposed solution is composed of two activities and focuses on the selection and validation of metrics to construct the Bayesian networks. We validated the model with simulated scenarios. Given the successful results, we concluded that the proposed solution is promising. This paper complements the state of the art by showing how to complement a popular metric selection technique, GQM, with information to model uncertainties of the metrics using the concepts of metric validation and Bayesian networks.

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“…Although their definitions may overlap in places, a metric is attained by one or more measurements together with other information. On the other hand, a measurement is a value that is assigned to an entity 10 . However, metrics can be much more complex, as they provide a greater level of information about the entity being measured 3 …”

“…On the other hand, a measurement is a value that is assigned to an entity. 10 However, metrics can be much more complex, as they provide a greater level of information about the entity being measured. 3 The SPI manifesto suggests creating a learning organization.…”