Background: During Rapid Software Development, a large amount of project and development data can be collected from different and heterogeneous data sources. Aims: Design a methodology to process these data and turn it into relevant strategic indicators to help companies make meaningful decisions. Method: We adapt an existing methodology to create and estimate strategic indicators using Bayesian Networks in the context of Rapid Software Development, and applied it to a use case. Results: Applying the methodology in the use case, we create a model to predict product quality based on software factors and metrics, using companies’ business knowledge and collected data. Conclusions: We proved the methodology’s feasibility and obtained positive ...
Bayesian Belief Networks (BBNs) are becoming popular within the Software Engineering research commun...
Constructing an accurate effort prediction model is a challenge in Software Engineering. This paper ...
(….) This thesis focuses on these problems: first, the need to give accurate estimations to drive th...
Background: During Rapid Software Development, a large amount of project and development data can be...
The ability to reliably predict the end quality of software under development presents a significant...
Assessing and predicting the complex concept of software quality is still challenging in practice as...
Software prediction unveils itself as a difficult but important task which can aid the manager on de...
Apesar do alto número de métricas de software que vêm sendo apresentadas desde a década de 1960, sua...
Software quality can be described by a set of features, such as functionality, reliability, usabilit...
During the life cycle of a software project it is common that problems with the system re...
A concept for a strategy for quality prediction in distributed product development processes is pres...
Many organizations require quality improvement initiatives to be based on quantified business cases....
An important decision in software projects is when to stop testing. Decision support tools for this ...
Creating accurate models of information systems is an important but challenging task. It is generall...
Software practitioners lack a consistent approach to assessing and predicting quality within their p...
Bayesian Belief Networks (BBNs) are becoming popular within the Software Engineering research commun...
Constructing an accurate effort prediction model is a challenge in Software Engineering. This paper ...
(….) This thesis focuses on these problems: first, the need to give accurate estimations to drive th...
Background: During Rapid Software Development, a large amount of project and development data can be...
The ability to reliably predict the end quality of software under development presents a significant...
Assessing and predicting the complex concept of software quality is still challenging in practice as...
Software prediction unveils itself as a difficult but important task which can aid the manager on de...
Apesar do alto número de métricas de software que vêm sendo apresentadas desde a década de 1960, sua...
Software quality can be described by a set of features, such as functionality, reliability, usabilit...
During the life cycle of a software project it is common that problems with the system re...
A concept for a strategy for quality prediction in distributed product development processes is pres...
Many organizations require quality improvement initiatives to be based on quantified business cases....
An important decision in software projects is when to stop testing. Decision support tools for this ...
Creating accurate models of information systems is an important but challenging task. It is generall...
Software practitioners lack a consistent approach to assessing and predicting quality within their p...
Bayesian Belief Networks (BBNs) are becoming popular within the Software Engineering research commun...
Constructing an accurate effort prediction model is a challenge in Software Engineering. This paper ...
(….) This thesis focuses on these problems: first, the need to give accurate estimations to drive th...