SEKE 2012 Proceedings - Knowledge Systems Institute

some of the key areas of management and software
development, such as: risk management, predicting failures and
effort estimation.
Figure 1. Bayesian network with the conditional probabilities
Risk Management: the study presented in [9] proposes a
standard architecture for risk identification called Risk
Identification Pattern model. The use of Bayesian networks
as the main component of the model made it possible to
represent the relationships among risk factors present in
web projects.
Predicting Failures: in [12] is presented a review of the use
of BN for predicting faults and software reliability. Beside
this, it proposes an approach that allows us to use dynamic
discretisation algorithms for continuous nodes.
Effort Estimate: stands out the comparative study of models
of Bayesian networks focused on the effort estimation in
web projects presented in [6] that disseminates the results
of an investigation where eight Bayesian network models
were compared for their accuracy in estimating effort for
web projects. The results showed that the Bayesian
networks represent a suitable approach for the treatment of
effort estimates.
activities and agenda of the developers. It was chosen as a
supportive environment for project management because
allows a r igorous control over the variables that form a
software process.
Bayesian networks tool: in this study we used GeNIe 1 ,
developed by Decision Systems Laboratory, from
Pittsburgh University. The GeNIe software was used to
model the Bayesian networks for the evaluation of the
proposed model, because of its ease of use and that the free
version has no restrictions on the maximum possible size
for a BN.
A. Presentation of the monitoring model scenarios
For better understanding of the monitoring and control
model proposed, it is necessary to presentation and analysis of
scenarios where the model will work and to observe the pre and
post conditions required for proper operation. Fig. 2 presents
the scenario 1 that handles the BN configuration required for
the correct functioning of the model. As prerequisites, it is
necessary a previous modeled process in the WebAPSEE;
furthermore, the topology of the network should already be set
according to the aspect that you wish to monitor in the process
(time, cost, quality,etc.). From this, is shown a web interface
through which you can select relevant data from the running
process according to the aspect that you wish to monitor. For
example: considering that you want to monitor the process on
the aspect of time, would be possible to select items such as
“number of agents involved” and “total hours remaining” and
so on. From this, the selected data will be extracted from the
running process. After extraction of this information, a table
containing the information extracted is generated
automatically. Through this table will be possible to identify
information used to configure the evidence in the BN, which is
the post condition of the scenario 1.
III. BAYESIAN NETWORKS AS A TOOL TO SUPPORT
MONITORING OF PROJECTS
Monitoring software projects using mechanisms to detect
changes during its progress contributes to that unexpected
events do not deviate the planning. If this happens, it is
possible to m ake changes in order to adapt to new reality
imposed a l ess traumatic and fastest possible. Unlike other
approaches used to make estimates that use parametric
measure, the use of BN suggests a statistical value (approx).
The following are the main software components used to
compose the monitoring model proposed. After this, is shown a
conceptual model of the proposed solution, which seeks to
provide how will the interaction among the tools that form the
solution, besides the characteristics and details of
implementation:
WebAPSEE[14]: through this environment is possible to
model a de velopment process, defining the activities, the
sequence among them, the papers involved, and the
execution time. The environment allows its execution
through a machine that coordinates the activation of
The scenario 2 refers to the monitoring process. As a
prerequisite for proper functioning of scenario 2 we have the
fact that the process is running and the BN is configured.
Initially, it is necessary to identify changes of state in the
running process. Thus, a Windows Service has been developed
in order to monitor the data involved in the process. This action
of checking changes in the process is done in time intervals
previously established. So, it make possible to use the latest
data from running process directly in the web interface. After
this, we can update the BN, spreading the current state of the
1
http://genie.sis.pitt.edu/
Figure 2. Bayesian network configuration
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