SEKE 2012 Proceedings - Knowledge Systems Institute

Investigating the use of Bayesian networks as a
support tool for monitoring software projects
Fábio Pittoli 1 , Abraham L. R. de Sousa 1, 2
1 Centro Universitário La Salle - Unilasalle
Canoas, Brazil
{fabio.pittoli@gmail.com, rabelo@unilasalle.edu.br}
Abstract— The monitoring of software development is one of the
most important activities of software projects management. In
this context, this paper proposes a Bayesian approach integrated
with a software process management environment. The aim is to
investigate how this probabilistic approach can be used for
projects monitoring. Preliminary results indicate that the use of
Bayesian networks brings the power of quantitative and
qualitative evaluation of some common scenarios of project
management, leading to the manager a greater power of decision
making.
Software Development Process; Bayesian Network; Project
Management
I. INTRODUCTION
One of the main challenges of any project manager is trying
to ensure that software development project will be concluded
within the constraints of time, cost, scope, and quality.
Establishing these constraints means working with estimates.
But the software development is an inherently uncertain
endeavor, because there is no way to ensure that during the
progress of the project delays will not occur, will not lack
resources, or the scope will not change. Within this context of
reasoning under uncertainty arise the Bayesian networks
[1][2][3] that are used in situations where there are causal
relations, but our understanding of what is really happening is
incomplete, requiring probabilistic description for a better
understanding. This type of network can be used for different
types of reasoning, as predictive analysis, to investigate the
impact of changes (cause and effect), and support decisionmaking.
The main goal of this paper is to s how the
preliminaries results of a study that aims to integrate an
environment for project management with Bayesian networks
during the monitoring of software development projects. In this
sense, the manager interacts with a Bayesian model to identify
potential behaviors of the project and thereafter, decisions are
made.
II. THEORETICAL GROUNDING
Estimated time and cost development is an activity that
requires attention and that has great influence in the process of
software development. It’s the estimation definition which
ensures that a project will succeed or not during its execution.
Effort estimates are useful for clients and developers. [5].
Based on these estimates, the organization that wants to hire
Daltro J Nunes 2
2 Instituto de Informática
Universidade Federal do Rio Grande do Sul - UFRGS
Porto Alegre, Brazil
{rabelo, daltro@inf.ufrgs.br}
the project can assess and monitor the implementation costs,
evaluate proposals, and develop realistic budgets and
schedules.
Whichever method you choose to run estimates, it is always
important to observe that the estimation process is a complex
domain where the causal relationship among factors are nondeterministic
and with an inherently uncertain nature [6]. For
example: we can assume that there is a cl ear relationship
between development effort and team experience, and that
when team experience increases, the effort decreases, although,
there are no concrete data proving that. So, would be correct to
say that handle estimates, necessarily, result in dealing with
uncertainties.
A. Bayesian Networks
Bayesian networks (BN) were developed in the early ‘80s
to facilitate the task of predicting and diagnosing in Artificial
Intelligence (AI) systems [1]. The name Bayesian Network
derives from the use of the mathematical formula for the
calculation of probabilities established in 1763 by Thomas
Bayes. According to [2], the BN allow us to express complex
cause-effect relationships based on the problem investigated.
The graphical representation of a Bay esian network is
composed of nodes that represent random variables that assume
discrete values or continuous. The arches represent the causal
relationships among nodes. For example, we can consider a
classic case of BN presented by [7] about a new burglar alarm.
This alarm is very reliable; however, it can also trigger if an
earthquake occurs. Two neighbors, John and Mary, pledged to
call if the alarm is ringing. John always calls when he hears the
alarm, but, sometimes confuses the alarm with the phone and
call also in these cases. Maria, however, likes to listen to loud
music and sometimes does not hear the alarm. Fig. 1 shows the
defined the network topology to this case and tables of
conditional probabilities to each node. This form of
representation can be used to represent discrete variables or the
continuous variables. Each of the lines in the table contains the
conditional probability for each conditional case parent nodes.
A conditional case represents a possible combination of values
for the parent nodes [8].
B. Related Work
Within several studies that use BN to support software
process development, we tried to divide the references among
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