An Application of Bayesian Network for Predicting
Object-Oriented Software Maintainability
Chikako van Koten
Andrew Gray
The Information Science
Discussion Paper Series
Number 2005/02
March 2005
ISSN 1172-6024
University of Otago
Department of Information Science
The Department of Information Science is one of six departments that make up the
School of Business at the University of Otago. The department offers courses of study
leading to a major in Information Science within the BCom, BA and BSc degrees. In
addition to undergraduate teaching, the department is also strongly involved in post-
graduate research programmes leading to MCom, MA, MSc and PhD degrees. Re-
search projects in spatial information processing, connectionist-based information sys-
tems, software engineering and software development, information engineering and
database, software metrics, distributed information systems, multimedia information
systems and information systems security are particularly well supported.
The views expressed in this paper are not necessarily those of the department as a
whole. The accuracy of the information presented in this paper is the sole responsibil-
ity of the authors.
Copyright
Copyright remains with the authors. Permission to copy for research or teaching pur-
poses is granted on the condition that the authors and the Series are given due ac-
knowledgment. Reproduction in any form for purposes other than research or teach-
ing is forbidden unless prior written permission has been obtained from the authors.
Correspondence
This paper represents work to date and may not necessarily form the basis for the au-
thors’ final conclusions relating to this topic. It is likely, however, that the paper will ap-
pear in some form in a journal or in conference proceedings in the near future. The au-
thors would be pleased to receive correspondence in connection with any of the issues
raised in this paper, or for subsequent publication details. Please write directly to the
authors at the address provided below. (Details of final journal/conference publication
venues for these papers are also provided on the Department’s publications web pages:
http://www.otago.ac.nz/informationscience/pubs/). Any other correspondence con-
cerning the Series should be sent to the DPS Coordinator.
Department of Information Science
University of Otago
P O Box 56
Dunedin
NEW ZEALAND
Fax: +64 3 479 8311
email: dps@infoscience.otago.ac.nz
www: http://www.otago.ac.nz/informationscience/
An application of Baye sian network for
predicting object-oriented software
maintainability
C. van Koten
1
and A.R. Gray
Department of Information Science,University of Otago, P.O.Box 56, Dunedin,
New Zealand
Abstract
As the number of object-oriented software systems increases, it becomes more im-
portant for organizations to maintain those systems effec tively. However, currently
only a small number of maintainability prediction models are available for object-
oriented s ystem s. This paper presents a Bayesian network maintainability predic-
tion model for an object-oriented software system. The model is constructed using
object-oriented metric data in Li and Henry’s datasets, which were collected from
two different object- oriented systems. Prediction accuracy of the model is evaluated
and compared with commonly used regression-based models. The results suggest
that the Bayesian network model can predict maintainability more accurately than
the regression-based models for one system, and almost as accurately as the best
regression-based model for the other system.
Key words: Object-oriented systems, Maintainability, Bayesian network,
Regression tree, Regression
1 Introduction
It is arguable that many object-oriented (OO) software systems are currently
in use. It is also arguable that the growing popularity of OO programming lan-
guages, such as Java, as well as the increasing number of software development
tools supporting the Unified Modelling Language (UML), encourages more OO
systems to be developed at present and in the future. Hence it is important
1
Corresponding author. Tel.: +64-3-479-8142; fax: +64-3-479-8311.
E-mail address: ckoten@infoscience.otago.ac.nz
Preprint submitted to Elsevier Science 27 February 2005
that those systems are maintained effectively and efficiently. A software main-
tainability prediction model enables organizations to predict maintainability
of a software system and assists them with managing maintenance resource.
In addition, if an accurate maintainability prediction model is available for
a software system, a defensive design can be adopted. This would minimize,
or at least reduce future maintenance effort of the system. Maintainability
of a software system can be measured in different ways. In this paper, main-
tainability is measured as the number of changes made to the code during
a maintenance period. Alternatively, maintainability may be measured as ef-
fort to make those changes. When maintainability is measured as effort, the
predictive model is called a maintenance effort prediction mo del. It is unfortu-
nate that the number of software maintainability prediction models including
maintenance effort prediction models, is currently very small in the literature.
Programming an OO software system is different from programming a non-
OO system due to the concepts that are specific to the OO paradigm, for
example, objects, inheritance and encapsulation. This difference limits the ap-
plicability of well-known non-OO software effort prediction models, such as
COCOMO [3], to OO software effort prediction, as well as non-OO software
metrics, such as Function Points [1], to measuring the characteristics of OO
software systems [23]. Hence a number of new software metrics were proposed
specifically for OO systems. Some of those OO metrics were used to predict
maintainability of OO systems. Examples of the OO metrics are Chidamber
and Kemerer (C&K) metrics and Li and Henry (L&H) metrics [10,25]. It was
shown that the L&H metrics had a correlation with the number of changes
made to the code of the OO software system [25]. It was also shown that
multiple linear regression models consisting of the C&K, L&H and other OO
metrics were able to predict software maintenance effort for some OO systems
[17].
This paper constructs an OO software maintainability prediction model using
a technique known as Bayesian network [14,20,22]. This technique allows a user
to construct a predictive model based on Bayesian probability theory [12]. An
application of Bayesian network to Software Engineering is currently limited
to a small number of studies of development effort prediction [2,11,31,34] and
defect prediction [15,28]. However, Bayesian network can also be a promis-
ing new technique for OO software maintainability prediction. This is due to
the ability to explicitly represent uncertainty using probabilities, the ability
to incorporate existing human expert’s knowledge into empirical data, and
the ability to update the model when new information becomes available.
Hence this paper investigates a research problem of what prediction accuracy
a Bayesian network OO software maintainability prediction model can achieve.
The term prediction accuracy in this paper means how well a predictive model
constructed using known data can predict the outcomes of unknown data. The
Bayesian ne twork model’s prediction accuracy is evaluated using some accu-
2
racy measures, which are commonly found in the software effort prediction
literature [16,24]. Those measures are absolute residuals, the magnitude of
relative error (MRE) and pred measures. Then, the Bayesian network mo del’s
prediction accuracy is compared with regression-based models, namely, a re-
gression tree [4] model and two different types of multiple linear regression
models.
The structure of the reminder of this paper is as follows. Section 2 describes
the OO software datasets and the sampling method used. Section 3 describes
the B ayesian network OO software maintainability prediction model. This
is followed by Section 4, which describes the regression tree model and the
multiple linear regression models. Section 5 describes the prediction accuracy
measures used. Section 6 evaluates the Bayesian network model’s prediction
accuracy using those accuracy measures and compares it with the regression
tree model and multiple linear regression models. Finally Section 7 presents
conclusions and discussions about a direction of future studies.
2 OO software datasets
2.1 Characteristics of datasets
This paper uses OO software datasets published by Li and Henry [25]. The
datasets consist of five C&K metrics: DIT, NOC, RFC, LCOM and WMC, and
four L&H metrics: MPC, DAC, NOM and SIZE2, as well as SIZE1, which is a
traditional lines of code size metric. Those metric data were collected from a
total of 110 classes in two OO software systems: User Interface Management
System (UIMS) and Quality Evaluation System (QUES). The code was writ-
ten in Cl assi cal − Ada
T M
. The UIMS and QUES datasets contain 39 classes
and 71 classes, respectively. Maintainability was measured in CHANGE met-
ric by counting the number of lines in the code, which were changed during
a three-year maintenance period. Neither UIMS nor QUES datasets contain
actual maintenance effort data. The de scription of each metric is given in
Table 1.
The descriptive statistics of the UIMS and QUES datasets are shown in Ta-
ble 2.
The Pearson’s correlation coefficients between CHANGE and each of the OO
metrics are shown in Table 3.
Table 3 shows that there is a significant correlation between CHANGE and the
OO metrics. However, Table 3 also shows that the correlations in the UIMS
3
![Table 1 Software Metrics [25]](/figures/table-1-software-metrics-25-16ehpid7.webp)
