An application of Bayesian network for predicting object-oriented software maintainability

Abstract

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 postgraduate research programmes leading to MCom, MA, MSc and PhD degrees. Research projects in spatial information processing, connectionist-based information systems, 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 responsibility of the authors. CopyrightCopyright remains with the authors. Permission to copy for research or teaching purposes is granted on the condition that the authors and the Series are given due acknowledgment. Reproduction in any form for purposes other than research or teaching is forbidden unless prior written permission has been obtained from the authors. CorrespondenceThis paper represents work to date and may not necessarily form the basis for the authors' final conclusions relating to this topic. It is likely, however, that the paper will appear in some form in a journal or in conference proceedings in the near future. The authors 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 concerning the Series should be sent to the DPS Coordinator. AbstractAs the number of object-oriented software systems increases, it becomes more important for organizations to maintain those systems effectively. However, currently only a small number of maintainability prediction models are available for objectoriented systems. This paper presents a Bayesian network maintainability prediction 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.

A Bayesian Belief Network for Modeling Open Source Software Maintenance Productivity

“…The use of function points are proposed for estimating the effort of software maintenance projects [1]. Bayesian Networks are also used in Software Maintenance [26]. Van Koten and Gray [26] use Bayesian Networks to predict the maintainability measured as the change in software lines of code between subsequent releases.…”

“…Bayesian Networks are also used in Software Maintenance [26]. Van Koten and Gray [26] use Bayesian Networks to predict the maintainability measured as the change in software lines of code between subsequent releases. The results are improved compared to regression models.…”

“…According to Riaz et al [22] the metric suites proposed by Li and Henry [15] and Chidamber and Kemerer [8] are the most accurate ones, while the model of van Koten and Gray [26] is the most stable one [22]. The metric suite that is used from the aforementioned study is presented in Table 1.…”

Self-learning Change-prone Class Prediction

“…[6], code smells [7], design patterns and [8] evolution metrics [9,10]. In terms of the techniques, different machine learning approaches have been used, such as Bayesian networks [11], neural networks [12], multivariate regression [1] and ensemble methods [5]. A typical prediction model based on machine learning is designed by learning from a historical labeled dataset in a supervised way.…”

Software Quality Prediction Models Compared

“…This effort is measured by means of the time spent on performing a maintenance task [6], [22], the changes performed [21], [23], [24], or the maintainability index (MI) [25]. The changes made in the code is in most cases approximated by number of lines of code changed.…”