Change Prediction in Object-Oriented Software Systems: A Probabilistic Approach

Abstract

<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 8.5pt;">An estimation of change-proneness of parts of a software system is an active topic in the area of software engineering. Such estimates can be used to predict changes to different classes of a system from one release to the next. They can also be used to estimate and possibly reduce the effort required during the development and maintenance phase by balancing the amount of developers’ time assigned to each part of a software system. This research work proposes a novel approach to predict changes in an object-oriented software system. The rationale behind this approach is that in a well-designed software system, feature enhancement or corrective maintenance should affect a limited amount of existing code. Our goal is to quantify this aspect of quality by assessing the probability that each class will change in a future generation. Our proposed probabilistic approach uses the dependencies obtained from the UML diagrams, as well as other code metrics extracted from source code of several releases of a software system using reverse engineering techniques. These measures, combined with the change log of the software system and the expected time of next release, are used in an automated manner to predict whether a class will change in the next release of the software system. The proposed systematic approach has been evaluated on a multiversion medium sized open source project namely JFlex, the Fast Scanner Generator for Java. The obtained results indicate the simplicity and accuracy of our approach in the comparison with existing methods referred in the literature.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 8.5pt;"></span></p

Developer-Related Factors in Change Prediction: An Empirical Assessment

“…For instance, the combination between dependencies mined from UML diagrams [36] and code metrics has been proposed [12], [13], [14], [15]. Also genetic and learning algorithms have been proposed in this context [37] [38] [39].…”

“…For this reason, researchers devoted a lot of attention to the problem by (i) analyzing the factors influencing the change-proneness of classes [6], [8], [9], [10], [11], and (ii) devising prediction models able to alert developers about the classes on which preventive actions should be focused on [12], [13], [14], [15].…”

Synergy of Classical and Model-Based Object-Oriented (OO) Metrics in Reducing Test Costs

“…Second, it aims to devise a workbench in which the changes to the source code do not occur in a vacuum, but can be evaluated and finetuned in order to address specific quality requirements for the Validated [12] new target system such as enhancements in maintainability. Ali R. Sharafat et al, [18]proposed future work on the extensibility of the proposed approach on large scale projects with the scope of determining those parameters directly from the source code, the application domain, or any other related data rather than calculating empirically. There areseveral possible solutions discarded due to the lack of resources which can be reconsidered when new case studies are analyzed.…”

“…Ali R. Sharafatet al, [18] proposed a probabilistic approach to predict changes in OO systems. This technique is applied on an OO open source project, JFlex [19].…”

Understanding Change Prone Classes in Object Oriented Software

“…To tackle this requirement, Ah-Rim Han et al [16] have offered an innovative and a systematic method for estimating the class dependency measure (BDM) which enables proper forecast of changeproneness in UML 2.0 brand. Ali R. Sharafat and Ladan Tahvildari [17] have come out with a novel method to forecast modifications in an object-oriented software mechanism. The key dilemma in software growth procedure is to evolve inaccuracy recognition to initial stages of the software life span.…”