A Bayesian Network Based Approach for Change Coupling Prediction

被引：26

作者：

Zhou, Yu ^{[1
,2
]}

Wuersch, Michael ^{[2
]}

Giger, Emanuel ^{[2
]}

Gall, Harald ^{[2
]}

Lue, Jian ^{[1
]}

机构：

[1] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Peoples R China

[2] Univ Zurich, Software Evolut & Architecture Lab, Zurich, Switzerland

来源：

FIFTEENTH WORKING CONFERENCE ON REVERSE ENGINEERING, PROCEEDINGS | 2008年

关键词：

D O I：

10.1109/WCRE.2008.39

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Source code coupling and change history are two important data sources for change coupling analysis. The popularity of public open source projects in recent years makes both sources available. Based on our previous research. in this paper we inspect different dimensions of software changes including change significance or source code dependency levels. extract a set of features from the two sources and propose a bayesian network-based approach for change coupling prediction. By combining the features from the co-changed entities and their dependency relation, the approach can model the underlying uncertainty. The empirical case study on two medium-sized open source projects demonstrates the feasibility and effectiveness of our approach compared to previous work.

引用

页码：27 / +

页数：2

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