Modeling class cohesion as mixtures of latent topics

Yixun Liu, Denys Poshyvanyk, Rudolf Ferenc, Tibor Gyimóthy, Nikos Chrisochoides

Research output: Chapter in Book/Report/Conference proceedingConference contribution

53 Citations (Scopus)

Abstract

The paper proposes a new measure for the cohesion of classes in Object-Oriented software systems. It is based on the analysis of latent topics embedded in comments and identifiers in source code. The measure, named as Maximal Weighted Entropy, utilizes the Latent Dirichlet Allocation technique and information entropy measures to quantitatively evaluate the cohesion of classes in software. This paper presents the principles and the technology that stand behind the proposed measure. Two case studies on a large open source software system are presented. They compare the new measure with an extensive set of existing metrics and use them to construct models that predict software faults. The case studies indicate that the novel measure captures different aspects of class cohesion compared to the existing cohesion measures and improves fault prediction for most metrics, which are combined with Maximal Weighted Entropy.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Software Maintenance, ICSM 2009 - Proceedings of the Conference
Pages233-242
Number of pages10
DOIs
Publication statusPublished - Dec 4 2009
Event2009 IEEE International Conference on Software Maintenance, ICSM 2009 - Edmonton, AB, Canada
Duration: Sep 20 2009Sep 26 2009

Publication series

NameIEEE International Conference on Software Maintenance, ICSM

Other

Other2009 IEEE International Conference on Software Maintenance, ICSM 2009
CountryCanada
CityEdmonton, AB
Period9/20/099/26/09

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ASJC Scopus subject areas

  • Software

Cite this

Liu, Y., Poshyvanyk, D., Ferenc, R., Gyimóthy, T., & Chrisochoides, N. (2009). Modeling class cohesion as mixtures of latent topics. In 2009 IEEE International Conference on Software Maintenance, ICSM 2009 - Proceedings of the Conference (pp. 233-242). [5306318] (IEEE International Conference on Software Maintenance, ICSM). https://doi.org/10.1109/ICSM.2009.5306318