Using the Conceptual Cohesion of Classes for Fault Prediction in Object Oriented Systems


High cohesion is a desirable property of software as it positively impacts understanding, reuse, and maintenance. Currently proposed measures for cohesion in Object-Oriented (OO) software reflect particular interpretations of cohesion and capture different aspects of it. Existing approaches are largely based on using the structural information from the source code, such as attribute references, in methods to measure cohesion. This paper proposes a new measure for the cohesion of classes in OO software systems based on the analysis of the unstructured information embedded in the source code, such as comments and identifiers. The measure, named the Conceptual Cohesion of Classes (C3), is inspired by the mechanisms used to measure textual coherence in cognitive psychology and computational linguistics. This paper presents the principles and the technology that stand behind the C3 measure. A large case study on three open source software systems is presented which compares the new measure with an extensive set of existing metrics and uses them to construct models that predict software faults. The case study shows that the novel measure captures different aspects of class cohesion compared to any of the existing cohesion measures. In addition, combining C3 with existing structural cohesion metrics proves to be a better predictor of faulty classes when compared to different combinations of structural cohesion metrics.

IEEE Transactions on Software Engineering, 34(2):287–300


    author   = {Marcus, Andrian and Poshyvanyk, Denys and Ferenc, Rudolf},
    title    = {Using the Conceptual Cohesion of Classes for Fault Prediction in Object Oriented Systems},
    journal  = {{IEEE} Transactions on Software Engineering},
    year     = {2008},
    volume   = {34},
    number   = {2},
    pages    = {287--300},
    month    = mar,
    issn     = {1939-3520},
    doi      = {10.1109/TSE.2007.70768},
    keywords = {Document indexing, Maintainability, Metrics/Measurement, Quality analysis and evaluation, Restructuring, reverse engineering, and reengineering, Code documentation, Document analysis},
    url      = {},