Statistical Methods for Predicting and Improving Cohesion Using Information Flow: An Empirical Study

  • Authors:

  • John Moses;Malcolm Farrow;Peter Smith

  • Affiliations:

  • University of Sunderland, School of Computing and Technology, St. Peter's Campus, St. Peters Way, P.O. Box 299, Sunderland, SR6 0YN, U.K. john.moses@sunderland.ac.uk;University of Sunderland, School of Computing and Technology, St. Peter's Campus, St. Peters Way, P.O. Box 299, Sunderland, SR6 0YN, U.K.;University of Sunderland, School of Computing and Technology, St. Peter's Campus, St. Peters Way, P.O. Box 299, Sunderland, SR6 0YN, U.K.

  • Venue:
  • Software Quality Control
  • Year:
  • 2002

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Abstract

We consider the difficulty in deriving and validating new scales of measurement for modular cohesion. We show that currently derived objective measures cannot predict, or measure, a scale of cohesion that has an empirical relation system, for which a “high degree of interpersonal agreement” exists. However, we demonstrate empirically that it is feasible to predict low levels of a cohesion scale with an observed empirical relation. For this scale there exists agreement to make the observational distinctions that form the empirical relation system. Our statistically derived prediction systems use information flow measures and are available at architectural and detailed design. These prediction systems have been validated and we have determined their predictive capability using cross-validation. Within the limits of their external validity, we discuss how these and future prediction systems can be used to improve modular cohesion. For example, improvements may be achieved by using a simple cut-off value for fanout to predict modules that lack cohesion.