Towards a metrics suite for object oriented design

By Shyam R. Chidamber, Chris F. Kemerer. Conference on Object-Oriented Programming Systems, Languages, and Applications, 2017

955 citations recorded by the source corpus.

While software metrics are a generally desirable feature in the software management functions of project planning and project evaluation, they are of especial importance with a new technology such as the object-oriented approach. This is due to the significant need to train software engineers in generally accepted object-oriented principles. This paper presents theoretical work that builds a suite of metrics for object-oriented design. In particular, these metrics are based upon measurement theory and are informed by the insights of experienced object-oriented software developers. The proposed metrics are formally evaluated against a widelyaccepted list of software metric evaluation criteria.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/ab4c9fc41f6acde475d34787caca8dae9e6a3fa4

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Citations in the corpus

Exploring the relationships between design measures and software quality in object-oriented systems (2000, background): “Many measures have been proposed in the literature to capture the structural quality of object-oriented (OO) code and design (Chidamber and Kemerer, 1991; Chidamber and Kemerer, 1994; Li and Henry, 1993; Lee et al., 1995; Briand et al., 1997b; Henderson-Sellers, 1996; Hitz and Montazeri, 1995; Biema”
Empirical Analysis of CK Metrics for Object-Oriented Design Complexity: Implications for Software Defects (2003, methodology): “In their original work on OO metrics, Chidamber and Kemerer have argued that the number of levels above a class in the class hierarchy may, to a great extent, determine the predictability of the class's behavior [20].”
Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems (2008, methodology): “We will refer to the first version of LCOM [19] as LCOM1 and to the extended one [20] as LCOM2.”
Software fault prediction metrics: A systematic literature review (2013, background): “Chidamber and Kemerer [12,13] CBO Coupling between object classes 48 studies”
Software defect prediction using Bayesian networks (2012, methodology): “3 Previous Work Pai and Dugan (2007) use Bayesian networks to analyze the effect of object oriented metrics (Chidamber and Kemerer 1991) on the number of defects (fault content) and defect proneness using KC1 project from the Nasa metrics data repository.”