An Empirical Investigation of an Object-Oriented Software System

By M. Cartwright, Martin J. Shepperd. IEEE Trans. Software Eng., 2000

298 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/c07c4942f07879be2ef38ca51746bd3bd531b79b

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Citations in the corpus

Application of neural networks for software quality prediction using object-oriented metrics (2003, background): “Many measures have been proposed in the literature to capture the quality of object-oriented (OO) code and design for detecting fault-proneness of classes (Briand et al., 2002; Cartwright and Shepperd, 2000; Emam et al., 2001; Fioravanti and Nesi, 2001; ReiBing, 2001).”
Empirical Analysis of CK Metrics for Object-Oriented Design Complexity: Implications for Software Defects (2003, background): “Analyzing a medium-sized telecommunication system, Cartwright and Shepperd [17] studied the inheritance measures from the CK suite (DIT, NOC) and found that both these measures were associated with defect density of classes.”
Dynamic coupling measurement for object-oriented software (2004, background): “Regardless of the structural attribute considered, most metrics have been so far defined and collected based on a static analysis of the design or code [7], [10], [12], [13], [15], [16].”
An Exploratory Study of the Impact of Code Smells on Software Change-proneness (2009, background): “Cartwright and Shepperd [6] conducted an empirical study on an industrial C++ system (over 133 KLOC), which supported the hypothesis that classes in inheritance relations are more fault prone.”
MuJava: an automated class mutation system (2005, background): “However, the novel object-oriented language features introduce new kinds of problems that in some cases require novel solutions [6].”
Exploring the relationships between design measures and software quality in object-oriented systems (2000, background): “…have so far investigated the actual impact of these measures on quality attributes such as fault-proneness (Basili et al., 1996; Briand et al., 1997b; Cartwright and Shepperd, 1999), productivity or effort (Chidamber et al., 1998), or the amount of maintenance modifications (Li and Henry, 1993).”
The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics (2001, methodology): “More promising results were obtained after we reanalyzed the data of Cartwright and Shepperd [ 27 ].”
Software fault prediction metrics: A systematic literature review (2013, background): “Cartwright and Shepperd [74] EVNT Events [74,8]”