Analyzing clusters of class characteristics in OO applications

Clarke, Peter J.; Babich, Djuradj; King, Tariq M.; Kibria, B. M. Golam

doi:10.1016/j.jss.2008.03.056

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…The class abstraction technique used in this paper is the taxonomy of OO classes previously described by Clarke et al [7]; in this section we summarise its main elements.…”

Section: Class Abstractionmentioning

confidence: 99%

“…A more detailed explanation of the descriptors and type families are provided in [7], and the approach has been implemented using a Taxonomy Tool for the Object-Oriented Language Java, TaxTOOLJ [1]. The significant saving in terms of the analysis overhead for Java classes has been demonstrated in a study of 22 Java applications, which showed that of all the possible groups representing the combinations of class characteristics only a small percentage were used [7].…”

Section: Class Abstractionmentioning

confidence: 99%

“…The significant saving in terms of the analysis overhead for Java classes has been demonstrated in a study of 22 Java applications, which showed that of all the possible groups representing the combinations of class characteristics only a small percentage were used [7]. …”

Section: Class Abstractionmentioning

confidence: 99%

See 2 more Smart Citations

Using a class abstraction technique to predict faults in OO classes

Babich

Clarke

Power

et al. 2011

Proceedings of the 2011 ACM Symposium on Applied Computing

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In this paper, we propose an innovative suite of metrics based on a class abstraction that uses a taxonomy for OO classes (CAT) to capture aspects of software complexity through combinations of class characteristics. We empirically validate their ability to predict fault prone classes using fault data for six versions of the Java-based open-source Eclipse Integrated Development Environment. We conclude that this proposed CAT metric suite, even though it treats classes in groups rather than individually, is as effective as the traditional Chidamber and Kemerer metrics in identifying fault-prone classes.

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“…The class abstraction technique used in this paper is the taxonomy of OO classes previously described by Clarke et al [7]; in this section we summarise its main elements.…”

Section: Class Abstractionmentioning

confidence: 99%

Section: Class Abstractionmentioning

confidence: 99%