Empirical analysis for investigating the effect of object-oriented metrics on fault proneness: a replicated case study

2009 ◽  
Vol 14 (1) ◽  
pp. 39-62 ◽  
Author(s):  
K. K. Aggarwal ◽  
Yogesh Singh ◽  
Arvinder Kaur ◽  
Ruchika Malhotra
Keyword(s):  
Empirical Analysis ◽  
Object Oriented ◽  
Fault Proneness ◽  
Object Oriented Metrics

A SYSTEMATIC REVIEW OF THE EMPIRICAL VALIDATION OF OBJECT-ORIENTED METRICS TOWARDS FAULT-PRONENESS PREDICTION

2013 ◽  
Vol 23 (10) ◽  
pp. 1513-1540 ◽  
Author(s):  
BASSEY ISONG ◽  
EKABUA OBETEN
Keyword(s):  
Empirical Studies ◽  
Object Oriented ◽  
Strong Impact ◽  
Empirical Validation ◽  
Relevant Article ◽  
System Failures ◽  
The Impact ◽  
Fault Proneness ◽  
Object Oriented Metrics

Object-oriented (OO) approaches of software development promised better maintainable and reusable systems, but the complexity resulting from its features usually introduce some faults that are difficult to detect or anticipate during software change process. Thus, the earlier they are detected, found and fixed, the lesser the maintenance costs. Several OO metrics have been proposed for assessing the quality of OO design and code and several empirical studies have been undertaken to validate the impact of OO metrics on fault proneness (FP). The question now is which metrics are useful in measuring the FP of OO classes? Consequently, we investigate the existing empirical validation of CK + SLOC metrics based on their state of significance, validation and usefulness. We used systematic literature review (SLR) methodology over a number of relevant article sources, and our results show the existence of 29 relevant empirical studies. Further analysis indicates that coupling, complexity and size measures have strong impact on FP of OO classes. Based on the results, we therefore conclude that these metrics can be used as good predictors for building quality fault models when that could assist in focusing resources on high risk components that are liable to cause system failures, when only CK + SLOC metrics are used.

scholarly journals Extension of Object-Oriented Metrics Suite for Software Maintenance

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
John Michura ◽  
Miriam A. M. Capretz ◽  
Shuying Wang
Keyword(s):  
Software Maintenance ◽  
Object Oriented ◽  
Software Developers ◽  
Additional Information ◽  
Complexity Metrics ◽  
Project Outcomes ◽  
Large Systems ◽  
Object Oriented Metrics ◽  
Require Information

Software developers require information to understand the characteristics of systems, such as complexity and maintainability. In order to further understand and determine characteristics of object-oriented (OO) systems, this paper describes research that identifies attributes that are valuable in determining the difficulty in implementing changes during maintenance, as well as the possible effects that such changes may produce. A set of metrics are proposed to quantify and measure these attributes. The proposed complexity metrics are used to determine the difficulty in implementing changes through the measurement of method complexity, method diversity, and complexity density. The paper establishes impact metrics to determine the potential effects of making changes to a class and dependence metrics that are used to measure the potential effects on a given class resulting from changes in other classes. The case study shows that the proposed metrics provide additional information not sufficiently provided by the related existing OO metrics. The metrics are also found to be useful in the investigation of large systems, correlating with project outcomes.

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