A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
An investigation into coupling measures for C++
ICSE '97 Proceedings of the 19th international conference on Software engineering
Polymorphism measures for early risk prediction
Proceedings of the 21st international conference on Software engineering
Defining and Validating Measures for Object-Based High-Level Design
IEEE Transactions on Software Engineering
Exploring the relationship between design measures and software quality in object-oriented systems
Journal of Systems and Software
The prediction of faulty classes using object-oriented design metrics
Journal of Systems and Software
The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics
IEEE Transactions on Software Engineering
Logistic Regression Using the SAS System: Theory and Application
Logistic Regression Using the SAS System: Theory and Application
Replicated Case Studies for Investigating Quality Factorsin Object-Oriented Designs
Empirical Software Engineering
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
Assessing the applicability of fault-proneness models across object-oriented software projects
IEEE Transactions on Software Engineering
Predicting Fault-Proneness using OO Metrics: An Industrial Case Study
CSMR '02 Proceedings of the 6th European Conference on Software Maintenance and Reengineering
IEEE Transactions on Software Engineering
An Empirical Study on Object-Oriented Metrics
METRICS '99 Proceedings of the 6th International Symposium on Software Metrics
Defect Frequency and Design Patterns: An Empirical Study of Industrial Code
IEEE Transactions on Software Engineering
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Metrics Suite for Class Complexity
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II - Volume 02
Application of neural networks for software quality prediction using object-oriented metrics
Journal of Systems and Software
Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction
IEEE Transactions on Software Engineering
Predicting fault-prone components in a java legacy system
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
Object-oriented software fault prediction using neural networks
Information and Software Technology
Journal of Systems and Software
Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults
IEEE Transactions on Software Engineering
Predicting Defects for Eclipse
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
IEEE Transactions on Software Engineering
Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods
IEEE Transactions on Software Engineering
Improving fault detection in modified code: a study from the telecommunication industry
Journal of Computer Science and Technology
Journal of Software Maintenance and Evolution: Research and Practice
Journal of Systems and Software
Techniques for evaluating fault prediction models
Empirical Software Engineering
Analyzing Receiver Operating Characteristic Curves With SAS
Analyzing Receiver Operating Characteristic Curves With SAS
Software Dependencies, Work Dependencies, and Their Impact on Failures
IEEE Transactions on Software Engineering
Identification of defect-prone classes in telecommunication software systems using design metrics
Information Sciences: an International Journal
Software fault prediction for object oriented systems: a literature review
ACM SIGSOFT Software Engineering Notes
The ability of object-oriented metrics to predict change-proneness: a meta-analysis
Empirical Software Engineering
Estimating software testing complexity
Information and Software Technology
An in-depth study of the potentially confounding effect of class size in fault prediction
ACM Transactions on Software Engineering and Methodology (TOSEM)
Hi-index | 0.00 |
Many studies use logistic regression models to investigate the ability of complexity metrics to predict fault-prone classes. However, it is not uncommon to see the inappropriate use of performance indictors such as odds ratio in previous studies. In particular, a recent study by Olague et al. uses the odds ratio associated with one unit increase in a metric to compare the relative magnitude of the associations between individual metrics and fault-proneness. In addition, the percents of concordant, discordant, and tied pairs are used to evaluate the predictive effectiveness of a univariate logistic regression model. Their results suggest that lesser known complexity metrics such as standard deviation method complexity (SDMC) and average method complexity (AMC) are better predictors than the two commonly used metrics: lines of code (LOC) and weighted method McCabe complexity (WMC). In this paper, however, we show that (1) the odds ratio associated with one standard deviation increase, rather than one unit increase, in a metric should be used to compare the relative magnitudes of the effects of individual metrics on fault-proneness. Otherwise, misleading results may be obtained; and that (2) the connection of the percents of concordant, discordant, and tied pairs with the predictive effectiveness of a univariate logistic regression model is false, as they indeed do not depend on the model. Furthermore, we use the data collected from three versions of Eclipse to re-examine the ability of complexity metrics to predict fault-proneness. Our experimental results reveal that: (1) many metrics exhibit moderate or almost moderate ability in discriminating between fault-prone and not fault-prone classes; (2) LOC and WMC are indeed better fault-proneness predictors than SDMC and AMC; and (3) the explanatory power of other complexity metrics in addition to LOC is limited.