Software engineering (3rd ed.): a practitioner's approach
Software engineering (3rd ed.): a practitioner's approach
Object-oriented metrics that predict maintainability
Journal of Systems and Software - Special issue on object-oriented software
Property-Based Software Engineering Measurement
IEEE Transactions on Software Engineering
A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
Predicting Fault-Prone Software Modules in Telephone Switches
IEEE Transactions on Software Engineering
Managerial Use of Metrics for Object-Oriented Software: An Exploratory Analysis
IEEE Transactions on Software Engineering
Effort estimation and prediction of object-oriented systems
Journal of Systems and Software
Another metric suite for object-oriented programming
Journal of Systems and Software
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Validation of an Approach for Improving Existing Measurement Frameworks
IEEE Transactions on Software Engineering
Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
Software Metrics: A Rigorous and Practical Approach
Software Metrics: A Rigorous and Practical Approach
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
Test-Execution-Based Reliability Measurement and Modeling for Large Commercial Software
IEEE Transactions on Software Engineering
OOA Metrics for the Unified Modeling Language
CSMR '98 Proceedings of the 2nd Euromicro Conference on Software Maintenance and Reengineering ( CSMR'98)
Predicting Fault-Prone Classes with Design Measures in Object-Oriented Systems
ISSRE '98 Proceedings of the The Ninth International Symposium on Software Reliability Engineering
Industrial Application of Criticality Predictions in Software Development
ISSRE '98 Proceedings of the The Ninth International Symposium on Software Reliability Engineering
Object-Oriented Architecture Measures
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Statistical significance testing: a panacea for software technology experiments?
Journal of Systems and Software - Special issue: Applications of statistics in software engineering
Modeling Design/Coding Factors That Drive Maintainability of Software Systems
Software Quality Control
Using Developer Information as a Factor for Fault Prediction
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
Automating algorithms for the identification of fault-prone files
Proceedings of the 2007 international symposium on Software testing and analysis
Software engineering research: from cradle to grave
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Comparing negative binomial and recursive partitioning models for fault prediction
Proceedings of the 4th international workshop on Predictor models in software engineering
Empirical Software Engineering
Empirical Analysis of the Relation between Level of Detail in UML Models and Defect Density
MoDELS '08 Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems
On the ability of complexity metrics to predict fault-prone classes in object-oriented systems
Journal of Systems and Software
Comparing the effectiveness of several modeling methods for fault prediction
Empirical Software Engineering
What can fault prediction do for you?
TAP'08 Proceedings of the 2nd international conference on Tests and proofs
Using complexity, coupling, and cohesion metrics as early indicators of vulnerabilities
Journal of Systems Architecture: the EUROMICRO Journal
ACM SIGSOFT Software Engineering Notes
A learning-to-rank algorithm for constructing defect prediction models
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Software fault prediction with object-oriented metrics based artificial immune recognition system
PROFES'07 Proceedings of the 8th international conference on Product-Focused Software Process Improvement
Investigating of high and low impact faults in object-oriented projects
ACM SIGSOFT Software Engineering Notes
An in-depth study of the potentially confounding effect of class size in fault prediction
ACM Transactions on Software Engineering and Methodology (TOSEM)
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The goal of this paper is to investigate and assess the ability of explanatory models based on design metrics to describe and predict defect counts in an object-oriented software system. Specifically, we empirically evaluate the influence of design decisions to defect behavior of the classes in two products from the commercial software domain. Information provided by these models can help in resource allocation and serve as a base for assessment and future improvements.We use innovative statistical methods to deal with the peculiarities of the software engineering data, such as non-normally distributed count data. To deal with overdispersed data and excess of zeroes in the dependent variable, we use negative binomial (NB) and zero-inflated NB regression in addition to Poisson regression.Furthermore, we form a framework for comparison of models' descriptive and predictive ability. Predictive capability of the models to identify most critical classes in the system early in the software development process can help in allocation of resources and foster software quality improvement. In addition to the correlation coefficients, we use additional statistics to assess a models' ability to explain high variability in the data and Pareto analysis to assess a models' ability to identify the most critical classes in the system.Results indicate that design aspects related to communication between classes and inheritance can be used as indicators of the most defect-prone classes, which require the majority of resources in development and testing phases. The zero-inflated negative binomial regression model, designed to explicitly model the occurrence of zero counts in the dataset, provides the best results for this purpose.