Comparing design and code metrics for software quality prediction
Proceedings of the 4th international workshop on Predictor models in software engineering
Implications of ceiling effects in defect predictors
Proceedings of the 4th international workshop on Predictor models in software engineering
Can data transformation help in the detection of fault-prone modules?
DEFECTS '08 Proceedings of the 2008 workshop on Defects in large software systems
Review: A systematic review of software fault prediction studies
Expert Systems with Applications: An International Journal
Misclassification cost-sensitive fault prediction models
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
On the relative value of cross-company and within-company data for defect prediction
Empirical Software Engineering
An FIS for early detection of defect prone modules
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Variance analysis in software fault prediction models
ISSRE'09 Proceedings of the 20th IEEE international conference on software reliability engineering
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
Information and Software Technology
Defect prediction using social network analysis on issue repositories
Proceedings of the 2011 International Conference on Software and Systems Process
Idea: using system level testing for revealing SQL injection-related error message information leaks
ESSoS'10 Proceedings of the Second international conference on Engineering Secure Software and Systems
Empirical evidence on OCL formal specification-based metrics as a predictor of fault-proneness
ACM SIGSOFT Software Engineering Notes
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The prediction of fault-prone modules in a software project has been the topic of many studies. In this paper, we investigate whether metrics available early in the development lifecycle can be used to identify fault-prone software modules. More precisely, we build predictive models using the metrics that characterize textual requirements. We compare the performance of requirements-based models against the performance of code-based models and models that combine requirement and code metrics. Using a range of modeling techniques and the data from three NASA projects, our study indicates that the early lifecycle metrics can play an important role in project management, either by pointing to the need for increased quality monitoring during the development or by using the models to assign verification and validation activities.