A Model for Software Product Quality
IEEE Transactions on Software Engineering
Detection Strategies: Metrics-Based Rules for Detecting Design Flaws
ICSM '04 Proceedings of the 20th IEEE International Conference on Software Maintenance
Object-Oriented Metrics in Practice
Object-Oriented Metrics in Practice
Identifying exogenous drivers and evolutionary stages in FLOSS projects
Journal of Systems and Software
Sourcerer: An internet-scale software repository
SUITE '09 Proceedings of the 2009 ICSE Workshop on Search-Driven Development-Users, Infrastructure, Tools and Evaluation
MSR '09 Proceedings of the 2009 6th IEEE International Working Conference on Mining Software Repositories
Integrating quality models and static analysis for comprehensive quality assessment
Proceedings of the 2nd International Workshop on Emerging Trends in Software Metrics
The quamoco tool chain for quality modeling and assessment
Proceedings of the 33rd International Conference on Software Engineering
A unifying model for software quality
Proceedings of the 8th international workshop on Software quality
The quamoco product quality modelling and assessment approach
Proceedings of the 34th International Conference on Software Engineering
A Systematic Literature Review on Fault Prediction Performance in Software Engineering
IEEE Transactions on Software Engineering
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In order to assess software quality by software metrics, usually, thresholds for metric values are needed. A common problem is to define reasonable threshold values. One possible solution is to use a benchmarking approach: the threshold value for a metric is calculated based on the metric values of a set of systems, which are called benchmarking base. A relevant question is, how the used benchmarking base inuences the result of the software quality assessment. Based on the quality assessment approach of Quamoco, we conduct a series of experiments, using different benchmarking bases. For each benchmarking base a quality assessment of a series of test systems is conducted. We analyze the whether the quality assessment results of the test systems are concordant for different benchmarking bases. The main findings are: (1) The bigger the benchmarking base, the less divergent are the rankings, and the less is the variance of the results. (2) The size of the systems contained within a benchmarking base does not inuence the results, i.e. a benchmarking base containing small systems works equally well for small and large systems, and vice versa. These results show that benchmarking is a feasible approach for determining threshold values.