Background: There has been much discussion amongst automated software defect prediction researchers regarding use of the precision and false positive rate classifier performance metrics. Aim: To demonstrate and explain why failing to report precision when using data with highly imbalanced class distributions may provide an overly optimistic view of classifier performance. Method: Well documented examples of how dependent class distribution affects the suitability of performance measures. Conclusions: When using data where the minority class represents less than around 5 to 10 percent of data points in total, failing to report precision may be a critical mistake. Furthermore, deriving the precision values omitted from studies can reveal valu...
Most empirical disciplines promote the reuse and sharing of datasets, as it leads to greater poss...
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 Int...
During the last 10 years, hundreds of different defect prediction models have been published. The p...
Background: There has been much discussion amongst automated software defect prediction researchers ...
Software defect prediction research has adopted various evaluation measures to assess the performanc...
Practitioners of data mining and machine learning have long observed that the imbalance of classes i...
Context: Conducting experiments is central to research machine learning research to benchmark, evalu...
IEEE In 2014 we published a meta-analysis of software defect prediction studies [1]. This suggested ...
Background. The ability to predict defect-prone software components would be valuable. Consequently,...
Binary classifiers are routinely evaluated with performance measures such as sensitivity and specifi...
During the last 10 years, hundreds of different defect prediction models have been published. The pe...
Software defect prediction performance varies over a large range. Menzies suggested there is a ceili...
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
Background. The ability to predict defect-prone software components would be valuable. Consequently,...
Context. Reports suggest that defects in code cost the US in excess of $50billion per year to put ri...
Most empirical disciplines promote the reuse and sharing of datasets, as it leads to greater poss...
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 Int...
During the last 10 years, hundreds of different defect prediction models have been published. The p...
Background: There has been much discussion amongst automated software defect prediction researchers ...
Software defect prediction research has adopted various evaluation measures to assess the performanc...
Practitioners of data mining and machine learning have long observed that the imbalance of classes i...
Context: Conducting experiments is central to research machine learning research to benchmark, evalu...
IEEE In 2014 we published a meta-analysis of software defect prediction studies [1]. This suggested ...
Background. The ability to predict defect-prone software components would be valuable. Consequently,...
Binary classifiers are routinely evaluated with performance measures such as sensitivity and specifi...
During the last 10 years, hundreds of different defect prediction models have been published. The pe...
Software defect prediction performance varies over a large range. Menzies suggested there is a ceili...
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
Background. The ability to predict defect-prone software components would be valuable. Consequently,...
Context. Reports suggest that defects in code cost the US in excess of $50billion per year to put ri...
Most empirical disciplines promote the reuse and sharing of datasets, as it leads to greater poss...
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 Int...
During the last 10 years, hundreds of different defect prediction models have been published. The p...