
We investigated a different mode of using the prediction model to identify the files associated with a fixed percentage of the faults. The tester could ask the tool to identify which files are likely to contain the bulks of faults, with the tester selecting any desired percentage of faults. Again the tool would return a list ordered in decreasing order of the predicted numbers of faults in the files the model expects to be most problematic. If the number of files identified is too large, the tester could reselect a smaller percentage of faults. This would make the number of files requiring particular scrutiny manageable. We expect both modes to be valuable to professional software testers and developers.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 3 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
