A Taxonomy of Metrics for Software Fault Prediction

摘要

Researchers in the field of Software Fault Prediction (SFP) make use of software metrics to build predictive models, for example, by means of machine learning and statistical techniques. The number of metrics used for SFP has increased dramatically in the last few decades. Therefore, a taxonomy of metrics for SFP could be useful to standardize the lexicon, to simplify the communication among researchers/practitioners, and to organize and classify such metrics. In this research, we built a taxonomy of metrics for SFP with the aim of making it as comprehensive as possible. We exploited and extended two Systematic Literature Reviews (SLRs) to collect and classify a total of 512 metrics for SFP and then to build our taxonomy. We also provide information on the metrics in this taxonomy in terms of: acronym(s), extended name, description, granularity of the prediction, category, and research papers in which they were used. To allow the taxonomy to be constantly updated over time, we provide external contributors the possibility to ask for changes via pull-requests on GitHub.