A Taxonomy of Metrics for Software Fault Prediction

In the field of Software Fault Prediction (SFP), researchers exploit software metrics to build predictive models using machine learning and/or statistical techniques. SFP has existed for several decades and the number of metrics used has increased dramatically. Thus, the need for a taxonomy of metrics for SFP arises firstly to standardize the lexicon used in this field so that the communication among researchers is simplified and then to organize and systematically classify the used metrics. In this doctoral symposium paper, I present my ongoing work which aims not only to build such a taxonomy as comprehensive as possible, but also to provide a global understanding of the metrics for SFP in terms of detailed information: acronym(s), extended name, univocal description, granularity of the fault prediction (e.g., method and class), category, and research papers in which they were used.