An extensive comparison of bug prediction approaches
Università della Svizzera italiana · University of Chile
Abstract
Reliably predicting software defects is one of software engineering's holy grails. Researchers have devised and implemented a plethora of bug prediction approaches varying in terms of accuracy, complexity and the input data they require. However, the absence of an established benchmark makes it hard, if not impossible, to compare approaches. We present a benchmark for defect prediction, in the form of a publicly available data set consisting of several software systems, and provide an extensive comparison of the explanative and predictive power of well-known bug prediction approaches, together with novel approaches we devised. Based on the results, we discuss the performance and stability of the approaches…
Citation impact
- FWCI
- 50.67
- Percentile
- 100%
- References
- 34
Authors
3Topics & keywords
- Benchmark (surveying)
- Computer science
- Software bug
- Software
- Set (abstract data type)
- Data mining
- Predictive modelling
- Machine learning