2008 | OriginalPaper | Buchkapitel
Predicting Bugs from History
verfasst von : Thomas Zimmermann, Nachiappan Nagappan, Andreas Zeller
Erschienen in: Software Evolution
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Version and bug databases contain a wealth of information about software failures— how the failure occurred, who was affected, and how it was fixed. Such defect information can be automatically mined from software archives; and it frequently turns out that some modules are far more defect-prone than others. How do these differences come to be? We research how code properties like (a) code complexity, (b) the problem domain, (c) past history, or (d) process quality affect software defects, and how their correlation with defects in the past can be used to predict future software properties—where the defects are, how to fix them, as well as the associated cost.