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[[Machine learning]] techniques can improve the effectiveness of automatic bug-fixing systems.<ref name="prophet" /> One example of such techniques learns from past successful patches from human developers collected from [[open-source software|open source]] [[software repository|repositories]] in [[GitHub]] and [[SourceForge]].<ref name="prophet" /> It then use the learned information to recognize and prioritize potentially correct patches among all generated candidate patches.<ref name="prophet" /> Alternatively, patches can be directly mined from existing sources. Example approaches include mining patches from donor applications<ref name="codephage" /> or from QA web sites.<ref name="QAFix" />
Getafix<ref name=":0">{{Cite journal |last1=Bader |first1=Johannes |last2=Scott |first2=Andrew |last3=Pradel |first3=Michael |last4=Chandra |first4=Satish |date=2019-10-10 |title=Getafix: learning to fix bugs automatically |journal=Proceedings of the ACM on Programming Languages |volume=3 |issue=OOPSLA |pages=159:1–159:27 |doi=10.1145/3360585|doi-access=free |arxiv=1902.06111 }}</ref> is a language-agnostic approach developed and used in production at [[Facebook, Inc.|Facebook]]. Given a sample of [[Commit (version control)|code commits]] where engineers fixed a certain kind of bug, it learns human-like fix patterns that apply to future bugs of the same kind. Besides using Facebook's own [[Repository (version control)|code repositories]] as training data, Getafix learnt some fixes from [[open source]] Java repositories. When new bugs get detected, Getafix applies its previously learnt patterns to produce candidate fixes and ranks them within seconds. It presents only the top-ranked fix for final validation by tools or an engineer, in order to save resources and ideally be so fast that no human time was spent on fixing the same bug, yet.
=== Template-based repair ===
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