Created by W.Langdon from gp-bibliography.bib Revision:1.8081
QuixBugs small Java program. Parse using SrcML. Landscape analysis. Better than prior work on QuixBugs.
Fewer unique patches and much slower (slide 10), efficiency depends on nature of bug.
Future work (slide 15), expect difference between APR and non-function (eg speedup) GI.
Video e8uJf67hL0w Aymeric Blot
12:52 Discussion Chair: Bobby R. Bruce. A: Justyna Petke, Aymeric Blot.
13:13 Q: Westley Weimer, A: Justyna Petke
13:58 Q: Alexander Brownlee, why is delete mutation operator less successful? A: Aymeric Blot depends upon on actual bug fix, only if required fix is a deletion is deletion mutation operator successful.
14:40 Q: Bobby R. Bruce, other mutations may also lead to a deletion (A:yes) A: Aymeric Blot based on mutation type only. Tracking is complex. Reinforcement learning, dynamic mutation type selection or mutation targets (line number in source code).
17:45 Q: Stephanie Forrest, assembly code. A: Aymeric Blot may be no change. Q: Bobby R. Bruce, Syntax not so constraining? Assembly language gives GI more scope? A: Aymeric Blot, Possibly more need to learn which type of mutation to apply than in Java.
20:32 Q: Oliver Krauss only 1/4 of bugs due to deletion A: Aymeric Blot only if fix is deletion, having higher fraction of deletion mutations helps. A: Justyna Petke multiple changes, importance of location of mutations not sufficiently studied. A: Aymeric Blot may depend on APR or non-function (memory reduction) GI
part of \cite{Petke:2021:ICSEworkshop} http://geneticimprovementofsoftware.com/events/icse2021.html",
Genetic Programming entries for Marta Smigielska Aymeric Blot Justyna Petke