RT Journal Article
JF 2013 35th International Conference on Software Engineering (ICSE)
YR 2013
VO 00
SP 802
TI Automatic patch generation learned from human-written patches
A1 Dongsun Kim,
A1 Jaechang Nam,
A1 Jaewoo Song,
A1 Sunghun Kim, K1 Fault location
K1 Computer bugs
K1 Context
K1 Semantics
K1 Manuals
K1 Arrays
K1 Maintenance engineering

AB Patch generation is an essential software maintenance task because most software systems inevitably have bugs that need to be fixed. Unfortunately, human resources are often insufficient to fix all reported and known bugs. To address this issue, several automated patch generation techniques have been proposed. In particular, a genetic-programming-based patch generation technique, GenProg, proposed by Weimer et al., has shown promising results. However, these techniques can generate nonsensical patches due to the randomness of their mutation operations. To address this limitation, we propose a novel patch generation approach, Pattern-based Automatic program Repair (Par), using fix patterns learned from existing human-written patches. We manually inspected more than 60,000 human-written patches and found there are several common fix patterns. Our approach leverages these fix patterns to generate program patches automatically. We experimentally evaluated Par on 119 real bugs. In addition, a user study involving 89 students and 164 developers confirmed that patches generated by our approach are more acceptable than those generated by GenProg. Par successfully generated patches for 27 out of 119 bugs, while GenProg was successful for only 16 bugs.
PB IEEE Computer Society, [URL:http://www.computer.org]
LA English
DO 10.1109/ICSE.2013.6606626
LK http://doi.ieeecomputersociety.org/10.1109/ICSE.2013.6606626