Understanding Automatically-Generated Patches Through Symbolic Invariant Differences

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{DBLP:conf/kbse/CashinMWF19,

• author = "Padraic Cashin and Carianne Martinez and Westley Weimer and Stephanie Forrest",
• title = "Understanding Automatically-Generated Patches Through Symbolic Invariant Differences",
• booktitle = "34th {IEEE/ACM} International Conference on Automated Software Engineering, ASE 2019",
• year = "2019",
• pages = "411--414",
• address = "San Diego, CA, USA",
• month = nov # " 11-15",
• keywords = "genetic algorithms, genetic programming, GenProg, daikon, APR",
• timestamp = "Sun, 19 Jan 2020 15:19:48 +0100",
• biburl = "https://dblp.org/rec/conf/kbse/CashinMWF19.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• URL = "https://doi.org/10.1109/ASE.2019.00046",
• DOI = "doi:10.1109/ASE.2019.00046",
• size = "4 pages",
• abstract = "Developer trust is a major barrier to the deployment of automatically-generated patches. Understanding the effect of a patch is a key element of that trust. We find that differences in sets of formal invariants characterize patch differences and that implication-based distances in invariant space characterize patch similarities. When one patch is similar to another it often contains the same changes as well as additional behaviour; this pattern is well-captured by logical implication. We can measure differences using a theorem prover to verify implications between invariants implied by separate programs. Although effective, theorem provers are computationally intensive; we find that string distance is an efficient heuristic for implication-based distance measurements. We propose to use distances between patches to construct a hierarchy highlighting patch similarities. We evaluated this approach on over 300 patches and found that it correctly categorises programs into semantically similar clusters. Clustering programs reduces human effort by reducing the number of semantically distinct patches that must be considered by over 50percent, thus reducing the time required to establish trust in automatically generated repairs.",

}

Genetic Programming entries for Padraic Cashin Carianne Martinez Westley Weimer Stephanie Forrest

Citations