- author = "Ibrahim Mesecan and Michael C. Gerten and James I. Lathrop and Myra B. Cohen and Tomas {Haddad Caldas}",
- title = "{CRNRepair}: Automated Program Repair of Chemical Reaction Networks",
- booktitle = "GI @ ICSE 2021",
- year = "2021",
- month = "30 " # may,
- editor = "Justyna Petke and Bobby R. Bruce and Yu Huang and Aymeric Blot and Westley Weimer and W. B. Langdon",
- publisher = "IEEE",
- address = "internet",
- pages = "23--30",
- note = "best paper",
- keywords = "genetic algorithms, genetic programming, genetic improvement, program repair, APR, chemical reaction networks, PyGGI, XML, Matlab, Tabu search, Simulated Annealing, ChemTest, SBML",
- isbn13 = "978-1-6654-4466-8/21",
-
code_url = "
https://github.com/LavaOps/CRNRepair/",
-
URL = "
https://geneticimprovementofsoftware.com/paper_pdfs/gi2021icse/mesecan_gi-icse_2021.pdf",
-
video_url = "https://www.youtube.com/watch?v=SjNsbERd6e0&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=15",
-
video_url = "https://www.youtube.com/watch?v=AtwAfPwdfJ4&list=PLI8fiFpB7BoKDaxvS7SQp0iA7fN7rrvDD&index=16",
-
video_url = "https://www.youtube.com/watch?v=YD-JfwQOEiY&list=PLXTjhGKkSnI-se7uQneCX-pEDiDrQ7TIS&index=3",
-
DOI = "
doi:10.1109/GI52543.2021.00014",
- size = "8 pages",
- abstract = "Chemical reaction networks (CRNs) are abstractions of distributed networks that form the foundations of many natural phenomena such as biological processes. These can be encoded and/or compiled into DNA and have been shown to be Turing complete. Before CRNs are implemented in a physical environment, they are often simulated in programming environments. Like traditional programs, these CRN programs must be validated. Researchers have recently designed a software testing framework for CRNs, however, repairing CRN programs is still a manual task. While the programs are often small in size, finding and repairing the faults can be difficult without automated support. we present CRNRepair, a program repair framework for CRN programs. We built our framework on top of an existing APR framework. We use a testing infrastructure built in the Matlab SimBiology package and adapt it to use the SBML representation for its abstract syntax tree. In a case study on 19 mutant versions of 2 programs, we find plausible patches for 90 percent of one of the programs, and 50 percent of the other. We find several common types of repairs, which differ from the correct programs, but are functionally correct.",
-
notes = "Video YD-JfwQOEiY Ibrahim Mesecan (Iowa).
15:15 Discussion chair: Aymeric Blot.
15:24 Q: W. B. Langdon, A: Ibrahim Mesecan, benchmark used in Michael C. Gerten et al 2020. 60percent patches like human, 38 percent not human like, eg speed up. A: Michael C. Gerten mutation set.
18:23 Q: Aymeric Blot testing without simulation? A: Ibrahim Mesecan yes (some common errors), Michael C. Gerten.
21:16 Westley Weimer, fitness, A: plateus a problem.
22:39 Justyna Petke state of the art + size of search state, A: 1st work using testing, some work with model checking but scales badly. Search spaces grows fast.
24:05 Q: open source, A: yes (eventually).
part of \cite{Petke:2021:ICSEworkshop} http://geneticimprovementofsoftware.com/events/icse2021.html",
