- author = "Claire {Le Goues} and Michael Pradel and Abhik Roychoudhury",
- title = "Automated Program Repair",
- journal = "Communications of the ACM",
- year = "2019",
- volume = "62",
- number = "12",
- pages = "56--65",
- month = dec,
- keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Bugs, Symbolic Execution, Learning",
- ISSN = "0001-0782",
-
URL = "
http://software-lab.org/publications/cacm2019_program_repair.pdf",
-
DOI = "
doi:10.1145/3318162",
-
video_url = "
https://youtu.be/MGpJEa8makw",
- size = "10 pages",
- abstract = "Automated program repair can greatly relieve programmers from the burden of manually fixing the ever increasing number of programming mistakes. At the same time, achieving such a goal involves solving technical challenges in scalability, patch quality, and integration into developer work flows. This article presents an overview of the state-of-the-art tools and techniques in automated program repair. We also take a forward looking view of the area by presenting emerging and potential use cases for program repair, such as on-line programming education and patching of security vulnerabilities.",
-
notes = "Key Insights
Automated program repair is an emerging and exciting field of research that allows for automated rectification of software errors and vulnerabilities.
The uses of automated program repair can be myriad, such as improving programmer productivity, automated fixing of security vulnerabilities, self-healing software for autonomous devices, and automatically generating hints for solving programming assignments.
Automated repair can benefit from various techniques: intelligent navigation over a search space of program edits, symbolic reasoning to synthesize suitable code fragments, and techniques that learn from existing code and patches.
See also \cite{Rinard:2020:ACM}",
