- author = "Giovani Guizzo and Justyna Petke and Federica Sarro and Mark Harman",
- title = "Enhancing Genetic Improvement of Software with Regression Test Selection",
- booktitle = "Proceedings of the International Conference on Software Engineering, ICSE 2021",
- year = "2021",
- editor = "Arie {van Deursen} and Tao Xie and Natalia Juristo Oscar Dieste",
- pages = "1323--1333",
- address = "Madrid",
- month = "25-28 " # may,
- publisher = "IEEE",
- note = "Winner ACM SIGSOFT Distinguished Artifact Award",
- keywords = "genetic algorithms, genetic programming, Genetic Improvement, SBSE, AI, Regression Test Selection, RTS, Search Based Software Engineering, Java, Gin, Ekstazi, STARTS, non-functional improvement, optimise runtime",
-
URL = "
https://bit.ly/Guizzo-ICSE-2021",
- urk = "http://www.cs.ucl.ac.uk/staff/F.Sarro/resource/papers/ICSE21Guizzo.pdf",
-
URL = "https://conf.researchr.org/details/icse-2021/icse-2021-papers/6/Enhancing-Genetic-Improvement-of-Software-with-Regression-Test-Selection",
-
URL = "
https://discovery.ucl.ac.uk/id/eprint/10120263/1/Petke_correctedICSE21Guizzo.pdf",
-
URL = "https://research.fb.com/wp-content/uploads/2021/04/Enhancing-Genetic-Improvement-of-Software-with-Regression-Test-Selection-.pdf",
-
URL = "https://research.facebook.com/publications/enhancing-genetic-improvement-of-software-with-regression-test-selection/",
-
URL = "https://discovery.ucl.ac.uk/id/eprint/10120263/",
-
DOI = "
doi:10.1109/ICSE43902.2021.00120",
-
video_url = "https://www.youtube.com/watch?v=4cxbTB8yF_M",
-
code_url = "https://doi.org/10.5522/04/12890792",
- size = "11 pages",
-
abstract = "Genetic improvement uses artificial intelligence to
automatically improve software with respect to
non-functional properties (AI for SE).
we propose the use of existing software engineering best practice to enhance Genetic Improvement (SE for AI). We conjecture that existing Regression Test Selection (RTS) techniques (which have been proven to be efficient and effective) can and should be used as a core component of the GI search process for maximising its effectiveness.
To assess our idea, we have carried out a thorough empirical study assessing the use of both dynamic and static Regression Test Selection techniques with GI to improve seven real-world software programs.
The results of our empirical evaluation show that incorporation of Regression Test Selection within GI significantly speeds up the whole GI process, making it up to 78percent faster on our benchmark set, being still able to produce valid software improvements.
Our findings are significant in that they can save hours to days of computational time, and can facilitate the uptake of GI in an industrial setting, by significantly reducing the time for the developer to receive feedback from such an automated technique. Therefore, we recommend the use of Regression Test Selection in future test-based automated software improvement work. Finally, we hope this successful application of SE for AI will encourage other researchers to investigate further applications in this area.",
-
notes = "Also known as \cite{9401972}
'we recommend the use of RTS in future test-based automated software improvement work'
Relative Safety measure. Relative Improvement Change (RIC). Perfect improvement v. fast improvement v. diverse improvement.
7 Java (from Apache Commons project) libraries: codec-1.14, compress-1.20, csv-1.71, fileupload-1.4, imaging-1.0, text-1.3, validator-1.6
almost all patches are valid. almost 100percent safety.
Replication package https://figshare.com/s/440c2105bad3259bda6f \cite{Guizzo:2021:ICSEcomp}
See also \cite{Guizzo:2024:TOSEM} Tue 25 May 2021 11:45 - 12:05 at Blended Sessions Room 2 - 1.2.2. Search-Based SE & Genetic Operations
Video 4cxbTB8yF_M includes discussion at ICSE-2021
University College London",
