- author = "Yu Huang and Kevin Leach and Zohreh Sharafi and Nicholas McKay and Tyler Santander and Westley Weimer",
- title = "Biases and Differences in Code Review using Medical Imaging and Eye-Tracking: Genders, Humans, and Machines",
- booktitle = "Proceedings of the 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2020",
- year = "2020",
- editor = "Myra Cohen and Thomas Zimmermann",
- pages = "456--468",
- address = "Virtual Event, USA",
- month = "8--13 " # nov,
- publisher = "ACM",
- keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, APR, automatic program repair, code review, sex bias, fMRI, gender, eye-tracking, automation",
- isbn13 = "9781450370431",
-
URL = "
https://2020.esec-fse.org/details/fse-2020-papers/114/Biases-and-Differences-in-Code-Review-using-Medical-Imaging-and-Eye-Tracking-Genders",
-
URL = "
http://www-personal.umich.edu/~yhhy/index_files/bias_code_review_FSE20.pdf",
-
URL = "https://doi.org/10.1145/3368089.3409681",
-
DOI = "
10.1145/3368089.3409681",
-
video_url = "https://youtu.be/Z_fKVMkd2Dk",
- size = "13 pages",
- abstract = "Code review is a critical step in modern software quality assurance,yet it is vulnerable to human biases. Previous studies have clarified the extent of the problem, particularly regarding biases against the authors of code, but no consensus understanding has emerged.Advances in medical imaging are increasingly applied to software engineering, supporting grounded neurobiological explorations of computing activities, including the review, reading, and writing of source code. In this paper, we present the results of a controlled experiment using both medical imaging and also eye tracking to investigate the neurological correlates of biases and differences between genders of humans and machines (e.g., automated program repair tools) in code review. We find that men and women conduct code reviews differently, in ways that are measurable and supported by behaviour, eye-tracking and medical imaging data. We also find biases in how humans review code as a function of its apparent author, when controlling for code quality. In addition to advancing our fundamental understanding of how cognitive biases relate to the code review process, the results may inform subsequent training and tool design to reduce bias",
-
notes = "Not on GP, but human bias against machine generated
code may be of interest.
Human (37 students) bias against machine generated code patches.
Data: https://web.eecs.umich.edu/~weimerw/fmri.html
Thu 12 Nov 2020 01:35 - 01:36 at Virtual room 1 - Community.
Univ. of Michigan Ann Arbor, MI, USA",
