Hi Qt community,

My name is Eve and I'm a master’s student working with Prof. Shane McIntosh and 
Prof. Mike Godfrey at the University of Waterloo. We're conducting a research 
study on the effectiveness of risk assessment during code review. Specifically, 
we're studying whether risk assessment can improve a reviewer’s awareness of 
risky changes, and if this assessment can affect their performance in 
identifying defects during code review.


To help you better understand who we are and what we are doing, here is the 
link to my page on our lab website: 
https://swag.uwaterloo.ca/members/xueyao-eve-yu.html. This includes the 
investigators’ information as well as the context of our project.


We are performing our study on the Qt codebase and recruiting developers from 
Qt Base to participate in a short user experiment. We’d like to thank those who 
already participated for their interest and time. The experiment is still 
ongoing and we are still looking for new participants.


The experiment will take each participant about 30-60 minutes in total. It will 
be performed online and asynchronously using a simple web application.  Each 
participant will be asked to examine three recent code changes (i.e., commits) 
taken from Qt Base; they will then be asked to rank the commits by perceived 
riskiness and also to perform a code review on each commit.


Once the experiment is complete, we'll share our results with the Qt community 
(and anyone else who is interested).  Additionally, if the experiment goes 
well, we'll release our risk assessment tool as a (free) plug-in to Gerrit.  
We're hoping that this "carrot" might make you feel more positive about taking 
part in the study and furthering research into code review.  If so, please do 
not hesitate to sign up by using the link below.

Sign-up link: https://forms.gle/zG16V9jESHZt6Vvx8


This study has been reviewed by and received ethics clearance from the 
University of Waterloo Research Ethics Board. Please note that all your 
experiment data will be anonymized. You will be assigned a randomly generated 
participation ID to access the experiment and your identifiable information 
will not be collected and used in any dataset or publication.


Best,

Eve

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