@inproceedings{bhagwan2019fastlane,
author = {Bhagwan, Ranjita and Philip, Adithya and Kumar, Rahul and Maddila, Chandra and Nagappan, Nachi},
title = {FastLane: Test Minimization for Rapidly Deployed Large-scale Online Services},
organization = {IEEE CS and ACM},
booktitle = {International Conference on Software Engineering},
year = {2019},
month = {May},
abstract = {Today, we depend on numerous large-scale services
for basic operations such as email. These services, built on the
basis of Continuous Integration/Continuous Deployment (CI/CD)
processes, are extremely dynamic: developers continuously com-
mit code and introduce new features, functionality and fixes.
Hundreds of commits may enter the code-base in a single day.
Therefore one of the most time-critical, yet resource-intensive
tasks towards ensuring code-quality is effectively testing such
large code-bases.
This paper presents FastLane, a system that performs data-
driven test minimization. FastLane uses light-weight machine-
learning models built upon a rich history of test and commit logs
to predict test outcomes. Tests for which we predict outcomes
need not be explicitly run, thereby saving us precious test-
time and resources. Our evaluation on a large-scale email and
collaboration platform service shows that our techniques can
save 18.04%, i.e., almost a fifth of test-time while obtaining a
test outcome accuracy of 99.99%.},
url = {http://approjects.co.za/?big=en-us/research/publication/fastlane-test-minimization-for-rapidly-deployed-large-scale-online-services/},
}