Name
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 commit 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%.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICSE.2019.00054 | Proceedings of the 41st International Conference on Software Engineering |
Keywords | DocType | ISSN |
commit risk, machine learning, test prioritization | Conference | 0270-5257 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adithya Abraham Philip | 1 | 1 | 1.03 |
| ranjita bhagwan | 2 | 833 | 66.26 |
| Rahul Kumar | 3 | 26 | 7.97 |
| Chandra Shekhar Maddila | 4 | 18 | 4.79 |
| Nachiappan Nagappan | 5 | 4602 | 190.30 |