Today, we depend on numerous large-scale services
\nfor basic operations such as email. These services, built on the
\nbasis of Continuous Integration\/Continuous Deployment (CI\/CD)
\nprocesses, are extremely dynamic: developers continuously com-
\nmit code and introduce new features, functionality and fixes.
\nHundreds of commits may enter the code-base in a single day.
\nTherefore one of the most time-critical, yet resource-intensive
\ntasks towards ensuring code-quality is effectively testing such
\nlarge code-bases.
\nThis paper presents FastLane, a system that performs data-
\ndriven test minimization. FastLane uses light-weight machine-
\nlearning models built upon a rich history of test and commit logs
\nto predict test outcomes. Tests for which we predict outcomes
\nneed not be explicitly run, thereby saving us precious test-
\ntime and resources. Our evaluation on a large-scale email and
\ncollaboration platform service shows that our techniques can
\nsave 18.04%, i.e., almost a fifth of test-time while obtaining a
\ntest outcome accuracy of 99.99%.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 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