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Generating unit tests using reinforcement learning
Organisatsiooni nimiComputational Neuroscience Lab
KokkuvõteCode coverage is an important metric for measuring the quality of unit tests. The idea of this project is to study if reinforcement learning could be used to improve code coverage. Recurrent neural network is used to parse code and generate function calls. Reinforcement learning is used to improve the network, taking number of lines covered as a reward. The student has to be familiar with neural networks for text processing and policy gradient method for reinforcement learning.
Lõputöö kaitsmise aasta2017-2018
JuhendajaTambet Matiisen, Vesal Vojdani, Kristjan Sägi, Triin Kask
Suhtlemiskeel(ed)eesti keel, inglise keel
Nõuded kandideerijale
Tase Magister
Märksõnad #unit_tests #reinforcement_learning
Kandideerimise kontakt
Nimi Tambet Matiisen
Tel +3725286457