TÜ arvutiteaduse instituudi lõputööde teemade register

Organisatsiooni nimi

Computational Neuroscience Lab

Kokkuvõte

Code 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 aasta

2017-2018

Juhendaja

Tambet Matiisen, Vesal Vojdani, Kristjan Sägi, Triin Kask

Suhtlemiskeel(ed)

eesti keel, inglise keel

Nõuded kandideerijale

Tase

Magister

Märksõnad

#unit_tests #reinforcement_learning

 

Nimi

Tambet Matiisen

Tel

+3725286457

E-mail