Enabling Log Recommendation Through Machine Learning on Source Code

Enabling Log Recommendation Through Machine Learning on Source Code

Mikalauskas, Liudas (TU Delft Electrical Engineering, Mathematics and Computer Science)

Barros Cândido, J. (mentor)
Aniche, Maurício (mentor)
Katsifodimos, A (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2021-07-02

Logging is a common practice in software development that assists developers with the maintenance of software. Logging a system optimally is a challenging task, thus Li et al. have proposed a state-of-the-art log recommendation model. However, no further attempts exist to improve the model or reproduce their results using different training data. In this research, a model was developed using the methods of Li et al. to evaluate its performance when trained on a specific dataset. Some aspects of the model such as feature filtering were studied. It was concluded that the methods of Li et al. are reproducible and can produce a model that performs well with various training data. The study on feature filtering revealed that not filtering features results in an increase of all tested metrics.

Deep learning
Software Engineering
debugging
Software Maintenance
Artificial intelligence
code blocks
logging
logging locations
Neural Network
logging suggestions

http://resolver.tudelft.nl/uuid:c0cb185d-16ae-4829-ac5c-f81b41d6c7aa

Student theses

bachelor thesis

© 2021 Liudas Mikalauskas