Sravya_Sravya.pdf (1.07 MB)
Download file

On Empirically Examining The Effectiveness Of Deep Learning-Based Bug Localization Models

Download (1.07 MB)
thesis
posted on 22.05.2021, 16:15 by Sravya Sravya, Andriy Miranskyy, Ayse Bener
Software Bug Localization involves a significant amount of time and effort on the part of the software developer. Many state-of-the-art bug localization models have been proposed in the past, to help developers localize bugs easily. However, none of these models meet the adoption thresholds of the software practitioner. Recently some deep learning-based models have been proposed, that have been shown to perform better than the state-of-the-art models. With this motivation, we experiment on Convolution Neural Networks (CNNs) to examine their effectiveness in localizing bugs. We also train a SimpleLogistic model as a baseline model for our experiments. We train both our models on five open source Java projects and compare their performance across the projects. Our experiments show that the CNN models perform better than the SimpleLogistic models in most of the cases, but do not meet the adoption criteria set by the practitioners.

History

Language

eng

Degree

Master of Science

Program

Computer Science

Granting Institution

Ryerson University

LAC Thesis Type

Thesis