On Usefulness of the Deep-Learning-Based Bug Localization Models to Practitioners
Sravya Polisetty, Andriy Miranskyy, Ayşe Bener · 2019
Background: Developers spend a significant amount of time and effort to localize bugs. In the literature, many researchers proposed state-of-the-art bug localization models to help developers localize bugs easily. The practitioners, on the other hand, expect a bug localization tool to meet certain criteria, such as trustworthiness, scalability, and efficiency. The current models are not capable of meeting these criteria, making it harder to adopt these models in practice. Recently, deep-learning-based bug localization models have been proposed in the literature. They show a better performance than the state-of-the-art models.