Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Fri 15 Nov 2019 11:30 - 12:00 at Cortez 3 - Software Engineering Intelligence via NLP

Software defect prediction is still a challenging task in industrial settings. Noisy data and/or lack of data make it hard to build successful prediction models. In this study, we aim to build a change-level defect prediction model for a software project in an industrial setting. We combine various probabilistic models, namely matrix factorization and topic modeling, with the expectation of overcoming the noisy and limited nature of industrial settings by extracting hidden features from multiple resources. Commit level process metrics, latent features from commits, and semantic features from commit messages are combined to build the defect predictors with the use of log filtering and feature selection techniques, and two machine learning algorithms Naive Bayes and Extreme Gradient Boosting (XGBoost). Statistical tests show that collecting data from various sources and applying data pre-processing techniques show an improvement in terms of probability of detection by up to 24% when compared to a base model with process metrics only.

Fri 15 Nov

11:00 - 12:30: SEI 2019 - Software Engineering Intelligence via NLP at Cortez 3
SEI-2019-papers11:00 - 11:30
Mining Text in Incident Repositories: Experiences and Perspectives on Adopting Machine Learning Solutions in Practice.
SEI-2019-papers11:30 - 12:00
Predicting Defects with Latent and Semantic Features from Commit Logs in an Industrial Setting.
SEI-2019-papers12:00 - 12:30
Where Does LDA Sit for GitHub?