DeBGUer: A Tool for Bug Prediction and Diagnosis
Abstract
In this paper, we present the DeBGUer tool, a web-based tool for prediction and isolation of software bugs. DeBGUer is a partial implementation of the Learn, Diagnose, and Plan (LDP) paradigm, which is a recently introduced paradigm for integrating Artificial Intelligence (AI) in the software bug detection and correction process. In LDP, a diagnosis (DX) algorithm is used to suggest possible explanations – diagnoses – for an observed bug. If needed, a test planning algorithm is subsequently used to suggest further testing. Both diagnosis and test planning algorithms consider a fault prediction model, which associates each software component (e.g., class or method) with the likelihood that it contains a bug. DeBGUer implements the first two components of LDP, bug prediction (Learn) and bug diagnosis (Diagnose). It provides an easy-to-use web interface, and has been successfully tested on 12 projects.
Cite
Text
Elmishali et al. "DeBGUer: A Tool for Bug Prediction and Diagnosis." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33019446Markdown
[Elmishali et al. "DeBGUer: A Tool for Bug Prediction and Diagnosis." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/elmishali2019aaai-debguer/) doi:10.1609/AAAI.V33I01.33019446BibTeX
@inproceedings{elmishali2019aaai-debguer,
title = {{DeBGUer: A Tool for Bug Prediction and Diagnosis}},
author = {Elmishali, Amir and Stern, Roni and Kalech, Meir},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {9446-9451},
doi = {10.1609/AAAI.V33I01.33019446},
url = {https://mlanthology.org/aaai/2019/elmishali2019aaai-debguer/}
}