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DeBGUer: A Tool for Bug Prediction and Diagnosis

Authors

Amir Elmishali

Ben Gurion University of the Negev


Roni Stern

Ben Gurion University of the Negev


Meir Kalech

Ben Gurion University of the Negev


Proceedings:

No. 1: AAAI-19, IAAI-19, EAAI-20

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 33

Track:

IAAI Technical Track: Emerging Papers

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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.

DOI:

10.1609/aaai.v33i01.33019446


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 33