DeBGUer: A Tool for Bug Prediction and Diagnosis


  • Amir Elmishali Ben Gurion University of the Negev
  • Roni Stern Ben Gurion University of the Negev
  • Meir Kalech Ben Gurion University of the Negev



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.




How to Cite

Elmishali, A., Stern, R., & Kalech, M. (2019). DeBGUer: A Tool for Bug Prediction and Diagnosis. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9446-9451.



IAAI Technical Track: Emerging Papers