IdentiBug: Model-Driven Visualization of Bug Reports by Extracting Class Diagram Excerpts
Gelareh Meidanipour Lahijany, Manuel Ohrndorf, Johannes Zenkert, Madjid Fathi, Udo Kelte · 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2021
Bug reports are essential software artifacts that describe software bugs using natural language. Bug localization tools can help developers to understand the relation between bug reports and a software system. However, most approaches for localizing bugs work with unstructured textual information from the source codes and bug reports. This paper proposes an approach for locating and visualizing bug reports based on class diagrams representing the overall structural design of a software system. Our approach called IdentiBug takes advantage of deep learning techniques to train our bug localization model to predict connections between a bug report and the system’s class diagram. The result is a ranked list of classes from which we extract and rank a list of class diagram excerpts for assisting the developers during bug documentation and localization.