Multi-View Feature Fusion Model for Software Bug Repair Pattern Prediction

Abstract

Many search-based Automatic Program Repair (APR) techniques employ a set of repair patterns to generate candidate patches. Regarding repair pattern selection, existing search-based APR techniques either randomly select a repair pattern from the repair pattern set to apply or prioritize all repair patterns based on the bug's context information. In this paper, we introduce PatternNet, a multi-view feature fusion model capable of predicting the repair pattern for a reported software bug. To accomplish this task, PatternNet first extracts multi-view features from the pair of buggy code and bug report using different models. Specifically, a transformer-based model (i.e., UniXcoder) is utilized to obtain the bimodal feature representation of the buggy code and bug report. Additionally, an Abstract Syntax Tree (AST)-based neural model (i.e., ASTNN) is employed to learn the feature representation of the buggy code. Second, a co-attention mechanism is adopted to capture the dependencies between the statement trees in the AST of the buggy code and the textual tokens of the reported bug, resulting in co-attentive features between statement trees and reported bug's textual tokens. Finally, these multi-view features are combined into a unified representation using a feature fusion network. We quantitatively demonstrate the effectiveness of PatternNet and the feature fusion network for predicting software bug repair patterns.