PreCrash: A Multi-Feature Fusion Approach for Predicting the Reproducibility of Crash Reports

Crash reports are critical for software debugging and maintenance. In software maintenance, developers reproduce a crash to locate the root cause via analyzing the crash report. A crash report is a document that records details of the crash as it occurred. The crash reports cannot be reproduced by developers because numerous crash reports in real-world issue-tracking systems contain incomplete reproduction steps and ambiguous descriptions. Existing works have explored the reproduction of crash reports for Android applications. These works focus on the process of reproducing crashes based on steps to reproduce and stack traces. However, it is a challenging task to manually identify reproducible crash reports from a large number of crash reports that consist of missing reproduction steps. To address this challenge, we propose PreCrash, a multi-feature fusion approach for predicting the reproducibility of a crash report. This approach helps identify reproducible crash reports before developers expend manual effort trying to reproduce the crashes. First, PreCrash automatically extracts 49 features from each crash report, which cover basic metadata, contextual content, comments, and supplementary evidence in crash reports. Second, PreCrash integrates four text representation models, including Word2Vec, GloVe, FastText, and BERT representations. Third, PreCrash employs a TextCNN-based prediction model to capture semantic patterns based on the integrated text representation. We evaluate PreCrash on 4133 crash reports collected from 102 real-world Java and Android projects on GitHub. Experimental results show that PreCrash achieves an accuracy of 0.91 and an F1-score of 0.91. PreCrash outperforms traditional machine learning models and single text representation baselines.