{'fraction_non_alphanumeric': 0.022810890360559236, 'fraction_numerical': 0.0029433406916850625, 'mean_word_length': 5.210045662100456, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Bug Report Classification Using LSTM Architecture for More Accurate Software Defect Locating

Recently many information retrieval (IR)-based approaches have been proposed to help locate software defects automatically by using information from bug report contents. However, some bug reports that do not semantically related to the relevant code are not helpful to IR-based systems. Running an IR-based system on these reports may produce false positives. In this paper, we propose a classification model for classifying a bug report as either helpful or unhelpful using a LSTM-network. By filtering our unhelpful reports before running an IR-based bug locating system, our approach helps reduce false positives and improve the ranking performance. We test our model over 9,000 bug reports from three software projects. The evaluation result shows that our model helps improve a state-of-the-art IR-based system's ranking performance under a trade-off between the precision and the recall. Our comparison experiments show that the LSTM-network achieves the best trade-off between precision and recall than other classification models including CNN, multilayer perceptron, and a simple baseline approach that classifies a bug report based its length. In the situation that precision is more important than recall, our classification model helps for bug locating.