Efficient feature extraction model for validation performance improvement of duplicate bug report detection in software bug triage systems

There are many duplicate bug reports in the semi-structured software repository of various software bug triage systems. The duplicate bug report detection (DBRD) process is a significant problem in software triage systems. The DBRD problem has many issues, such as efficient feature extraction to calculate similarities between bug reports accurately, building a high-performance duplicate detector model, and handling continuous real-time queries. Feature extraction is a technique that converts unstructured data to structured data. The main objective of this study is to improve the validation performance of DBRD using a feature extraction model. This research focuses on feature extraction to build a new general model containing all types of features. Moreover, it introduces a new feature extractor method to describe a new viewpoint of similarity between texts. The proposed method introduces new textual features based on the aggregation of term frequency and inverse document frequency of text fields of bug reports in uni-gram and bi-gram forms. Further, a new hybrid measurement metric is proposed for detecting efficient features, whereby it is used to evaluate the efficiency of all features, including the proposed ones. The validation performance of DBRD was compared for the proposed features and state-of-the-art features. To show the effectiveness of our model, we applied it and other related studies to DBRD of the Android, Eclipse, Mozilla, and Open Office datasets and compared the results. The comparisons showed that our proposed model achieved (i) approximately 2% improvement for accuracy and precision and more than 4.5% and 5.9% improvement for recall and F1-measure, respectively, by applying the linear regression (LR) and decision tree (DT) classifiers and (ii) a performance of 91%−99% (average ~97%) for the four metrics, by applying the DT classifier as the best classifier. Our proposed features improved the validation performance of DBRD concerning runtime performance. The pre-processing methods (primarily stemming) could improve the validation performance of DBRD slightly (up to 0.3%), but rule-based machine learning algorithms are more useful for the DBRD problem. The results showed that our proposed model is more effective both for the datasets for which state-of-the-art approaches were effective (i.e., Mozilla Firefox) and those for which state-of-the-art approaches were less effective (i.e., Android). The results also showed that the combination of all types of features could improve the validation performance of DBRD even for the LR classifier with less validation performance, which can be implemented easily for software bug triage systems. Without using the longest common subsequence (LCS) feature, which is effective but time-consuming, our proposed features could cover the effectiveness of LCS with lower time-complexity and runtime overhead. In addition, a statistical analysis shows that the results are reliable and can be generalized to other datasets or similar classifiers.