Context

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.

Objective

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.

Method

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.

Results

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.

Conclusion

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.

中文翻译：

---

高效的特征提取模型，可提高软件错误分类系统中重复错误报告检测的验证性能

语境

在各种软件错误分类系统的半结构化软件存储库中，有许多重复的错误报告。在软件分类系统中，重复的错误报告检测（DBRD）过程是一个重大问题。

目的

DBRD问题有很多问题，例如有效的特征提取以准确计算错误报告之间的相似度，建立高性能的重复检测器模型以及处理连续的实时查询。特征提取是一种将非结构化数据转换为结构化数据的技术。这项研究的主要目的是使用特征提取模型来提高DBRD的验证性能。

方法

这项研究着重于特征提取以构建包含所有类型特征的新通用模型。此外，它引入了一种新的特征提取器方法来描述文本之间相似性的新观点。所提出的方法引入了新的文本特征，该特征基于对错误报告的单字和双字形式的文本字段的词频和逆文档频率的汇总。此外，提出了一种新的混合测量度量，用于检测有效特征，从而用于评估所有特征（包括所提出的特征）的效率。

结果

比较了DBRD的验证性能和建议功能和最新功能。为了展示我们模型的有效性，我们将其和其他相关研究应用于Android，Eclipse，Mozilla和Open Office数据集的DBRD并比较了结果。比较结果表明，我们提出的模型通过应用线性回归（LR）和决策树（DT），分别在（i）的准确性和精确度方面分别提高了约2％，在召回和F1措施方面分别提高了4.5％和5.9％）分类器，以及（ii）通过将DT分类器用作最佳分类器，四个指标的性能为91％−99％（平均〜97％）。

结论

我们提出的功能提高了有关运行时性能的DBRD验证性能。预处理方法（主要是阻止方法）可以稍微提高DBRD的验证性能（最高0.3％），但是基于规则的机器学习算法对于DBRD问题更有用。结果表明，我们提出的模型对于采用最新方法有效的数据集（即Mozilla Firefox）和对于采用最新方法效果较差的数据集（即， Android）。结果还表明，所有类型的功能的组合都可以提高DBRD的验证性能，即使对于LR分类器，其验证性能也较差，这对于软件错误分类系统很容易实现。如果不使用最长的公共子序列（LCS）功能，这是有效但费时的，我们提出的功能可以以较低的时间复杂度和运行时开销覆盖LCS的有效性。此外，统计分析表明结果是可靠的，可以推广到其他数据集或类似的分类器。