In bug fixing process, estimating the ‘Time to Fix Bug’ is one of the factors that helps the triager to allocate jobs in a better way. Due to the limitation of resources for bug fixing, the bugs with long fixing time must be identified, as soon as possible, after receiving the report. This helps the prioritisation and fixing process of the bug reports. In the process of bug fixing, a temporal sequence of activities is done. Each activity is represented by a term. Useful semantic information and long-term dependency are available between terms in the sequence, but it is usually underutilised by existing bug fixing time predictor approaches. This work presents a novel deep learning-based model (called DeepLSTMPred) that (i) converts constituent terms to a vector of real numbers by considering their semantic meaning, (ii) finds the long-term dependencies between terms by deep long short term memory (LSTM) and (iii) classifies sequences to short fixing time or long fixing time. DeepLSTMPred is evaluated on bug reports extracted from the Mozilla project. The results show that the proposed method has better performance in comparison with a state-of-the-art approach (that is the hidden Markov-based model). The experimental results show that DeepLSTMPred achieves 15–20% improvement in terms of accuracy, precision, f -score, and recall.

中文翻译：

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使用单词嵌入和深层的长期短期记忆预测错误修复时间

在错误修复过程中，估算“修复错误的时间”是帮助分类人员以更好的方式分配作业的因素之一。由于错误修复的资源有限，必须在收到报告后尽快识别出修复时间长的错误。这有助于确定错误报告的优先级和修复过程。在错误修复过程中，按时间顺序进行了一系列活动。每个活动都用一个术语表示。序列中各个术语之间可以使用有用的语义信息和长期依赖关系，但是现有的错误修复时间预测器方法通常未充分利用该信息。这项工作提出了一个新颖的基于深度学习的模型（称为DeepLSTMPred），该模型（i）通过考虑构成项的语义含义将其转换为实数向量，（ii）通过深长的短期记忆（LSTM）查找术语之间的长期依赖性，并且（iii）将序列分类为短固定时间或长固定时间。DeepLSTMPred在从Mozilla项目提取的错误报告中进行评估。结果表明，与最新方法（基于隐马尔可夫模型）相比，该方法具有更好的性能。实验结果表明，DeepLSTMPred在准确性，精确度，结果表明，与最新方法（基于隐马尔可夫模型）相比，该方法具有更好的性能。实验结果表明，DeepLSTMPred在准确性，精确度，结果表明，与最新方法（基于隐马尔可夫模型）相比，该方法具有更好的性能。实验结果表明，DeepLSTMPred在准确性，精确度，F -得分，并回想一下。