Developer forums are essential for software engineers to solve their problems with the assistance of experts on such forums. However, sometimes the solutions (answers) of a problem are not satisfactory or challenging to select the potential answer. Information seekers usually browse all the answers within the question thread to get the potential answer. The manual selection of correct answers is a tedious and time-consuming task. In this paper, we propose an automatic classification approach to predict the correct answers for developer forums. We first extract the metadata and combination of Q/A for each thread of the developer community ( Stack Overflow ). Then, the natural language processing techniques are applied to preprocess the Q/A combinations of the given dataset. After that, a keyword ranking algorithm is leveraged to extract keywords and their ranking scores for each Q/A combination. Based on keywords and their ranking scores for each Q/A combination, a keywords-based feature vector is constructed. Subsequently, word embedding is leveraged to convert each preprocessed Q/A combination into a text-based feature vector. Finally, we pass the metadata, keywords-based features, and text-based features to the ensemble deep learning model for training to predict correct answers. The results of 10-fold cross-validation specify that the proposed approach is accurate and surpasses the state-of-the-art. On average, it improves the accuracy , precision , recall , and f-measure up to 1.72% , 24.96% , 6.57% , and 16.62% , respectively.

中文翻译：

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面向开发者论坛的基于深度学习的正确答案预测

开发人员论坛对于软件工程师在此类论坛上的专家的帮助下解决他们的问题至关重要。然而，有时问题的解决方案（答案）并不令人满意或难以选择潜在答案。信息寻求者通常浏览问题线程中的所有答案以获得潜在的答案。手动选择正确答案是一项繁琐且耗时的任务。在本文中，我们提出了一种自动分类方法来预测开发者论坛的正确答案。我们首先为开发者社区的每个线程提取元数据和 Q/A 组合（ 堆栈溢出 ）。然后，应用自然语言处理技术对给定数据集的 Q/A 组合进行预处理。之后，利用关键字排名算法为每个 Q/A 组合提取关键字及其排名分数。根据每个 Q/A 组合的关键字及其排名分数，构建基于关键字的特征向量。随后，利用词嵌入将每个预处理的 Q/A 组合转换为基于文本的特征向量。最后，我们将元数据、基于关键字的特征和基于文本的特征传递给集成深度学习模型进行训练以预测正确答案。10 折交叉验证的结果表明，所提出的方法是准确的，并且超越了最新技术。平均而言，它提高了准确性 ， 精确 ， 回忆，和 f-测量高达 1.72% , 24.96% , 6.57% 和 分别为 16.62%。