Software applications often receive a large number of enhancement requests that suggest developers to fulfill additional functions. Such requests are usually checked manually by the developers, which is time consuming and tedious. Consequently, an approach that can automatically predict whether a new enhancement report will be approved is beneficial for both the developers and enhancement suggesters. With the approach, according to their available time, the developers can rank the reports and thus limit the number of reports to evaluate from large collection of low quality enhancement requests that are unlikely to be approved. The approach can help developers respond to the useful requests more quickly. To this end, we propose a multinomial naive Bayes based approach to automatically predict whether a new enhancement report is likely to be approved or rejected. We acquire the enhancement reports of open-source software applications from Bugzilla for evaluation. Each report is preprocessed and modeled as a vector. Using these vectors with their corresponding approval status, we train a Bayes based classifier. The trained classifier predicts approval or rejection of the new enhancement reports. We apply different machine learning and neural network algorithms, and it turns out that the multinomial naive Bayes classifier yields the highest accuracy with the given dataset. The proposed approach is evaluated with 40,000 enhancement reports from 35 open source applications. The results of tenfold cross validation suggest that the average accuracy is up to 89.25%.

中文翻译：

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软件增强请求的自动批准预测

软件应用程序通常会收到大量建议开发人员完成附加功能的增强请求。此类请求通常由开发人员手动检查，既费时又乏味。因此，一种可以自动预测新增强报告是否会被批准的方法对开发人员和增强建议者都是有益的。通过该方法，开发人员可以根据他们的可用时间对报告进行排名，从而限制要从不太可能被批准的大量低质量增强请求中评估的报告数量。该方法可以帮助开发人员更快地响应有用的请求。为此，我们提出了一种基于多项式朴素贝叶斯的方法来自动预测新的增强报告是否可能被批准或拒绝。我们从 Bugzilla 获取开源软件应用程序的增强报告进行评估。每个报告都经过预处理并建模为一个向量。使用这些向量及其相应的批准状态，我们训练一个基于贝叶斯的分类器。经过训练的分类器预测批准或拒绝新的增强报告。我们应用了不同的机器学习和神经网络算法，结果证明多项式朴素贝叶斯分类器对给定的数据集产生了最高的准确度。建议的方法通过来自 35 个开源应用程序的 40,000 份增强报告进行评估。十倍交叉验证的结果表明平均准确率高达89.25%。