Many existing warning prioritization techniques seek to reorder the static analysis warnings such that true positives are provided first. However, excessive amount of time is required therein to investigate and fix prioritized warnings because some are not actually true positives or are irrelevant to the code context and topic. In this paper, we propose a warning prioritization technique that reflects various latent topics from bug-related code blocks. Our main aim is to build a prioritization model that comprises separate warning priorities depending on the topic of the change sets to identify the number of true positive warnings. For the performance evaluation of the proposed model, we employ a performance metric called warning detection rate, widely used in many warning prioritization studies, and compare the proposed model with other competitive techniques. Additionally, the effectiveness of our model is verified via the application of our technique to eight industrial projects of a real global company.

中文翻译：

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来自软件存储库变更集的基于主题建模的警告优先级

许多现有的警告优先排序技术试图对静态分析警告重新排序，以便首先提供真正的阳性。然而，其中需要过多的时间来调查和修复优先警告，因为有些警告实际上并不是真正的肯定或与代码上下文和主题无关。在本文中，我们提出了一种警告优先级技术，该技术反映了与错误相关的代码块中的各种潜在主题。我们的主要目标是建立一个优先级模型，该模型根据更改集的主题包含单独的警告优先级，以确定真正的正面警告的数量。对于所提出模型的性能评估，我们采用称为警告检测率的性能指标，广泛用于许多警告优先级研究，并将所提出的模型与其他竞争技术进行比较。此外，通过将我们的技术应用于一家真正的跨国公司的八个工业项目，我们的模型的有效性得到了验证。