Context:

Feedback from end users of software applications is a valuable resource in understanding what users request, what they value, and what they dislike. Information derived from user-feedback can support software evolution activities, such as requirements prioritization. User-feedback analysis is still mostly performed manually by practitioners, despite growing research in automated analysis.

Objective:

We address two issues in automated user-feedback analysis: (i) most of the existing automated analysis approaches that exploit linguistic analysis assume that the vocabulary adopted by users (when expressing feedback) and developers (when formulating requirements) are the same; and (ii) user-feedback analysis techniques are usually experimentally evaluated only on some user-feedback dataset, not involving assessment by potential software developers.

Method:

We propose an approach, ReFeed, that computes, for each requirement, the set of related user-feedback, and from such user-feedback extracts quantifiable properties which are relevant for prioritizing the requirement. The extracted properties are propagated to the related requirements, based on which ranks are computed for each requirement. ReFeed relies on domain knowledge, in the form of an ontology, helping mitigate the gap in the vocabulary of end users and developers. The effectiveness of ReFeed is evaluated on a realistic requirements prioritization scenario in two experiments involving graduate students from two different universities.

Results:

ReFeed is able to synthesize reasonable priorities for a given set of requirements based on properties derived from user-feedback. The implementation of ReFeed and related resources are publicly available.

Conclusion:

The results from our studies are encouraging in that using only three properties of user-feedback, ReFeed is able to prioritize requirements with reasonable accuracy. Such automatically determined prioritization could serve as a good starting point for requirements experts involved in the task of prioritizing requirements Future studies could explore additional user-feedback properties to improve the effectiveness of computed priorities.

中文翻译：

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自动执行用户反馈驱动的需求优先级

语境：

来自软件应用程序最终用户的反馈对于了解用户的要求，他们的价值和他们不喜欢的东西是宝贵的资源。从用户反馈中获得的信息可以支持软件演进活动，例如需求优先级。尽管对自动分析的研究不断增加，但用户反馈分析仍大部分由从业人员手动执行。

客观的：

我们在自动用户反馈分析中解决了两个问题：（i）利用语言分析的大多数现有自动分析方法都假定用户（表达反馈时）和开发人员（制定要求时）所采用的词汇是相同的；（ii）用户反馈分析技术通常仅在某些用户反馈数据集上进行实验评估，而不涉及潜在软件开发人员的评估。

方法：

我们提出一种方法ReFeed，它针对每个需求计算相关的用户反馈集，并从此类用户反馈中提取与确定需求优先级相关的可量化属性。提取的属性将传播到相关需求，根据这些需求为每个需求计算等级。ReFeed依赖于本体形式的领域知识，有助于减轻最终用户和开发人员词汇之间的差距。在两个来自两个不同大学的研究生进行的实验中，在一个现实的需求优先级排序方案上评估了ReFeed的有效性。

结果：

ReFeed能够基于从用户反馈得出的属性，为给定的一组需求综合合理的优先级。ReFeed的实施和相关资源是公开可用的。

结论：

我们的研究结果令人鼓舞，因为ReFeed仅使用用户反馈的三个属性，便能够以合理的准确性确定需求的优先级。这种自动确定的优先级可以为参与需求优先级任务的需求专家提供一个良好的起点。未来的研究可以探索其他用户反馈属性，以提高计算出的优先级的有效性。