Inferring test cases from user bug reports'

作为推荐实践的一部分，项目使用问题跟踪器作为用户与开发人员或贡献者之间沟通的平台。这包括请求新功能或报告问题和错误。这些追踪器上的信息是自然语言的，有时会按照推荐的格式或模板进行结构化。然而，它还没有被用来指导测试用例的生成或修复。首先，这个项目的目标是使用这些信息来推断导致错误浮出水面的操作序列，以便可以生成测试用例来确认问题，然后-验证修复。为此，来自自然语言处理的技术应用于以下任务：通过重新创建语料库之间缺失的链接来提高收集数据的质量，例如代码提交和用户错误报告--这是一项通常由领域专家执行的任务；将请求分类为功能或错误；当动作序列以自由形式的叙述呈现时，使用参与者模型来推断动作序列；使用动作序列为正在分析的代码生成或增强测试套件；虽然NLP技术被用于构建项目本体，而这些技术又被用来创建软件构件之间的链接，但这些技术以前并没有直接使用过。理想情况下，这个项目应该自动化作为软件生命周期一部分的典型任务，从而应该减少软件维护的成本。它还应该促使其他研究人员效仿，利用自然语言处理技术挖掘大多数项目中存在的非结构化信息，并以关联数据格式提供软件人工制品语料库，供研究人员解决本文讨论的问题以外的其他问题。

As part of recommended practice, projects employ issue trackers as a platform for communication between users and developers or contributors. This includes the request of new features or the report of issues and bugs. The information on these trackers is in a natural language and sometimes structured per a recommended format or template. However, it is yet untapped to guide the generation or repair of test cases. This project, in the first instance, aims to use this information to deduce action sequences that caused the bug to surface so that test cases can be generated for the confirmation of the issue, and later - the validation of the fix. To do so, techniques from natural language processing should be used for the following tasks: increase the quality of the collected data by recreating missing links between corpora, such as code commits and user bug reports - a task usually performed by a domain expert; classification of requests into features or bugs; using actor models to deduce action sequences when they are presented in a free-form narrative; use action sequences to generate or enhance test suits for the code under analysis;While NLP techniques have been used for the constructions of project ontologies and these in turn employed for creating links between software artefacts, these techniques have not been previously used directly.Ideally, this project should automate a typical task that occurs as part of the software life-cycle and thus should reduce the cost of software maintenance. It should also prompt other researchers to follow suit in tapping into unstructured information that exists in most projects using NLP techniques and should provide corpora of software artefacts in a linked data format for researchers to work with to tackle other questions than the ones considered here.'