Inferring test cases from user bug reports'

从用户错误报告中推断测试用例

• 批准号: 1817557
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1817557
• 关键词: Inferring; test; cases; user; bug

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.'

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的沟通平台。这包括新功能的请求或问题和错误的报告。这些跟踪器上的信息采用自然语言，有时根据推荐的格式或模板进行结构化。然而，它尚未开发，以指导测试用例的生成或修复。该项目的目的是首先使用这些信息来推断导致bug出现的操作序列，以便生成测试用例来确认问题，然后验证修复。要做到这一点，自然语言处理的技术应该用于以下任务：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-通常由领域专家执行的任务;将请求分类为功能或错误;使用演员模型来推断以自由形式叙述的动作序列;使用动作序列来生成或增强被分析代码的测试套件;虽然NLP技术已经被用于项目本体的构建，并且这些本体又被用于在软件工件之间创建链接，但是这些技术以前没有被直接使用。该项目应自动执行作为软件生命周期一部分的典型任务，因此应降低软件维护成本。它还应该促使其他研究人员效仿，利用大多数使用NLP技术的项目中存在的非结构化信息，并应该以链接数据格式提供软件人工制品语料库，供研究人员使用，以解决本文所考虑的问题之外的其他问题。'

项目成果

Flexeme: untangling commits using lexical flows

Flexeme：使用词汇流理清提交

• DOI: 10.1145/3368089.3409693
• 发表时间: 2020
• 作者: Pâr?achi P