测试用例优先级排序的信息属性分类法：适用性，机器学习
A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learning
摘要：
大多数软件公司都有大量的测试套件，并不断地重新运行其中的一部分，以确保最近的更改没有负面影响。由于测试套件的执行成本很高，行业需要测试用例优先级（TCP）的方法。最近，TCP方法使用机器学习（ML）来利用关于被测系统（SUT）及其测试用例的已知信息。然而，应就收集信息的成本对基于ML的TCP方法的附加值进行严格评估。本文分析了20年的TCP研究，并提出了一个分类的91个信息属性已被使用。根据属性的信息源及其提取过程的特征对属性进行分类。在此基础上，从信息可用性、属性组合和适用于ML的数据特征定义等方面分析了基于工业数据验证的TCP方法和应用ML的TCP方法。依赖于大量的信息属性，假设容易访问SUT代码和简化的测试环境被确定为可能阻碍基于ML的TCP的工业适用性的因素。TePIA分类法提供了一个参考框架，用于统一术语和评估考虑信息属性成本效益的替代方案。
英文摘要：
Most software companies have extensive test suites and re-run parts of them continuously to ensure recent changes have no adverse effects. Since test suites are costly to execute, industry needs methods for test case prioritisation (TCP). Recently, TCP methods use machine learning (ML) to exploit the information known about the system under test (SUT) and its test cases. However, the value added by ML-based TCP methods should be critically assessed with respect to the cost of collecting the information. This paper analyses two decades of TCP research, and presents a taxonomy of 91 information attributes that have been used. The attributes are classified with respect to their information sources and the characteristics of their extraction process. Based on this taxonomy, TCP methods validated with industrial data and those applying ML are analysed in terms of information availability, attribute combination and definition of data features suitable for ML. Relying on a high number of information attributes, assuming easy access to SUT code and simplified testing environments are identified as factors that might hamper industrial applicability of ML-based TCP. The TePIA taxonomy provides a reference framework to unify terminology and evaluate alternatives considering the cost-benefit of the information attributes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
