ASSESSING THE QUALITY OF TEST SUITES IN INDUSTRIAL CODE ("AQUATIC")

大多数基于计算机的系统随着年龄的增长质量会下降。这给系统的开发人员带来了越来越大的负担，以确保维持一定的服务水平。虽然运行的系统代表了不断被监视的代码，但系统的一个方面很少受到关注，那就是它的测试代码。这是确保运行系统的正确性的代码，因此是系统的一个重要方面。根据美国NIST的数据，如果进行更好的软件测试，美国IT行业超过三分之一的缺陷成本可以避免，大约80亿美元。在许多组织中，测试代码至少与它所测试的代码一样大。从财务上讲，组织的测试代码维护良好非常重要-测试代码与“生产中”代码有很强的映射，并且对一个代码的更改通常需要更改另一个代码。AQUATIC项目的目的是探索测试代码是如何衰减的，它是如何演变的，以及测试代码和生产代码之间的异同。该项目采用经验方法，并使用来自两个工业合作伙伴的测试和生产代码作为基础。数据挖掘技术用于分析所涉及的大量数据并从数据中提取知识。该项目得到一系列可交付成果和相关传播活动的支持，包括一系列项目进展讲习班。

Most computer-based systems deteriorate in quality as they age. This places an increasing burden on the developers of the system to ensure that a certain level of service is maintained. While the running system represents code that is constantly monitored, an aspect of systems that has received very little attention is its test code. This is code that ensures a level of correctness of the running system and is therefore an important aspect of a system. According to US-based NIST, more than a third of the cost of defects in the US IT industry could be avoided if better software testing was performed i.e., approximately $8 billion. In a large number of organisations, test code is at least as large as the code which it tests. Financially, it is of great importance that an organisation's test code is maintained well - test code has a strong mapping to the 'in production' code and changes to one usually require changes to the other. The purpose of the AQUATIC project is to explore how test code decays, how it evolves and similarities and differences between test code and production code. The project adopts an empirical methodology and uses test and production code from two industrial partners as a basis. Data mining techniques are used to analyse the large volumes of data involved and to extract knowledge from the data. The project is supported by a set of deliverables and associated dissemination activities including a set of project progress Workshops.