SEBASE: Software Engineering By Automated SEarch

当前的软件工程实践是在有限的资源下以人为主导的寻求满足需求和约束的解决方案。在功能性标准和非功能性标准之间和内部，常常会有冲突。当然，像其他工程师一样，我们会寻找一个接近最佳的解决方案。随着系统变得更大、更分散、更动态和更关键，这种劳动密集型搜索将达到根本性的极限。如果不自动化或部分自动化地寻找接近最优的解决方案，我们将无法继续以传统的方式开发、操作和维护系统。基于自动搜索的解决方案在其他工程学科中具有成功的记录，其特征在于存在大量潜在的解决方案，其中存在许多复杂的、竞争的和冲突的约束，并且其中构建完美的解决方案是不可能的或不切实际的。SEMINAL网络表明，这些技术为软件工程中的几个问题提供了强大的，具有成本效益和高质量的解决方案。迄今为止的成功可以被视为搜索的强大指针，它具有作为一个总体解决方案范例的巨大潜力。SEBASE项目旨在为理解和实践软件工程提供一种新的方法。它将把软件工程问题从基于人类的搜索转移到基于机器的搜索。因此，人类的努力将上升到抽象链，专注于指导自动搜索，而不是执行它。这个项目将解决软件工程中的关键问题，包括可扩展性，鲁棒性，可靠性和稳定性。它还将研究搜索算法的理论基础，并将所获得的见解应用于为大型复杂的软件工程问题开发更有效和高效的搜索算法。这些见解将对搜索算法社区以及软件工程社区产生重大影响。

Current software engineering practice is a human-led search for solutions which meet needs and constraints under limited resources. Often there will be conflict, both between and within functional and non-functional criteria. Naturally, like other engineers, we search for a near optimal solution. As systems get bigger, more distributed, more dynamic and more critical, this labour-intensive search will hit fundamental limits. We will not be able to continue to develop, operate and maintain systems in the traditional way, without automating or partly automating the search for near optimal solutions. Automated search based solutions have a track record of success in other engineering disciplines, characterised by a large number of potential solutions, where there are many complex, competing and conflicting constraints and where construction of a perfect solution is either impossible or impractical. The SEMINAL network demonstrated that these techniques provide robust, cost-effective and high quality solutions for several problems in software engineering. Successes to date can be seen as strong pointers to search having great potential to serve as an overarching solution paradigm. The SEBASE project aims to provide a new approach to the way in which software engineering is understood and practised. It will move software engineering problems from human-based search to machine-based search. As a result, human effort will move up the abstraction chain, to focus on guiding the automated search, rather than performing it. This project will address key issues in software engineering, including scalability, robustness, reliability and stability. It will also study theoretical foundations of search algorithms and apply the insights gained to develop more effective and efficient search algorithms for large and complex software engineering problems. Such insights will have a major impact on the search algorithm community as well as the software engineering community.