GGGP: Grow and Graft Genetic Programming

编程很难。向现有的大型系统添加新功能是一项挑战，即使对于最有能力的人类程序员也是如此。尽管软件开发环境取得了很大进展，但编程仍然包括许多枯燥、非生产性和乏味的人类活动。GGGP项目的动机通常是这样的问题：“为什么软件工程师要花这么长时间重复执行同样乏味的低级软件开发任务？”以及“程序员有多少次想过如何在特定的上下文中表达空指针检查的思想，或者调整现有代码以搜索迭代的数据结构？”我们希望找到一种全新的软件开发方法，由自动搜索支持，我们相信，这种方法将显著减少开发时间。我们提出了一种新的软件开发方法：Growth and Graft Genetic Programming(GGGP)，在GGGP中，(使用遗传编程)生长一个新功能，然后将其嫁接到现有系统中。这种生长和嫁接开发方法旨在减少人类程序员为开发新功能并将其添加到现有系统中所需的繁琐工作量。我们的初步概念验证工作发现，指导生长和嫁接遗传编程需要程序员提供令人惊讶的少量人类指导和领域知识。因此，我们相信它可以从根本上改变编程，使其更快、更不容易出错，从而对软件行业产生变革性的影响。我们还相信，它可能会使更多的人享受软件开发的乐趣，并对更广泛的公众参与(和理解)软件开发产生潜在的变革性影响。我们的方法可以在基于搜索的软件工程(SBSE)的最新趋势中得到最好的理解，该趋势称为“遗传改进”，它使用现有代码作为帮助自动改进现有软件系统的“遗传物质”，最近实现了几个显著的突破，例如，在现实世界系统上的速度提高了7到70倍，在优化约束求解器方面取得了人类具有竞争力的结果，以及在现有系统上自动修复错误和修复工作。

Programming is hard. Adding new functionality to an existing, large, and perhaps poorly-understood system is a challenge, even for the most competent human programmer. Despite much progress in software development environments, programming still includes many human activities that are dull, unproductive and tedious. The GGGP project is motivated by the frustration often expressed as questions such as "Why do software engineers spend so long repeatedly performing the same tedious low level software development tasks?" and "How many times do programmers work out how to express the idea of null pointer checking in a particular context or adapt existing code for searching an iterated data structure?" We want to find a radically new approach to software development, supported by automated search that, we believe, will yield a dramatic reduction in development time. We propose a new approach to software development: Grow and Graft Genetic Programming (GGGP), in which a new feature is grown (using genetic programming) and subsequently grafted into an existing system. This grow and graft development approach aims to reduce the amount of tedious effort required by human programmer in order to develop and add new functionality into an existing system.Our initial proof of concept work found that surprisingly little human guidance and domain knowledge is required from the programmer to guide Grow and Graft Genetic Programming. We therefore believe that it can radically change programming, making it faster and less error prone, with a consequent transformative effect on the software industry. We also believe it may make it more enjoyable to a wider range of people, with potentially transformative impact on the wider public involvement in (and understanding of) software development.Our approach can be best understood in the context of the recent trend in Search Based Software Engineering (SBSE) called "genetic improvement", which uses existing code as "genetic material" that helps to automatically improve existing software systems, which has achieved several recent notable breakthroughs, such as speed ups of 7 to 70 times on real-world systems, human competitive results in optimising constraint solvers, and automated bug fixing and repair work on existing systems.