GGGP: Grow and Graft Genetic Programming

编程很难。将新功能添加到现有的大型且可能不太理解的系统中是一项挑战，即使对于最有能力的人类程序员也是如此。尽管在软件开发环境中取得了很大的进步，但是编程仍然包括许多枯燥、低效和乏味的人类活动。GGGP项目的动力来自于这样的问题：“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“程序员要花多少次研究如何在特定的上下环境中表达空指针检查的想法，或者调整现有的代码来搜索迭代的数据结构？”我们想要找到一种全新的软件开发方法，它由自动搜索支持，我们相信这将大大减少开发时间。我们提出了一种新的软件开发方法：生长和嫁接遗传编程（GGGP），其中一个新的功能被生长（使用遗传编程），随后嫁接到现有的系统中。这种增长和移植开发方法旨在减少人类程序员为开发和向现有系统添加新功能所需要的繁琐工作。我们最初的概念证明工作发现，令人惊讶的是，很少需要程序员的指导和领域知识来指导生长和移植遗传编程。因此，我们相信它可以从根本上改变编程，使其更快，更少出错，从而对软件行业产生变革性的影响。我们还相信，它可能会使更广泛的人更喜欢它，对更广泛的公众参与（和理解）软件开发具有潜在的变革性影响。我们的方法可以在基于搜索的软件工程（SBSE）中被称为“遗传改进”的最新趋势的背景下得到最好的理解，它使用现有的代码作为“遗传物质”，帮助自动改进现有的软件系统，这已经取得了最近几个显著的突破，例如在现实世界系统上的速度提高了7到70倍，优化约束求解器的人类竞争结果，以及在现有系统上的自动错误修复和修复工作。

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.