GGGP: Grow and Graft Genetic Programming

编程很难。向现有的、大型的、可能还不太容易理解的系统添加新功能是一个挑战，即使对于最有能力的人类程序员来说也是如此。尽管软件开发环境取得了很大的进步，但编程仍然包括许多枯燥、低效和乏味的人类活动。GGGP项目的动机是经常被表达为诸如“为什么软件工程师要花这么长时间重复地执行相同的乏味的低级软件开发任务？有多少次程序员会考虑如何在特定的上下文中表达空指针检查的思想，或者调整现有的代码来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），其中一个新的功能是增长（使用遗传编程），随后嫁接到现有的系统。这种生长和嫁接开发方法旨在减少人类程序员为了开发和添加新功能到现有系统中所需的繁琐工作量。我们最初的概念证明工作发现，令人惊讶的是，程序员只需要很少的人类指导和领域知识来指导生长和嫁接遗传编程。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，这可能会使更广泛的人更愉快，对更广泛的公众参与产生潜在的变革性影响，我们的方法可以在最近基于搜索的软件工程（SBSE）称为“遗传改进”的趋势的背景下得到最好的理解，该趋势使用现有代码作为“遗传材料”，有助于自动改进现有软件系统，它最近取得了几项显著的突破，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.