In today’s technology driven society, software is becoming increasingly important in more
areas of our lives. The domain of software extends beyond the obvious domain of computers,
tablets, and mobile phones. Smart devices and the internet-of-things have inspired the integra-
tion of digital and computational technology into objects that some of us would never have
guessed could be possible or even necessary. Fridges and freezers connected to social media
sites, a toaster activated with a mobile phone, physical buttons for shopping, and verbally
asking smart speakers to order a meal to be delivered. This is the world we live in and it is an
exciting time for software engineers and computer scientists. The sheer volume of code that is
currently in use has long since outgrown beyond the point of any hope for proper manual
maintenance. The rate of which mobile application stores such as Google’s and Apple’s have
expanded is astounding.
The research presented here aims to shed a light on an emerging field of research, called
Genetic Improvement ( GI ) of software. It is a methodology to change program code to improve
existing software. This thesis details a framework for GI that is then applied to explore fitness
landscape of bug fixing Python software, reduce execution time in a C ++ program, and
integrated into a live system.
We show that software is generally not fragile and although fitness landscapes for GI are
flat they are not impossible to search in. This conclusion applies equally to bug fixing in small
programs as well as execution time improvements. The framework’s application is shown to
be transportable between programming languages with minimal effort. Additionally, it can be
easily integrated into a system that runs a live web service.
The work within this thesis was funded by EPSRC grant EP/J017515/1 through the DAASE
project.