Embedding Adaptivity in Software Systems using the ECSELR framework

International audienceECSELR is an ecologically-inspired approach to software evolution that enables environmentally driven evolution at runtime in extant software systems without relying on any offline components or management. ECSELR embeds adaptation and evolution inside the target software system enabling the system to transform itself via darwinian evolutionary mechanisms and adapt in a self contained manner. This allows the software system to benefit autonomously from the useful emergent byproducts of evolution like adaptivity and biodiversity , avoiding the problems involved in engineering and maintaining such properties. ECSELR enables software systems to address changing environments at runtime, ensuring benefits like mitigation of attacks and memory-optimization among others while avoiding time consuming and costly maintenance and downtime. ECSELR differs from existing work in that, 1) adaptation is embedded in the target system, 2) evolution and adaptation happens online(i.e. in-situ at runtime) and 3) ECSELR is able to embed adaptation inside systems that have already been started and are in the midst of execution. We demonstrate the use of ECSELR and present results on using the ECSELR framework to slim a software system

Evaluation of genetic improvement tools for improvement of non-functional properties of software

Genetic improvement (GI) improves both functional properties of software, such as bug repair, and non-functional properties, such as execution time, energy consumption, or source code size. There are studies summarising and comparing GI tools for improving functional properties of software; however there is no such study for improvement of its non-functional properties using GI. Therefore, this research aims to survey and report on the existing GI tools for improvement of non-functional properties of software. We conducted a literature review of available GI tools, and ran multiple experiments on the found open-source tools to examine their usability. We applied a cross-testing strategy to check whether the available tools can work on different programs. Overall, we found 63 GI papers that use a GI tool to improve nonfunctional properties of software, within which 31 are accompanied with open-source code. We were able to successfully run eight GI tools, and found that ultimately only two ---Gin and PyGGI--- can be readily applied to new general software

Polytypic Genetic Programming

Program synthesis via heuristic search often requires a great deal of boilerplate code to adapt program APIs to the search mechanism. In addition, the majority of existing approaches are not type-safe: i.e. they can fail at runtime because the search mechanisms lack the strict type information often available to the compiler. In this article, we describe Polytope, a Scala framework that uses polytypic programming, a relatively recent advance in program abstraction. Polytope requires a minimum of boilerplate code and supports a form of strong-typing in which type rules are automatically enforced by the compiler, even for search operations such as mutation which are applied at run-time. By operating directly on language-native expressions, it provides an embeddable optimization procedure for existing code. We give a tutorial example of the specific polytypic approach we adopt and compare both runtime efficiency and required lines of code against the well-known EpochX GP framework, showing comparable performance in the former and the complete elimination of boilerplate for the latter

Visualising the Search Landscape of the Triangle Program

High order mutation analysis of a software engineering benchmark, including schema and local optima networks, suggests program improvements may not be as hard to find as is often assumed. 1) Bit-wise genetic building blocks are not deceptive and can lead to all global optima. 2) There are many neutral networks, plateaux and local optima, nevertheless in most cases near the human written C source code there are hill climbing routes including neutral moves to solutions

Genetic Improvement of Software: a Comprehensive Survey

Genetic improvement (GI) uses automated search to find improved versions of existing software. We present a comprehensive survey of this nascent field of research with a focus on the core papers in the area published between 1995 and 2015. We identified core publications including empirical studies, 96% of which use evolutionary algorithms (genetic programming in particular). Although we can trace the foundations of GI back to the origins of computer science itself, our analysis reveals a significant upsurge in activity since 2012. GI has resulted in dramatic performance improvements for a diverse set of properties such as execution time, energy and memory consumption, as well as results for fixing and extending existing system functionality. Moreover, we present examples of research work that lies on the boundary between GI and other areas, such as program transformation, approximate computing, and software repair, with the intention of encouraging further exchange of ideas between researchers in these fields

RANCANGAN KAJIAN FAKTOR USAHA DALAM MEMBANGUN PANDUAN PENGEMBANGAN PERANGKAT LUNAK SEDERHANA, AKURAT, DAN DINAMIS

Abstrak. Panduan dalam membangun Perangkat Lunak (selanjutnya disebut PL) yang sederhana, akurat, dan dinamis merupakan sebuah proses pemikiran yang kompleks.  Banyak faktor yang mempengaruhinya diantaranya adalah usaha (biaya, waktu, dan tim), konsep, metode, kondisi, dan lingkungan pengembangan PL. Usaha PL dan proses estimasi biaya dalam proyek rekayasa PL merupakan komponen yang sangat penting. Keberhasilan atau kegagalan proyek sangat tergantung pada keakuratan usaha dan jadwal estimasi(5). Manajemen proyek perangkat lunak adalah salah satu kegiatan penting dalam proses pengembangan PL. Banyak proyek pengembangan PLgagal karena buruknya pengelolaan proyek. Tujuan utama dari software tim manajemen proyek adalah untuk menghitung apa yang perlu dihitung, mengukur apa yang perlu diukur dan mempersiapkan parameter terukur untuk terus memantau dan mengelola proyek pengembangan PL(11). Estimasi usaha PL adalah salah satu yang penting pada tahap awal dari siklus hidup pengembangan PL, khususnya ketika rincian persyaratan tidak dapat diidentifikasi dengan jelas(13).   Kata Kunci: Fuzzy Tahani, Sistem Pendukung Keputusan, Penilaian Kinerj

Genetic Improvement of Software: a Comprehensive Survey

Genetic improvement uses automated search to find improved versions of existing software. We present a comprehensive survey of this nascent field of research with a focus on the core papers in the area published between 1995 and 2015. We identified core publications including empirical studies, 96% of which use evolutionary algorithms (genetic programming in particular). Although we can trace the foundations of genetic improvement back to the origins of computer science itself, our analysis reveals a significant upsurge in activity since 2012. Genetic improvement has resulted in dramatic performance improvements for a diverse set of properties such as execution time, energy and memory consumption, as well as results for fixing and extending existing system functionality. Moreover, we present examples of research work that lies on the boundary between genetic improvement and other areas, such as program transformation, approximate computing, and software repair, with the intention of encouraging further exchange of ideas between researchers in these fields

Genetic Improvement of Software: From Program Landscapes to the Automatic Improvement of a Live System

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