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

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

Automated Repair of Binary and Assembly Programs for Cooperating Embedded Devices

We present a method for automatically repairing arbitrary software defects in embedded systems, which have limited memory, disk and CPU capacities, but exist in great numbers. We extend evolutionary computation (EC) algorithms that search for valid repairs at the source code level to assembly and ELF format binaries, compensating for limited system resources with several algorithmic innovations. Our method does not require access to the source code or build toolchain of the software under repair, does not require program instrumentation, specialized execution environments, or virtual machines, or prior knowledge of the bug type. We repair defects in ARM and x86 assembly as well as ELF binaries, observing decreases of 86 % in memory and 95 % in disk requirements, with 62 % decrease in repair time, compared to similar source-level techniques. These advances allow repairs previously possible only with C source code to be applied to any ARM or x86 assembly or ELF executable. Efficiency gains are achieved by introducing stochastic fault localization, with much lower overhead than comparable deterministic methods, and low-level program representations. When distributed over multiple devices, our algorithm finds repairs faster than predicted by naïve parallelism. Four devices using our approach are five times more efficient than a single device because of our collaboration model. The algorithm is implemented on Nokia N900 smartphones, with inter-phone communication fitting in 900 bytes sent in 7 SMS text messages per device per repair on average