17 research outputs found
Optimising Darwinian Data Structures on Google Guava
Data structure selection and tuning is laborious but can vastly
improve application performance and memory footprint. In this paper,
we demonstrate how artemis, a multiobjective, cloud-based optimisation
framework can automatically find optimal, tuned data structures and how
it is used for optimising the Guava library. From the proposed solutions
that artemis found, 27.45% of them improve all measures (execution
time, CPU usage, and memory consumption). More specifically, artemis
managed to improve the memory consumption of Guava by up 13%,
execution time by up to 9%, and 4% CPU usage
Darwinian Data Structure Selection
Data structure selection and tuning is laborious but can vastly improve an
application's performance and memory footprint. Some data structures share a
common interface and enjoy multiple implementations. We call them Darwinian
Data Structures (DDS), since we can subject their implementations to survival
of the fittest. We introduce ARTEMIS a multi-objective, cloud-based
search-based optimisation framework that automatically finds optimal, tuned DDS
modulo a test suite, then changes an application to use that DDS. ARTEMIS
achieves substantial performance improvements for \emph{every} project in
Java projects from DaCapo benchmark, popular projects and uniformly
sampled projects from GitHub. For execution time, CPU usage, and memory
consumption, ARTEMIS finds at least one solution that improves \emph{all}
measures for () of the projects. The median improvement across
the best solutions is , , for runtime, memory and CPU
usage.
These aggregate results understate ARTEMIS's potential impact. Some of the
benchmarks it improves are libraries or utility functions. Two examples are
gson, a ubiquitous Java serialization framework, and xalan, Apache's XML
transformation tool. ARTEMIS improves gson by \%, and for
memory, runtime, and CPU; ARTEMIS improves xalan's memory consumption by
\%. \emph{Every} client of these projects will benefit from these
performance improvements.Comment: 11 page
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
MAGPIE: Machine Automated General Performance Improvement via Evolution of Software
Performance is one of the most important qualities of software. Several
techniques have thus been proposed to improve it, such as program
transformations, optimisation of software parameters, or compiler flags. Many
automated software improvement approaches use similar search strategies to
explore the space of possible improvements, yet available tooling only focuses
on one approach at a time. This makes comparisons and exploration of
interactions of the various types of improvement impractical.
We propose MAGPIE, a unified software improvement framework. It provides a
common edit sequence based representation that isolates the search process from
the specific improvement technique, enabling a much simplified synergistic
workflow. We provide a case study using a basic local search to compare
compiler optimisation, algorithm configuration, and genetic improvement. We
chose running time as our efficiency measure and evaluated our approach on four
real-world software, written in C, C++, and Java.
Our results show that, used independently, all techniques find significant
running time improvements: up to 25% for compiler optimisation, 97% for
algorithm configuration, and 61% for evolving source code using genetic
improvement. We also show that up to 10% further increase in performance can be
obtained with partial combinations of the variants found by the different
techniques. Furthermore, the common representation also enables simultaneous
exploration of all techniques, providing a competitive alternative to using
each technique individually.Comment: 19 page
Genetic Programming + Proof Search = Automatic Improvement
Search Based Software Engineering techniques are emerging as important tools for software maintenance. Foremost among these is Genetic Improvement, which has historically applied the stochastic techniques of Genetic Programming to optimize pre-existing program code. Previous work in this area has not generally preserved program semantics and this article describes an alternative to the traditional mutation operators used, employing deterministic proof search in the sequent calculus to yield semantics-preserving transformations on algebraic data types. Two case studies are described, both of which are applicable to the recently-introduced `grow and graft' technique of Genetic Improvement: the first extends the expressiveness of the `grafting' phase and the second transforms the representation of a list data type to yield an asymptotic efficiency improvement
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
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
Multi-Objective Improvement of Android Applications
Non-functional properties, such as runtime or memory use, are important to
mobile app users and developers, as they affect user experience. Previous work
on automated improvement of non-functional properties in mobile apps failed to
address the inherent trade-offs between such properties. We propose a practical
approach and the first open-source tool, GIDroid (2023), for multi-objective
automated improvement of Android apps. In particular, we use Genetic
improvement, a search-based technique that navigates the space of software
variants to find improved software. We use a simulation-based testing framework
to greatly improve the speed of search. GIDroid contains three state-of-the-art
multi-objective algorithms, and two new mutation operators, which cache the
results of method calls. Genetic improvement relies on testing to validate
patches. Previous work showed that tests in open-source Android applications
are scarce. We thus wrote tests for 21 versions of 7 Android apps, creating a
new benchmark for performance improvements. We used GIDroid to improve versions
of mobile apps where developers had previously found improvements to runtime,
memory, and bandwidth use. Our technique automatically re-discovers 64% of
existing improvements. We then applied our approach to current versions of
software in which there were no known improvements. We were able to improve
execution time by up to 35%, and memory use by up to 33% in these apps.Comment: 32 pages, 8 Figure