11,995 research outputs found
Introducing Interactions in Multi-Objective Optimization of Software Architectures
Software architecture optimization aims to enhance non-functional attributes
like performance and reliability while meeting functional requirements.
Multi-objective optimization employs metaheuristic search techniques, such as
genetic algorithms, to explore feasible architectural changes and propose
alternatives to designers. However, the resource-intensive process may not
always align with practical constraints. This study investigates the impact of
designer interactions on multi-objective software architecture optimization.
Designers can intervene at intermediate points in the fully automated
optimization process, making choices that guide exploration towards more
desirable solutions. We compare this interactive approach with the fully
automated optimization process, which serves as the baseline. The findings
demonstrate that designer interactions lead to a more focused solution space,
resulting in improved architectural quality. By directing the search towards
regions of interest, the interaction uncovers architectures that remain
unexplored in the fully automated process
Metaheuristic models for decision support in the software construction process
En la actualidad, los ingenieros software no solo tienen la responsabilidad de construir
sistemas que desempe~nen una determinada funcionalidad, sino que cada vez
es más importante que dichos sistemas también cumplan con requisitos no funcionales
como alta disponibilidad, efciencia o seguridad, entre otros. Para lograrlo,
los ingenieros se enfrentan a un proceso continuo de decisión, pues deben estudiar
las necesidades del sistema a desarrollar y las alternativas tecnológicas existentes
para implementarlo. Todo este proceso debe estar encaminado a la obtención de
sistemas software de gran calidad, reutilizables y que faciliten su mantenimiento y
modificación en un escenario tan exigente y competitivo.
La ingeniería del software, como método sistemático para la construcción de software,
ha aportado una serie de pautas y tareas que, realizadas de forma disciplinada
y adaptadas al contexto de desarrollo, posibilitan la obtención de software de calidad.
En concreto, el proceso de análisis y diseño del software ha adquirido una gran
importancia, pues en ella se concibe la estructura del sistema, en términos de sus bloques
funcionales y las interacciones entre ellos. Es en este momento cuando se toman
las decisiones acerca de la arquitectura, incluyendo los componentes que la conforman,
que mejor se adapta a los requisitos, tanto funcionales como no funcionales,
que presenta el sistema y que claramente repercuten en su posterior desarrollo. Por
tanto, es necesario que el ingeniero analice rigurosamente las alternativas existentes,
sus implicaciones en los criterios de calidad impuestos y la necesidad de establecer
compromisos entre ellos. En este contexto, los ingenieros se guían principalmente
por sus habilidades y experiencia, por lo que dotarles de métodos de apoyo a la
decisión representaría un avance significativo en el área.
La aplicación de técnicas de inteligencia artificial en este ámbito ha despertado un
gran interés en los últimos años. En particular, la inteligencia artificial ha encontrado
en la ingeniería del software un ámbito de aplicación complejo, donde diferentes
técnicas pueden ayudar a conseguir la semi-automatización de tareas tradicionalmente
realizadas de forma manual. De la unión de ambas áreas surge la denominada
ingeniería del software basada en búsqueda, que propone la reformulación de las
actividades propias de la ingeniería del software como problemas de optimización.
A continuación, estos problemas podrían ser resueltos mediante técnicas de búsqueda
como las metaheurísticas. Este tipo de técnicas se caracterizan por explorar el
espacio de posibles soluciones de una manera \inteligente", a menudo simulando
procesos naturales como es el caso de los algoritmos evolutivos.
A pesar de ser un campo de investigación muy reciente, es posible encontrar propuestas
para automatizar una gran variedad de tareas dentro del ciclo de vida del software, como son la priorización de requisitos, la planifcación de recursos, la refactorización del código fuente o la generación de casos de prueba. En el ámbito del
análisis y diseño de software, cuyas tareas requieren de creatividad y experiencia,
conseguir una automatización completa resulta poco realista. Es por ello por lo que
la resolución de sus tareas mediante enfoques de búsqueda debe ser tratada desde la
perspectiva del ingeniero, promoviendo incluso la interacción con ellos. Además, el
alto grado de abstracción de algunas de sus tareas y la dificultad de evaluar cuantitativamente
la calidad de un diseño software, suponen grandes retos en la aplicación
de técnicas de búsqueda durante las fases tempranas del proceso de construcción de
software.
Esta tesis doctoral busca realizar aportaciones significativas al campo de la ingeniería
del software basada en búsqueda y, más concretamente, al área de la optimización
de arquitecturas software. Aunque se están realizando importantes avances en este
área, la mayoría de propuestas se centran en la obtención de arquitecturas de bajo
nivel o en la selección y despliegue de artefactos software ya desarrollados. Por tanto,
no existen propuestas que aborden el modelado arquitectónico a un nivel de abstracción elevado, donde aún no existe un conocimiento profundo sobre cómo será el
sistema y, por tanto, es más difícil asistir al ingeniero. Como problema de estudio,
se ha abordado principalmente la tarea del descubrimiento de arquitecturas software
basadas en componentes. El objetivo de este problema consiste en abstraer los bloques
arquitectónicos que mejor definen la estructura actual del software, así como
sus interacciones, con el fin de facilitar al ingeniero su posterior análisis y mejora.
Durante el desarrollo de esta tesis doctoral se ha explorado el uso de una gran variedad
de técnicas de búsqueda, estudiando su idoneidad y realizando las adaptaciones
necesarias para hacer frente a los retos mencionados anteriormente. La primera propuesta
se ha centrado en la formulación del descubrimiento de arquitecturas como
problema de optimización, abordando la representación computacional de los artefactos
software que deben ser modelados y definiendo medidas software para evaluar
su calidad durante el proceso de búsqueda. Además, se ha desarrollado un primer
modelo basado en algoritmos evolutivos mono-objetivo para su resolución, el cual ha
sido validado experimentalmente con sistemas software reales. Dicho modelo se caracteriza
por ser comprensible y
exible, pues sus componentes han sido diseñados
considerando estándares y herramientas del ámbito de la ingeniería del software,
siendo además configurable en función de las necesidades del ingeniero.
A continuación, el descubrimiento de arquitecturas ha sido tratado desde una perspectiva
multiobjetivo, donde varias medidas software, a menudo en con
icto, deben
ser simultáneamente optimizadas. En este caso, la resolución del problema se ha
llevado a cabo mediante ocho algoritmos del estado del arte, incluyendo propuestas recientes del ámbito de la optimización de muchos objetivos. Tras ser adaptados al
problema, estos algoritmos han sido comparados mediante un extenso estudio experimental
con el objetivo de analizar la ifnuencia que tiene el número y la elección
de las métricas a la hora de guiar el proceso de búsqueda. Además de realizar una
validación del rendimiento de estos algoritmos siguiendo las prácticas habituales
del área, este estudio aporta un análisis detallado de las implicaciones que supone
la optimización de múltiples objetivos en la obtención de modelos de soporte a la
decisión.
La última propuesta en el contexto del descubrimiento de arquitecturas software
se centra en la incorporación de la opinión del ingeniero al proceso de búsqueda.
Para ello se ha diseñado un mecanismo de interacción que permite al ingeniero indicar
tanto las características deseables en las soluciones arquitectónicas (preferencias
positivas) como aquellos aspectos que deben evitarse (preferencias negativas). Esta
información es combinada con las medidas software utilizadas hasta el momento,
permitiendo al algoritmo evolutivo adaptar la búsqueda conforme el ingeniero interactúe. Dadas las características del modelo, su validación se ha realizado con la
participación de ingenieros con distinta experiencia en desarrollo software, a fin de
demostrar la idoneidad y utilidad de la propuesta.
En el transcurso de la tesis doctoral, los conocimientos adquiridos y las técnicas
desarrolladas también han sido extrapolados a otros ámbitos de la ingeniería del
software basada en búsqueda mediante colaboraciones con investigadores del área.
Cabe destacar especialmente la formalización de una nueva disciplina transversal,
denominada ingeniería del software basada en búsqueda interactiva, cuyo fin es promover
la participación activa del ingeniero durante el proceso de búsqueda. Además,
se ha explorado la aplicación de algoritmos de muchos objetivos a un problema clásico
de la computación orientada a servicios, como es la composición de servicios web.Nowadays, software engineers have not only the responsibility of building systems that provide a particular functionality, but they also have to guarantee that these systems ful l demanding non-functional requirements like high availability, e ciency or security. To achieve this, software engineers face a continuous decision process, as they have to evaluate system needs and existing technological alternatives to implement it. All this process should be oriented towards obtaining high-quality and reusable systems, also making future modi cations and maintenance easier in such a competitive scenario. Software engineering, as a systematic method to build software, has provided a number of guidelines and tasks that, when done in a disciplinarily manner and properly adapted to the development context, allow the creation of high-quality software. More speci cally, software analysis and design has acquired great relevance, being the phase in which the software structure is conceived in terms of its functional blocks and their interactions. In this phase, engineers have to make decisions about the most suitable architecture, including its constituent components. Such decisions are made according to the system requirements, either functional or non-functional, and will have a great impact on its future development. Therefore, the engineer has to rigorously analyse existing alternatives, their implications on the imposed quality criteria and the need of establishing trade-o s among them. In this context, engineers are mostly guided by their own capabilities and experience, so providing them with decision support methods would represent a signi cant contribution. The application of arti cial intelligent techniques in this area has experienced a growing interest in the last years. Particularly, software engineering represents a complex application domain to arti cial intelligence, whose diverse techniques can help in the semi-automation of tasks traditionally performed manually. The union of both elds has led to the appearance of search-based software engineering, which proposes reformulating software engineering activities as optimisation problems. For their resolution, search techniques like metaheuristics can be then applied. This type of technique performs an \intelligent" exploration of the space of candidate solutions, often inspired by natural processes as happens with evolutionary algorithms. Despite the novelty of this research eld, there are proposals to automate a great variety of tasks within the software lifecycle, such as requirement prioritisation, resource planning, code refactoring or test case generation. Focusing on analysis and design, whose tasks require creativity and experience, trying to achieve full automation is not realistic. Therefore, solving design tasks by means of search approaches should be oriented towards the engineer's perspective, even promoting their interaction. Furthermore, design tasks are also characterised by a high level of abstraction and the di culty of quantitatively evaluating design quality. All these aspects represent key challenges for the application of search techniques in early phases of the software construction process. The aim of this Ph.D. Thesis is to make signi cant contributions in search-based software engineering and, specially, in the area of software architecture optimisation. Although it is an area in which signi cant progress is being done, most of the current proposals are focused on generating low-level architectures or selecting and deploying already developed artefacts. Therefore, there is a lack of proposals dealing with architectural modelling at a high level of abstraction. At this level, engineers do not have a deep understanding of the system yet, meaning that assisting them is even more di cult. As case study, the discovery of component-based software architectures has been primary addressed. The objective for this problem consists in the abstraction of the architectural blocks, and their interactions, that best de ne the current structure of a software system. This can be viewed as the rst step an engineer would perform in order to further analyse and improve the system architecture. In this Ph.D. Thesis, the use of a great variety of search techniques has been explored. The suitability of these techniques has been studied, also making the necessary adaptations to cope with the aforementioned challenges. A rst proposal has been focused on the formulation of software architecture discovery as an optimisation problem, which consists in the computational representation of its software artefacts and the de nition of software metrics to evaluate their quality during the search process. Moreover, a single-objective evolutionary algorithm has been designed for its resolution, which has been validated using real software systems. The resulting model is comprehensible and exible, since its components have been designed under software engineering standards and tools and are also con gurable according to engineer's needs. Next, the discovery of software architectures has been tackled from a multi-objective perspective, in which several software metrics, often in con ict, have to be simultaneously optimised. In this case, the problem is solved by applying eight state-of-theart algorithms, including some recent many-objective approaches. These algorithms have been adapted to the problem and compared in an extensive experimental study, whose purpose is to analyse the in uence of the number and combination of metrics when guiding the search process. Apart from the performance validation following usual practices within the eld, this study provides a detailed analysis of the practical
implications behind the optimisation of multiple objectives in the context of
decision support.
The last proposal is focused on interactively including the engineer's opinion in the
search-based architecture discovery process. To do this, an interaction mechanism
has been designed, which allows the engineer to express desired characteristics for
the solutions (positive preferences), as well as those aspects that should be avoided
(negative preferences). The gathered information is combined with the software
metrics used until the moment, thus making possible to adapt the search as the
engineer interacts. Due to the characteristics of the proposed model, engineers of
di erent expertise in software development have participated in its validation with
the aim of showing the suitability and utility of the approach.
The knowledge acquired along the development of the Thesis, as well as the proposed
approaches, have also been transferred to other search-based software engineering
areas as a result of research collaborations. In this sense, it is worth noting the
formalisation of interactive search-based software engineering as a cross-cutting discipline,
which aims at promoting the active participation of the engineer during the
search process. Furthermore, the use of many-objective algorithms has been explored
in the context of service-oriented computing to address the so-called web service
composition problem
Search based software engineering: Trends, techniques and applications
© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives.
This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E
Metaheuristic Design Patterns: New Perspectives for Larger-Scale Search Architectures
Design patterns capture the essentials of recurring best practice in an abstract form. Their merits are well established in domains as diverse as architecture and software development. They offer significant benefits, not least a common conceptual vocabulary for designers, enabling greater communication of high-level concerns and increased software reuse. Inspired by the success of software design patterns, this chapter seeks to promote the merits of a pattern-based method to the development of metaheuristic search software components. To achieve this, a catalog of patterns is presented, organized into the families of structural, behavioral, methodological and component-based patterns. As an alternative to the increasing specialization associated with individual metaheuristic search components, the authors encourage computer scientists to embrace the ‘cross cutting' benefits of a pattern-based perspective to optimization algorithms. Some ways in which the patterns might form the basis of further larger-scale metaheuristic component design automation are also discussed
System Architecture Design Using Multi-Criteria Optimization
System architecture is defined as the description of a complex system in terms of its functional requirements, physical elements and their interrelationships. Designing a complex system architecture can be a difficult task involving multi-faceted trade-off decisions. The system architecture designs often have many project-specific goals involving mix of quantitative and qualitative criteria and a large design trade space. Several tools and methods have been developed to support the system architecture design process in the last few decades. However, many conventional problem solving techniques face difficulties in dealing with complex system design problems having many goals.
In this research work, an interactive multi-criteria design optimization framework is proposed for solving many-objective system architecture design problems and generating a well distributed set of Pareto optimal solutions for these problems. System architecture design using multi-criteria optimization is demonstrated using a real-world application of an aero engine health management (EHM) system. A design process is presented for the optimal deployment of the EHM system functional operations over physical architecture subsystems. The EHM system architecture design problem is formulated as a multi-criteria optimization problem. The proposed methodology successfully generates a well distributed family of Pareto optimal architecture solutions for the EHM system, which provides valuable insights into the design trade-offs. Uncertainty analysis is implemented using an efficient polynomial chaos approach and robust architecture solutions are obtained for the EHM system architecture design. Performance assessment through evaluation of benchmark test metrics demonstrates the superior performance of the proposed methodology
A Survey on Compiler Autotuning using Machine Learning
Since the mid-1990s, researchers have been trying to use machine-learning
based approaches to solve a number of different compiler optimization problems.
These techniques primarily enhance the quality of the obtained results and,
more importantly, make it feasible to tackle two main compiler optimization
problems: optimization selection (choosing which optimizations to apply) and
phase-ordering (choosing the order of applying optimizations). The compiler
optimization space continues to grow due to the advancement of applications,
increasing number of compiler optimizations, and new target architectures.
Generic optimization passes in compilers cannot fully leverage newly introduced
optimizations and, therefore, cannot keep up with the pace of increasing
options. This survey summarizes and classifies the recent advances in using
machine learning for the compiler optimization field, particularly on the two
major problems of (1) selecting the best optimizations and (2) the
phase-ordering of optimizations. The survey highlights the approaches taken so
far, the obtained results, the fine-grain classification among different
approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our
Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated
quarterly here (Send me your new published papers to be added in the
subsequent version) History: Received November 2016; Revised August 2017;
Revised February 2018; Accepted March 2018
Deep learning for video game playing
In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards
- …