Intelligent Web Services Architecture Evolution Via An Automated Learning-Based Refactoring Framework

Architecture degradation can have fundamental impact on software quality and productivity, resulting in inability to support new features, increasing technical debt and leading to significant losses. While code-level refactoring is widely-studied and well supported by tools, architecture-level refactorings, such as repackaging to group related features into one component, or retrofitting files into patterns, remain to be expensive and risky. Serval domains, such as Web services, heavily depend on complex architectures to design and implement interface-level operations, provided by several companies such as FedEx, eBay, Google, Yahoo and PayPal, to the end-users. The objectives of this work are: (1) to advance our ability to support complex architecture refactoring by explicitly defining Web service anti-patterns at various levels of abstraction, (2) to enable complex refactorings by learning from user feedback and creating reusable/personalized refactoring strategies to augment intelligent designers’ interaction that will guide low-level refactoring automation with high-level abstractions, and (3) to enable intelligent architecture evolution by detecting, quantifying, prioritizing, fixing and predicting design technical debts. We proposed various approaches and tools based on intelligent computational search techniques for (a) predicting and detecting multi-level Web services antipatterns, (b) creating an interactive refactoring framework that integrates refactoring path recommendation, design-level human abstraction, and code-level refactoring automation with user feedback using interactive mutli-objective search, and (c) automatically learning reusable and personalized refactoring strategies for Web services by abstracting recurring refactoring patterns from Web service releases. Based on empirical validations performed on both large open source and industrial services from multiple providers (eBay, Amazon, FedEx and Yahoo), we found that the proposed approaches advance our understanding of the correlation and mutual impact between service antipatterns at different levels, revealing when, where and how architecture-level anti-patterns the quality of services. The interactive refactoring framework enables, based on several controlled experiments, human-based, domain-specific abstraction and high-level design to guide automated code-level atomic refactoring steps for services decompositions. The reusable refactoring strategy packages recurring refactoring activities into automatable units, improving refactoring path recommendation and further reducing time-consuming and error-prone human intervention.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/142810/1/Wang Final Dissertation.pdfDescription of Wang Final Dissertation.pdf : Dissertatio

Attack-Surface Metrics, OSSTMM and Common Criteria Based Approach to “Composable Security” in Complex Systems

In recent studies on Complex Systems and Systems-of-Systems theory, a huge effort has been put to cope with behavioral problems, i.e. the possibility of controlling a desired overall or end-to-end behavior by acting on the individual elements that constitute the system itself. This problem is particularly important in the “SMART” environments, where the huge number of devices, their significant computational capabilities as well as their tight interconnection produce a complex architecture for which it is difficult to predict (and control) a desired behavior; furthermore, if the scenario is allowed to dynamically evolve through the modification of both topology and subsystems composition, then the control problem becomes a real challenge. In this perspective, the purpose of this paper is to cope with a specific class of control problems in complex systems, the “composability of security functionalities”, recently introduced by the European Funded research through the pSHIELD and nSHIELD projects (ARTEMIS-JU programme). In a nutshell, the objective of this research is to define a control framework that, given a target security level for a specific application scenario, is able to i) discover the system elements, ii) quantify the security level of each element as well as its contribution to the security of the overall system, and iii) compute the control action to be applied on such elements to reach the security target. The main innovations proposed by the authors are: i) the definition of a comprehensive methodology to quantify the security of a generic system independently from the technology and the environment and ii) the integration of the derived metrics into a closed-loop scheme that allows real-time control of the system. The solution described in this work moves from the proof-of-concepts performed in the early phase of the pSHIELD research and enrich es it through an innovative metric with a sound foundation, able to potentially cope with any kind of pplication scenarios (railways, automotive, manufacturing, ...)

A systematic literature review on physical internet containers : Empirical evidence and research opportunities

The Physical Internet is an emerging concept that seeks to improve the efficiency and sustainability of global logistics by creating a network under a common structure. Containers are a key tool for implementing this concept, as they enable the efficient and secure transport of goods along standardized hubs. This systematic literature review aims to identify and analyze existing research on the use of containers in the Physical Internet and their contributions to the efficiency and sustainability of global logistics. This review emphasizes the growing significance and empirical evidence supporting the usefulness and benefits of adopting container systems, including improved cargo optimization, logistics operations, and the sustainability of the logistics network. To solve difficulties like standardization, cost implementation, privacy, and integration of technology, more study is required. Regarding the methodology used, first a qualitative study of the articles is made, which will allow us to have a vision of the evolution that has taken place in recent years. Next, a study of the findings and conclusions will be made, which will allow for the classification of the research approaches and contributions. By giving a thorough overview, it provides insightful information for supply chain management, logistics, and other professionals. Furthermore, limitations and unanswered questions will be discussed in order to guide future studies with the identified research opportunities, emphasizing areas that need attention to progress this field’s research. In the last instance, these gaps offer chances to improve our comprehension and use of containers in the supply chain networ

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

Cost management and cross-functional communication through product architectures

Product architecture decisions regarding, for example, product modularity, component commonality, and design re-use, are important for balancing costs, responsiveness, quality, and other important business objectives. Firms are challenged with complex tradeoffs between competing design priorities, face the need to facilitate communication between functional silos, and want to learn from past experiences. In this paper, we present a qualitative approach for systematically evaluating the product architecture of a product family, comparing the original architecture objectives and actual experiences. The intended contribution of our research is threefold: (1) to present a framework that brings together a diverse set of product architecture-related decisions and business performance; (2) to provide a set of metrics that operationalise the variables in the framework, and (3) to provide a workshop protocol that is based on the framework and the metrics. This workshop aims to improve cross-functional communication about the product architecture of an existing product family, and it results in practical improvement actions for future architecture design projects. Experiences with this approach are reported in pilots with Philips Domestic Appliances and Personal Care, and Philips Consumer Electronics

Analyzing the Impacts of Emerging Technologies on Workforce Skills: A Case Study of Industrial Engineering in the Context of the Industrial Internet of Things

New technologies can result in major disruptions and change paradigms that were once well established. Methods have been developed to forecast new technologies and to analyze the impacts of them in terms of processes, products, and services. However, the current literature does not provide answers on how to forecast changes in terms of skills and knowledge, given an emerging technology. This thesis aims to fill this literature gap by developing a structured method to forecast the required set of skills for emerging technologies and to compare it with the current skills of the workforce. The method relies on the breakdown of the emerging technology into smaller components, so then skills can be identified for each component. A case study was conducted to implement and test the proposed method. In this case study, the impacts of the Industrial Internet of Things (IIoT) on engineering skills and knowledge were assessed. Text data analytics validated IIoT as an emerging technology, thus justifying the case study based on engineering and manufacturing discussions. The set of skills required for IIoT was compared to the current skills developed by Industrial Engineering students at the University of Windsor. Text data analytics was also used to evaluate the importance of each IIoT component by measuring how associated individual components are to IIoT. Therefore, existing skill gaps between the current Industrial Engineering program and IIoT requirements were not only mapped, but they were also given weights

Data science for engineering design: State of the art and future directions

Abstract Engineering design (ED) is the process of solving technical problems within requirements and constraints to create new artifacts. Data science (DS) is the inter-disciplinary field that uses computational systems to extract knowledge from structured and unstructured data. The synergies between these two fields have a long story and throughout the past decades, ED has increasingly benefited from an integration with DS. We present a literature review at the intersection between ED and DS, identifying the tools, algorithms and data sources that show the most potential in contributing to ED, and identifying a set of challenges that future data scientists and designers should tackle, to maximize the potential of DS in supporting effective and efficient designs. A rigorous scoping review approach has been supported by Natural Language Processing techniques, in order to offer a review of research across two fuzzy-confining disciplines. The paper identifies challenges related to the two fields of research and to their interfaces. The main gaps in the literature revolve around the adaptation of computational techniques to be applied in the peculiar context of design, the identification of data sources to boost design research and a proper featurization of this data. The challenges have been classified considering their impacts on ED phases and applicability of DS methods, giving a map for future research across the fields. The scoping review shows that to fully take advantage of DS tools there must be an increase in the collaboration between design practitioners and researchers in order to open new data driven opportunities