A Hierarchical Clustering Based Approach in Aspect Mining

A Hierarchical Clustering Based Approach in Aspect Mining Clustering is a division of data into groups of similar objects. Aspect mining is a process that tries to identify crosscutting concerns in existing software systems. The goal is to refactor the existing systems to use aspect oriented programming, in order to make them easier to maintain and to evolve. The aim of this paper is to present a new hierarchical clustering based approach in aspect mining. For this purpose we propose HAC algorithm (Hierarchical Agglomerative Clustering in aspect mining). Clustering is used in order to identify crosscutting concerns. We evaluate the obtained results from the aspect mining point of view, based on two quality measures that we have previously introduced and a newly defined one. The proposed approach is compared with other similar existing approaches in aspect mining and two case studies are also reported

Resource-based Verification for Robust Composition of Aspects

Aspect Oriented Software Development has been proposed as a means to improve modularization of software in the presence of crosscutting concerns. Compared to object-oriented or procedural approaches, Aspect Oriented Programming (AOP) has not yet been applied in many industrial applications. In this thesis we investigate the application of AOP within an industrial context and propose a novel solution to the problem of behavioral conflicts between aspects. We report on our experience transferring an aspect-oriented solution to a company called Advanced Semi-conductor Material Lithography (ASML). We investigate the acceptance criteria for AOP in industry, based on two industrial cases studies. We present a process that includes quantification of the benefits of AOP and elicitation of key worries expressed by stakeholders. We conducted a controlled experiment to assess the advantages and disadvantages of an aspect-based approach using a tracing example. Twenty developers from ASML were requested to carry out five maintenance scenarios. This experiment has shown that, in case the tracing concern is implemented using an AOP implementation instead of a procedural language, the development effort is on average 6% reduced while the impact of errors is reduced by 77%, for maintaining code related to tracing. For a subset of the scenarios, the results were statistically significant on a confidence interval of 95%. The so-called aspect interference problem is one of the major concerns in introducing AOP. Aspects can be developed independently and behave correct in isolation. However, due to intended or unintended composition of aspects, undesired behavior can emerge. In this thesis we focus on behavioral conflicts between aspects at shared join points. These are illustrated by a realistic example based on crosscutting concerns from ASML. We present an approach for the detection of behavioral interference that is based on a novel abstraction of the behavior of aspects, using resources and operations. This abstraction enables the expression of behavior in a simple manner that is suitable for automated detection of interference among aspects. The approach employs a set of conflict detection rules that can be used to detect both generic conflicts as well as domain specific conflicts. Our approach is general for AOP languages, its application to one specific AOP language Composition Filters is also illustrated in this thesis. The application to Composition Filters demonstrates how the use of a declarative advice language can be exploited for automated conflict detection. We detail the analysis process and discuss what information is required from the aspect developer to be able perform the analysis. We also discuss when static analysis is insufficient for detecting behavioral conflicts. We present a run time extension aiming at detecting dynamic conflicts. We discuss optimizations for this run time approach, which exploits the static verification results. Finally, we propose three improvements to the Composition Filters model to support automated and manual reasoning even further. The first improvement separates what behavior is executed from when this behavior is executed. Secondly, we introduce atomic filters that can be used to build more complex filters. The semantics of these filters are well defined. Although this approach has clear benefits from an automated reasoning perspective, the introduction of atomic filters results in the definition of numerous filters for specifying more complex behavior. Therefore, we introduce a filter composition language that enables the declarative composition of (atomic) filters, such that composed filter behavior can be reused elsewhere

Mutable Class Design Pattern

The dissertation proposes, presents and analyzes a new design pattern, the Mutable Class pattern, to support the processing of large-scale heterogeneous data models with multiple families of algorithms. Handling data-algorithm associations represents an important topic across a variety of application domains. As a result, it has been addressed by multiple approaches, including the Visitor pattern and the aspect-oriented programming (AOP) paradigm. Existing solutions, however, bring additional constraints and issues. For example, the Visitor pattern freezes the class hierarchies of application models and the AOP-based projects, such as Spring AOP, introduce significant overhead for processing large-scale models with fine-grain objects. The Mutable Class pattern addresses the limitations of these solutions by providing an alternative approach designed after the Class model of the UML specification. Technically, it extends a data model class with a class mutator supporting the interchangeability of operations. Design patterns represent reusable solutions to recurring problems. According to the design pattern methodology, the definition of these solutions encompasses multiple topics, such as the problem and applicability, structure, collaborations among participants, consequences, implementation aspects, and relation with other patterns. The dissertation provides a formal description of the Mutable Class pattern for processing heterogeneous tree-based models and elaborates on it with a comprehensive analysis in the context of several applications and alternative solutions. Particularly, the commonality of the problem and reusability of this approach is demonstrated and evaluated within two application domains: computational accelerator physics and compiler construction. Furthermore, as a core part of the Unified Accelerator Library (UAL) framework, the scalability boundary of the pattern has been challenged and explored with different categories of application architectures and computational infrastructures including distributed three-tier systems. The Mutable Class pattern targets a common problem arising from software engineering: the evolution of type systems and associated algorithms. Future research includes applying this design pattern in other contexts, such as heterogeneous information networks and large-scale processing platforms, and examining variations and alternative design patterns for solving related classes of problems

Clustering and its Application in Requirements Engineering

Large scale software systems challenge almost every activity in the software development life-cycle, including tasks related to eliciting, analyzing, and specifying requirements. Fortunately many of these complexities can be addressed through clustering the requirements in order to create abstractions that are meaningful to human stakeholders. For example, the requirements elicitation process can be supported through dynamically clustering incoming stakeholders’ requests into themes. Cross-cutting concerns, which have a significant impact on the architectural design, can be identified through the use of fuzzy clustering techniques and metrics designed to detect when a theme cross-cuts the dominant decomposition of the system. Finally, traceability techniques, required in critical software projects by many regulatory bodies, can be automated and enhanced by the use of cluster-based information retrieval methods. Unfortunately, despite a significant body of work describing document clustering techniques, there is almost no prior work which directly addresses the challenges, constraints, and nuances of requirements clustering. As a result, the effectiveness of software engineering tools and processes that depend on requirements clustering is severely limited. This report directly addresses the problem of clustering requirements through surveying standard clustering techniques and discussing their application to the requirements clustering process

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Vision 2040: A Roadmap for Integrated, Multiscale Modeling and Simulation of Materials and Systems

Over the last few decades, advances in high-performance computing, new materials characterization methods, and, more recently, an emphasis on integrated computational materials engineering (ICME) and additive manufacturing have been a catalyst for multiscale modeling and simulation-based design of materials and structures in the aerospace industry. While these advances have driven significant progress in the development of aerospace components and systems, that progress has been limited by persistent technology and infrastructure challenges that must be overcome to realize the full potential of integrated materials and systems design and simulation modeling throughout the supply chain. As a result, NASA's Transformational Tools and Technology (TTT) Project sponsored a study (performed by a diverse team led by Pratt & Whitney) to define the potential 25-year future state required for integrated multiscale modeling of materials and systems (e.g., load-bearing structures) to accelerate the pace and reduce the expense of innovation in future aerospace and aeronautical systems. This report describes the findings of this 2040 Vision study (e.g., the 2040 vision state; the required interdependent core technical work areas, Key Element (KE); identified gaps and actions to close those gaps; and major recommendations) which constitutes a community consensus document as it is a result of over 450 professionals input obtain via: 1) four society workshops (AIAA, NAFEMS, and two TMS), 2) community-wide survey, and 3) the establishment of 9 expert panels (one per KE) consisting on average of 10 non-team members from academia, government and industry to review, update content, and prioritize gaps and actions. The study envisions the development of a cyber-physical-social ecosystem comprised of experimentally verified and validated computational models, tools, and techniques, along with the associated digital tapestry, that impacts the entire supply chain to enable cost-effective, rapid, and revolutionary design of fit-for-purpose materials, components, and systems. Although the vision focused on aeronautics and space applications, it is believed that other engineering communities (e.g., automotive, biomedical, etc.) can benefit as well from the proposed framework with only minor modifications. Finally, it is TTT's hope and desire that this vision provides the strategic guidance to both public and private research and development decision makers to make the proposed 2040 vision state a reality and thereby provide a significant advancement in the United States global competitiveness

Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques

This dissertation presents the design, development, and simulation testing of a monitoring and control framework for dynamic systems using artificial intelligence techniques. A comprehensive monitoring and control system capable of detecting, identifying, evaluating, and accommodating various subsystem failures and upset conditions is presented. The system is developed by synergistically merging concepts inspired from the biological immune system with evolutionary optimization algorithms and adaptive control techniques.;The proposed methodology provides the tools for addressing the complexity and multi-dimensionality of the modern power plants in a comprehensive and integrated manner that classical approaches cannot achieve. Current approaches typically address abnormal condition (AC) detection of isolated subsystems of low complexity, affected by specific AC involving few features with limited identification capability. They do not attempt AC evaluation and mostly rely on control system robustness for accommodation. Addressing the problem of power plant monitoring and control under AC at this level of completeness has not yet been attempted.;Within the proposed framework, a novel algorithm, namely the partition of the universe, was developed for building the artificial immune system self. As compared to the clustering approach, the proposed approach is less computationally intensive and facilitates the use of full-dimensional self for system AC detection, identification, and evaluation. The approach is implemented in conjunction with a modified and improved dendritic cell algorithm. It allows for identifying the failed subsystems without previous training and is extended to address the AC evaluation using a novel approach.;The adaptive control laws are designed to augment the performance and robustness of baseline control laws under normal and abnormal operating conditions. Artificial neural network-based and artificial immune system-based approaches are developed and investigated for an advanced power plant through numerical simulation.;This dissertation also presents the development of an interactive computational environment for the optimization of power plant control system using evolutionary techniques with immunity-inspired enhancements. Several algorithms mimicking mechanisms of the immune system of superior organisms, such as cloning, affinity-based selection, seeding, and vaccination are used. These algorithms are expected to enhance the computational effectiveness, improve convergence, and be more efficient in handling multiple local extrema, through an adequate balance between exploration and exploitation.;The monitoring and control framework formulated in this dissertation applies to a wide range of technical problems. The proposed methodology is demonstrated with promising results using a high validity DynsimRTM model of the acid gas removal unit that is part of the integrated gasification combined cycle power plant available at West Virginia University AVESTAR Center. The obtained results show that the proposed system is an efficient and valuable technique to be applied to a real world application. The implementation of this methodology can potentially have significant impacts on the operational safety of many complex systems