物件導向軟體之架構(Object-Oriented Software Construction)探討

Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

[서평]「The Unified Modeling Language User Guide」

The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

This paper describes a study of the evolution of robust communication, specifically the distribution of data among individuals in a population, using digital evolution. In digital evolution, a population of self-replicating computer programs exists in a user-defined computational environment and is subject to instruction-level mutations and natural selection. To encourage the evolution of this cooperative behavior, we make use of "digital germlines," a form of group-level selection similar to multicellularity in biology. The results of experiments using the Avida platform for digital evolution demonstrate that populations of digital organisms are capable of evolving to distribute data in a network, and that through the application of different selective pressures, these digital organisms can overcome communication obstacles such as message loss, limited bandwidth, and node failure.

/pdf/evolution-of-robust-data-distribution-among-digital-23ryzzff2c.pdf

Evolution of robust data distribution among digital organisms

Correctness is paramount for safety-critical software control systems. Critical software failures in medical radiation treatment, communications, and defense are familiar to the public. The significant quantity of software malfunctions regularly reported to the software engineering community, the laws concerning liability, and a recent NRC Aeronautics and Space Engineering Board report additionally motivate the use of error-reducing and defect detection software development techniques. The benefits of formal methods in requirements driven software development ('forward engineering') is well documented. One advantage of rigorously engineering software is that formal notations are precise, verifiable, and facilitate automated processing. This paper describes the application of formal methods to reverse engineering, where formal specifications are developed for a portion of the shuttle on-orbit digital autopilot (DAP). Three objectives of the project were to: demonstrate the use of formal methods on a shuttle application, facilitate the incorporation and validation of new requirements for the system, and verify the safety-critical properties to be exhibited by the software.

/pdf/applying-formal-methods-and-object-oriented-analysis-to-4yods4bi5v.pdf

Applying formal methods and object-oriented analysis to existing flight software

We investigate the integration of evolutionary algorithms and novelty search in order to improve the performance and resilience of autonomous systems. We have developed two tools for this purpose: Evo-ROS and Enki. Evo-ROS combines evolutionary search with physics-based simulations of autonomous systems whose software infrastructure is based on the Robot Operating System (ROS). Enki uses novelty search to discover operational scenarios that lead to the most diverse behavior in the target system. Combining these tools yields an automated approach to explore the operational landscape of the target system, identify regions of poor performance, and evolve system parameters that better respond to adverse situations. In this paper, we present results of a case study of the throttle controller on AutoRally, a 1:5-scale autonomous vehicle designed by researchers at Georgia Tech for the study of aggressive autonomous driving. Preliminary experiments demonstrate the ability of the proposed methods to identify and characterize input speed signals that cause the existing controller to perform poorly. The ability to identify these troublesome signals enables development of a control system capable of handling a wider range of conditions by autonomously switching among controller modes that are optimized for different conditions.

Applying evolution and novelty search to enhance the resilience of autonomous systems

Detecting errors early within the development process for an embedded system assists a developer in avoiding excessive error correction costs and minimizing catastrophic losses resulting from failures in deployed systems. Towards that end, this paper presents i2MAP, an iterative and incremental goal-driven process for constructing an analysis-level UML model of an embedded system. The UML model is formally analyzed for adherence to the behavioral properties captured in a companion goal model. The process uses goal modeling to capture the requirements of the system, and uses UML to capture analysis-level structural and behavioral information. Both types of i2MAP models can be used to drive a rigorous approach to model-driven development of embedded systems. In this paper, we illustrate the i2MAP process and the accompanying tool suite in the development of an embedded system model for an adaptive light control system.

i2MAP: an incremental and iterative modeling and analysis process

Increasingly, safety-critical systems include artificial intelligence and machine learning components (i.e., Learning-Enabled Components (LECs)). However, when behavior is learned in a training environment that fails to fully capture real-world phenomena, the response of an LEC to untrained phenomena is uncertain, and therefore cannot be assured as safe. Automated methods are needed for self-assessment and adaptation to decide when learned behavior can be trusted. This work introduces a model-driven approach to manage self-adaptation of a Learning-Enabled System (LES) to account for run-time contexts for which the learned behavior of LECs cannot be trusted. The resulting framework enables an LES to monitor and evaluate goal models at run time to determine whether or not LECs can be expected to meet functional objectives. Using this framework enables stakeholders to have more confidence that LECs are used only in contexts comparable to those validated at design time.

Betty H. C. Cheng

Papers

Evolution of robust data distribution among digital organisms

Applying formal methods and object-oriented analysis to existing flight software

Applying evolution and novelty search to enhance the resilience of autonomous systems

i2MAP: an incremental and iterative modeling and analysis process

MoDALAS: Model-Driven Assurance for Learning-Enabled Autonomous Systems