Data Mining Practical Machine Learning Tools and Techniques

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

: Successful Software reuse requires the systematic discovery and exploitation of commonality across related software systems. By examining related software systems and the underlying theory of the class of systems they represent, domain analysis can provide a generic description of the requirements of that class of systems and a set of approaches for their implementation. This report will establish methods for performing a domain analysis and describe the products of the domain analysis process. To illustrate the application of domain analysis to a representative class of software systems, this report will provide a domain analysis of window management system software.

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Feature-Oriented Domain Analysis (FODA) Feasibility Study

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

OCL is a standard MDE language to express constraints. OCL has been criticized for being too complicated, over-engineered, and difficult to learn. But beneath OCL’s complicated exterior is an elegant language based on relational algebra. We call this language Aocl, which has a straightforward implementation in Java. Aocl can be used to write OCL-like constraints and model transformations in Java. A simple MDE tool generates an Aocl Java 8.0 package from an input class diagram for Aocl to be used.

Aocl : A Pure-Java Constraint and Transformation Language for MDE.

Feature models were invented in 1990 and have been recognised as one of the main contributions to the software product line community. Although there have been several attempts to establish a sort of standard variability modelling language, there is still no consensus. There can be many motivations to have one but there is one that is very important: information sharing among researchers, tools or developers. This first international workshop is an interactive event where all participants shall share knowledge about how to build up a simple variability modelling language that all the community can agree on.

First International Workshop on Languages for Modelling Variability (MODEVAR 2019)

Software architectures can be presented by graphs with components as nodes and connectors as edges. These graphs, or models, typically encode expert domain knowledge, which makes them difficult to understand. Hence, instead of presenting a complete complex model, we can derive it from a simple, easy-to-understand model by a set of easy-to-understand transformations. In two controlled experiments, we evaluate whether a derivation of a model is easier to understand than the model itself.

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Is the derivation of a model easier to understand than the model itself

Abstract Many analyses on configurable software systems are intractable when confronted with colossal and highly-constrained configuration spaces. These analyses could instead use statistical inference, where a tractable sample accurately predicts results for the entire space. To do so, the laws of statistical inference requires each member of the population to be equally likely to be included in the sample, i.e., the sampling process needs to be “uniform”. SAT-samplers have been developed to generate uniform random samples at a reasonable computational cost. However, there is a lack of experimental validation over colossal spaces to show whether the samplers indeed produce uniform samples or not. This paper (i) proposes a new sampler named , (ii) presents a new statistical test to verify sampler uniformity, and (iii) reports the evaluation of and five other state-of-the-art samplers: , , , , and . Our experimental results show only satisfies both scalability and uniformity. 

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Don Batory

Papers

Aocl : A Pure-Java Constraint and Transformation Language for MDE.

First International Workshop on Languages for Modelling Variability (MODEVAR 2019)

Is the derivation of a model easier to understand than the model itself

Uniform and scalable sampling of highly configurable systems

Proceedings of the 1st ACM SIGPLAN/SIGSOFT conference on Generative Programming and Component Engineering