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

Refactorings are behaviour-preserving program transformations that automate design-level changes in object-oriented applications. Our previous research established that many schema transformations, design patterns and hot-spot meta-patterns are automatable. This research evaluates whether refactoring technology can be transferred to the mainstream by restructuring non-trivial C++ applications. The applications that we examine were evolved manually by software engineers. We show that an equivalent evolution could be reproduced significantly faster and cheaper by applying a handful of general-purpose refactorings. In one application, over 14 K lines of code, that otherwise would have been coded by hand, were transformed automatically. Our experiments identify the benefits, limitations and topics of further research related to the transfer of refactoring technology to a production environment.

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Evolving object-oriented designs with refactorings

Programs of a software product line can be synthesized by composing modules that implement features. Besides high-level domain constraints that govern the compatibility of features, there are also low-level implementation constraints: a feature module can reference elements that are defined in other feature modules. Safe composition is the guarantee that all programs in a product line are type safe: i.e., absent of references to undefined elements (such as classes, methods, and variables). We show how safe composition properties can be verified for AHEAD product lines using feature models and SAT solvers.

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Safe composition of product lines

Customizable programs and program families provide user-selectable features to allow users to tailor a program to an application scenario. Knowing in advance which feature selection yields the best performance is difficult because a direct measurement of all possible feature combinations is infeasible. Our work aims at predicting program performance based on selected features. However, when features interact, accurate predictions are challenging. An interaction occurs when a particular feature combination has an unexpected influence on performance. We present a method that automatically detects performance-relevant feature interactions to improve prediction accuracy. To this end, we propose three heuristics to reduce the number of measurements required to detect interactions. Our evaluation consists of six real-world case studies from varying domains (e.g., databases, encoding libraries, and web servers) using different configuration techniques (e.g., configuration files and preprocessor flags). Results show an average prediction accuracy of 95 %.

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Predicting performance via automated feature-interaction detection

The Jakarta Tool Suite (JTS) aims to reduce substantially the cost of generator development by providing domain-independent tools for creating domain-specific languages and component-based generators called GenVoca generators. JTS is a set of precompiler-compiler tools for extending industrial programming languages (e.g., Java) with domain-specific constructs. JTS is itself a GenVoca generator where precompilers for JTS-extended languages are constructed from components.

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JTS: tools for implementing domain-specific languages

Current tool support for feature models is ad hoc, offering little or no support for debugging feature models or optimizing feature selections. Recent work shows how feature models can be reduced to propositional formulas or to constraint satisfaction problems, for which off-the-shelf tools can validate properties of models (e.g., confirming that a given set of features are incompatible or compatible) or to optimize the selection of features (e.g., performance) [1][2][4]. This opens up new possibilities for next-generation tools for specifying products in software product lines.

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Don Batory

Papers

Evolving object-oriented designs with refactorings

Safe composition of product lines

Predicting performance via automated feature-interaction detection

JTS: tools for implementing domain-specific languages

Automated analysis of feature models: challenges ahead