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"Object-oriented methods (Computer science)"
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Identification of Move Method Refactoring Opportunities
Placement of attributes/methods within classes in an object-oriented system is usually guided by conceptual criteria and aided by appropriate metrics. Moving state and behavior between classes can help reduce coupling and increase cohesion, but it is nontrivial to identify where such refactorings should be applied. In this paper, we propose a methodology for the identification of Move Method refactoring opportunities that constitute a way for solving many common feature envy bad smells. An algorithm that employs the notion of distance between system entities (attributes/methods) and classes extracts a list of behavior-preserving refactorings based on the examination of a set of preconditions. In practice, a software system may exhibit such problems in many different places. Therefore, our approach measures the effect of all refactoring suggestions based on a novel entity placement metric that quantifies how well entities have been placed in system classes. The proposed methodology can be regarded as a semi-automatic approach since the designer will eventually decide whether a suggested refactoring should be applied or not based on conceptual or other design quality criteria. The evaluation of the proposed approach has been performed considering qualitative, metric, conceptual, and efficiency aspects of the suggested refactorings in a number of open-source projects.
Journal Article
Design patterns in .NET : reusable approaches in C# and F# for object-oriented software design
\"Implement design patterns in .NET using the latest versions of the C# and F# languages. This book provides a comprehensive overview of the field of design patterns as they are used in today's developer toolbox. Using the C# programming language, \"Design patterns in .NET\" explores the classic design pattern implementation and discusses the applicability and relevance of specific language features for the purpose of implementing patterns. You will learn by example, reviewing scenarios where patterns are applicable. MVP and patterns expert Dmitri Nesteruk demonstrates possible implementations of patterns, discusses alternatives and pattern inter-relationships, and illustrates the way that a dedicated refactoring tool (ReSharper) can be used to implement design patterns with ease.\"-- Provided by publisher
Book
Empirical validation of object-oriented metrics on open source software for fault prediction
Open source software systems are becoming increasingly important these days. Many companies are investing in open source projects and lots of them are also using such software in their own work. But, because open source software is often developed with a different management style than the industrial ones, the quality and reliability of the code needs to be studied. Hence, the characteristics of the source code of these projects need to be measured to obtain more information about it. This paper describes how we calculated the object-oriented metrics given by Chidamber and Kemerer to illustrate how fault-proneness detection of the source code of the open source Web and e-mail suite called Mozilla can be carried out. We checked the values obtained against the number of bugs found in its bug database - called Bugzilla - using regression and machine learning methods to validate the usefulness of these metrics for fault-proneness prediction. We also compared the metrics of several versions of Mozilla to see how the predicted fault-proneness of the software system changed during its development cycle.
Journal Article
The NIRS Brain AnalyzIR Toolbox
Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low-levels of light (650–900 nm) to measure changes in cerebral blood volume and oxygenation. Over the last several decades, this technique has been utilized in a growing number of functional and resting-state brain studies. The lower operation cost, portability, and versatility of this method make it an alternative to methods such as functional magnetic resonance imaging for studies in pediatric and special populations and for studies without the confining limitations of a supine and motionless acquisition setup. However, the analysis of fNIRS data poses several challenges stemming from the unique physics of the technique, the unique statistical properties of data, and the growing diversity of non-traditional experimental designs being utilized in studies due to the flexibility of this technology. For these reasons, specific analysis methods for this technology must be developed. In this paper, we introduce the NIRS Brain AnalyzIR toolbox as an open-source Matlab-based analysis package for fNIRS data management, pre-processing, and first- and second-level (i.e., single subject and group-level) statistical analysis. Here, we describe the basic architectural format of this toolbox, which is based on the object-oriented programming paradigm. We also detail the algorithms for several of the major components of the toolbox including statistical analysis, probe registration, image reconstruction, and region-of-interest based statistics.
Journal Article
Large-Scale Live Active Learning: Training Object Detectors with Crawled Data and Crowds
Active learning and crowdsourcing are promising ways to efficiently build up training sets for object recognition, but thus far techniques are tested in artificially controlled settings. Typically the vision researcher has already determined the dataset’s scope, the labels “actively” obtained are in fact already known, and/or the crowd-sourced collection process is iteratively fine-tuned. We present an approach for
live learning
of object detectors, in which the system autonomously refines its models by actively requesting crowd-sourced annotations on images crawled from the Web. To address the technical issues such a large-scale system entails, we introduce a novel part-based detector amenable to linear classifiers, and show how to identify its most uncertain instances in sub-linear time with a hashing-based solution. We demonstrate the approach with experiments of unprecedented scale and autonomy, and show it successfully improves the state-of-the-art for the most challenging objects in the PASCAL VOC benchmark. In addition, we show our detector competes well with popular nonlinear classifiers that are much more expensive to train.
Journal Article
JAVA Basics Using ChatGPT/GPT-4
Encourages readers to compare and contrast hand-written code with ChatGPT-generated code. This approach fosters discussions on code efficiency, readability, and maintainability, enhancing understanding of programming paradigms and techniques. This book is designed for those new to Java and interested in understanding how ChatGPT/GPT-4 can enhance programming. It offers a unique approach to learning Java, combining traditional hand-written code with cutting-edge ChatGPT-generated examples. The book covers the basics of Java programming and development environments, including understanding recursion, strings, arrays, fundamental data structures, algorithm analysis, queues and stacks, and follows with the role of ChatGPT in generating, explaining, and debugging code. Companion files with source code and figures available for downloading. It’s an essential resource for those starting Java programming and for anyone curious about the applications of ChatGPT in coding.
eBook
Metaprogramming in Cyan
Certain languages allow a metaprogram to act as a compiler plugin and thus alter the compilation process. The metaprogram interacts with low-level details of the compiler, making its construction difficult and potentially leading to errors. Different parts of the metaprogram may have conflicting interactions, thus producing unintended outcomes. This article introduces metaprogramming in the prototype-based object-oriented language Cyan. This language provides the same core functionality as other metaprogramming systems while introducing features that improve interactions between the compiler and different components of the metaprogram. Further-more, Cyan incorporates security measures designed to circumvent typical issues encountered in metaprogramming.
Journal Article
Automatic test generation: a use case driven approach
Use cases are believed to be a good basis for system testing. Yet, to automate the test generation process, there is a large gap to bridge between high-level use cases and concrete test cases. We propose a new approach for automating the generation of system test scenarios in the context of object-oriented embedded software, taking into account traceability problems between high-level views and concrete test case execution. Starting from a formalization of the requirements based on use cases extended with contracts, we automatically build a transition system from which we synthesize test cases. Our objective is to cover the system in terms of statement coverage with those generated tests: an empirical evaluation of our approach is given based on this objective and several case studies. We briefly discuss the experimental deployment of our approach in the field at Thales Airborne Systems.
Journal Article