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"Object-oriented programming (Computer science)"
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Python GUI programming with tkinter : develop responsive and powerful GUI applications with tkinter
Tkinter is a lightweight, portable, and easy-to-use graphical toolkit available in the Python Standard Library. Due to its simplicity and availability, it is widely used to build GUIs in Python. This book teaches how to design and build a functional, appealing, and user-friendly GUI using one of the most powerful languages: Python with Tkinter.
eBook
Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes
Empirical validation of software metrics suites to predict fault proneness in object-oriented (OO) components is essential to ensure their practical use in industrial settings. In this paper, we empirically validate three OO metrics suites for their ability to predict software quality in terms of fault-proneness: the Chidamber and Kemerer (CK) metrics, Abreu's Metrics for Object-Oriented Design (MOOD), and Bansiya and Davis' Quality Metrics for Object-Oriented Design (QMOOD). Some CK class metrics have previously been shown to be good predictors of initial OO software quality. However, the other two suites have not been heavily validated except by their original proposers. Here, we explore the ability of these three metrics suites to predict fault-prone classes using defect data for six versions of Rhino, an open-source implementation of JavaScript written in Java. We conclude that the CK and QMOOD suites contain similar components and produce statistical models that are effective in detecting error-prone classes. We also conclude that the class components in the MOOD metrics suite are not good class fault-proneness predictors. Analyzing multivariate binary logistic regression models across six Rhino versions indicates these models may be useful in assessing quality in OO classes produced using modern highly iterative or agile software development processes.
Journal Article
Principles of package design : creating reusable software components
Apply design principles to your classes, preparing them for reuse. You will use package design principles to create packages that are just right in terms of cohesion and coupling, and are user- and maintainer-friendly at the same time. The first part of this book walks you through the five SOLID principles that will help you improve the design of your classes. The second part introduces you to the best practices of package design, and covers both package cohesion principles and package coupling principles. Cohesion principles show you which classes should be put together in a package, when to split packages, and if a combination of classes may be considered a \"package\" in the first place. Package coupling principles help you choose the right dependencies and prevent wrong directions in the dependency graph of your packages. What you'll learn: Apply the SOLID principles of class design ; Determine if classes belong in the same package ; Know whether it is safe for packages to depend on each other.
Book
Hands-On Functional Programming in Rust
Explore the support Rust offers for creating functional applications in Rust. Learn about various design patterns, implementing concurrency, metaprogramming, and so on in the processAbout This Book• Learn generics, organization, and design patterns in functional programming• Modularize your applications and make them highly reusable and testable using functional design patterns• Get familiar with complex concepts such as metaprogramming, concurrency, and immutabilityWho This Book Is ForThis book is for Rust developers who are comfortable with the language and now want to improve their coding abilities by learning advanced functional techniques to enhance their skillset and create robust and testable apps.What You Will Learn• How Rust supports the use of basic functional programming principles• Use functional programming to handle concurrency with elegance• Read and interpret complex type signatures for types and functions• Implement powerful abstractions using meta programming in Rust• Create quality code formulaically using Rust's functional design patterns• Master Rust's complex ownership mechanisms particularly for mutabilityIn DetailFunctional programming allows developers to divide programs into smaller, reusable components that ease the creation, testing, and maintenance of software as a whole. Combined with the power of Rust, you can develop robust and scalable applications that fulfill modern day software requirements. This book will help you discover all the Rust features that can be used to build software in a functional way.We begin with a brief comparison of the functional and object-oriented approach to different problems and patterns. We then quickly look at the patterns of control flow, data the abstractions of these unique to functional programming. The next part covers how to create functional apps in Rust; mutability and ownership, which are exclusive to Rust, are also discussed. Pure functions are examined next and you'll master closures, their various types, and currying. We also look at implementing concurrency through functional design principles and metaprogramming using macros. Finally, we look at best practices for debugging and optimization. By the end of the book, you will be familiar with the functional approach of programming and will be able to use these techniques on a daily basis.Style and approachStep-by-step guide covering advanced concepts and building functional applications in Rust
eBook
pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization
We present pyOpt, an object-oriented framework for formulating and solving nonlinear constrained optimization problems in an efficient, reusable and portable manner. The framework uses object-oriented concepts, such as class inheritance and operator overloading, to maintain a distinct separation between the problem formulation and the optimization approach used to solve the problem. This creates a common interface in a flexible environment where both practitioners and developers alike can solve their optimization problems or develop and benchmark their own optimization algorithms. The framework is developed in the Python programming language, which allows for easy integration of optimization software programmed in Fortran, C, C+ +, and other languages. A variety of optimization algorithms are integrated in pyOpt and are accessible through the common interface. We solve a number of problems of increasing complexity to demonstrate how a given problem is formulated using this framework, and how the framework can be used to benchmark the various optimization algorithms.
Journal Article
Parameter tuning or default values? An empirical investigation in search-based software engineering
Many software engineering problems have been addressed with search algorithms. Search algorithms usually depend on several parameters (e.g., population size and crossover rate in genetic algorithms), and the choice of these parameters can have an impact on the performance of the algorithm. It has been formally proven in the No Free Lunch theorem that it is impossible to
tune
a search algorithm such that it will have optimal settings for all possible problems. So, how to properly set the parameters of a search algorithm for a given software engineering problem? In this paper, we carry out the largest empirical analysis so far on parameter tuning in search-based software engineering. More than one million experiments were carried out and statistically analyzed in the context of test data generation for object-oriented software using the
EvoSuite
tool. Results show that tuning does indeed have impact on the performance of a search algorithm. But, at least in the context of test data generation, it does not seem easy to find good settings that significantly outperform the “default” values suggested in the literature. This has very practical value for both researchers (e.g., when different techniques are compared) and practitioners. Using “default” values is a reasonable and justified choice, whereas parameter tuning is a long and expensive process that might or might not pay off in the end.
Journal Article