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Sharing Data and Models in Software Engineering
Data Science for Software Engineering: Sharing Data and Models presents guidance and procedures for reusing data and models between projects to produce results that are useful and relevant. Starting with a background section of practical lessons and warnings for beginner data scientists for software engineering, this edited volume proceeds to identify critical questions of contemporary software engineering related to data and models. Learn how to adapt data from other organizations to local problems, mine privatized data, prune spurious information, simplify complex results, how to update models for new platforms, and more. Chapters share largely applicable experimental results discussed wit...
Software Design by Example
The best way to learn design in any field is to study examples, and some of the best examples of software design come from the tools programmers use in their own work. Software Design by Example: A Tool-Based Introduction with JavaScript therefore builds small versions of the things programmers use in order to demystify them and give some insights into how experienced programmers think. From a file backup system and a testing framework to a regular expression matcher, a browser layout engine, and a very small compiler, we explore common design patterns, show how making code easier to test also makes it easier to reuse, and help readers understand how debuggers, profilers, package managers, a...
Agile Processes in Software Engineering and Extreme Programming
Interest in agile development continues to grow: the number of practitioners adopting such methodologies is increasing as well as the number of researchers investigating the effectiveness of the different practices and proposing improvements. The XP c- ference series has actively participated in these processes and supported the evolution of Agile, promoting the conference as a place where practitioners and researchers meet to exchange ideas, experiences, and build connections. XP 2010 continued in the tradition of this conference series and provided an int- esting and varied program. As usual, we had a number of different kinds of activities in the conference program including: research pap...
Making Software
Many claims are made about how certain tools, technologies, and practices improve software development. But which claims are verifiable, and which are merely wishful thinking? In this book, leading thinkers such as Steve McConnell, Barry Boehm, and Barbara Kitchenham offer essays that uncover the truth and unmask myths commonly held among the software development community. Their insights may surprise you. Are some programmers really ten times more productive than others? Does writing tests first help you develop better code faster? Can code metrics predict the number of bugs in a piece of software? Do design patterns actually make better software? What effect does personality have on pair p...
Insolvent
How we can enact meaningful change in computing to meet the urgent need for sustainability and justice. The deep entanglement of information technology with our societies has raised hope for a transition to more sustainable and just communities—those that phase out fossil fuels, distribute public goods fairly, allow free access to information, and waste less. In principle, computing should be able to help. But in practice, we live in a world in which opaque algorithms steer us toward misinformation and unsustainable consumerism. Insolvent shows why computing’s dominant frame of thinking is conceptually insufficient to address our current challenges, and why computing continues to incur s...
Intelligent Systems and Machine Learning for Industry
The book explores the concepts and challenges in developing novel approaches using the Internet of Things, intelligent systems, machine intelligence systems, and data analytics in various industrial sectors such as manufacturing, smart agriculture, smart cities, food processing, environment, defense, stock market and healthcare. Further, it discusses the latest improvements in the industrial sectors using machine intelligence learning and intelligent systems techniques, especially robotics. Features: • Highlights case studies and solutions to industrial problems using machine learning and intelligent systems. • Covers applications in smart agriculture, smart healthcare, intelligent machi...
Product-Focused Software Process Improvement
This book constitutes the refereed proceedings of the 18th International Conference on Product-Focused Software Process Improvement, PROFES 2017, held in Innsbruck, Austria, in November/December 2017. The 17 revised full papers presented together with 10 short papers, 21 workshop papers. 3 posters and tool demonstrations papers, and 4 tutorials were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on : Agile software Development; Data science and analytics; Software engineering processes and frameworks; Industry relevant qualitative research; User and value centric approaches; Software startups; Serum; Software testing.
Intelligent Software Defect Prediction
With the increasing complexity of and dependency on software, software products may suffer from low quality, high prices, be hard to maintain, etc. Software defects usually produce incorrect or unexpected results and behaviors. Accordingly, software defect prediction (SDP) is one of the most active research fields in software engineering and plays an important role in software quality assurance. Based on the results of SDP analyses, developers can subsequently conduct defect localization and repair on the basis of reasonable resource allocation, which helps to reduce their maintenance costs. This book offers a comprehensive picture of the current state of SDP research. More specifically, it ...
Recommendation Systems in Software Engineering
With the growth of public and private data stores and the emergence of off-the-shelf data-mining technology, recommendation systems have emerged that specifically address the unique challenges of navigating and interpreting software engineering data. This book collects, structures and formalizes knowledge on recommendation systems in software engineering. It adopts a pragmatic approach with an explicit focus on system design, implementation, and evaluation. The book is divided into three parts: “Part I – Techniques” introduces basics for building recommenders in software engineering, including techniques for collecting and processing software engineering data, but also for presenting r...
