Software Quality Future Perspectives On Software Engineering Quality
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Software Quality: Future Perspectives on Software Engineering Quality
This book constitutes the refereed proceedings of the 13th Software Quality Days Conference, SWQD 2021, which was planned to be held in Vienna, Austria, during January 19–21, 2021. Due to the COVID-19 pandemic, the conference was cancelled and will be merged with SWQD 2022. The Software Quality Days (SWQD) conference started in 2009 and has grown to the biggest conference on software quality in Europe with a strong community. The program of the SWQD conference is designed to encompass a stimulating mixture of practical presentations and new research topics in scientific presentations. The guiding conference topic of the SWQD 2021 is “Future Perspectives on Software Engineering Quality”. The 3 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 13 submissions. The volume also contains 2 invited talks and one introductory paper for an interactive session. The contributions were organized in topical sections named: automation in software engineering; quality assurance for AI-based systems; machine learning applications; industry-academia collaboration; and experimentation in software engineering.
Machine Learning Infrastructure and Best Practices for Software Engineers
Author: Miroslaw Staron
language: en
Publisher: Packt Publishing Ltd
Release Date: 2024-01-31
Efficiently transform your initial designs into big systems by learning the foundations of infrastructure, algorithms, and ethical considerations for modern software products Key Features Learn how to scale-up your machine learning software to a professional level Secure the quality of your machine learning pipeline at runtime Apply your knowledge to natural languages, programming languages, and images Book DescriptionAlthough creating a machine learning pipeline or developing a working prototype of a software system from that pipeline is easy and straightforward nowadays, the journey toward a professional software system is still extensive. This book will help you get to grips with various best practices and recipes that will help software engineers transform prototype pipelines into complete software products. The book begins by introducing the main concepts of professional software systems that leverage machine learning at their core. As you progress, you’ll explore the differences between traditional, non-ML software, and machine learning software. The initial best practices will guide you in determining the type of software you need for your product. Subsequently, you will delve into algorithms, covering their selection, development, and testing before exploring the intricacies of the infrastructure for machine learning systems by defining best practices for identifying the right data source and ensuring its quality. Towards the end, you’ll address the most challenging aspect of large-scale machine learning systems – ethics. By exploring and defining best practices for assessing ethical risks and strategies for mitigation, you will conclude the book where it all began – large-scale machine learning software.What you will learn Identify what the machine learning software best suits your needs Work with scalable machine learning pipelines Scale up pipelines from prototypes to fully fledged software Choose suitable data sources and processing methods for your product Differentiate raw data from complex processing, noting their advantages Track and mitigate important ethical risks in machine learning software Work with testing and validation for machine learning systems Who this book is for If you’re a machine learning engineer, this book will help you design more robust software, and understand which scaling-up challenges you need to address and why. Software engineers will benefit from best practices that will make your products robust, reliable, and innovative. Decision makers will also find lots of useful information in this book, including guidance on what to look for in a well-designed machine learning software product.
Software Quality and Productivity
As the world becomes increasingly dependent on the use of computers, the need for quality software which can be produced at reasonable cost increases. This IFIP proceedings brings together the work of leading researchers and practitioners who are concerned with the efficient production of quality software.