Miroslaw Staron

Software Engineering professionals now face challenges in incorporating GAI into the products and programs they are developing. At this point, the knowledge about developing AI-based software is mostly based on classical AI, i.e., non-generative ML systems. Developers know how to use machine learning and, to some extent, how to include it in production systems. Engineering Generative-AI Based Software takes software development to the next level by using generative AI instead. Readers learn how to use text, image and audio models as part of larger software systems. The book discusses both the process of developing such software and the architectures for this kind of software, combining theory with practice. Generative AI software is gaining popularity thanks to such models as GPT-4 or Llama. More and more products use them as part of their feature portfolio, but this software is often limited to web applications or recommendation systems. Author Miroslav Staron shows readers how to tackle the challenges of professionally engineering generative AI-based systems. The book starts by reviewing the most relevant models and technologies in this area, both theoretically and practically. Once readers know the technologies, the book goes into details of software engineering practices for such systems, e.g., eliciting functional and non-functional requirements specific to generative AI, various architectural styles and tactics for such systems, and different programming platforms. The book also shows how to create robust licensing models and the technology to support them. Finally, readers learn how to manage data, both during the training and also when generating new data, as well as how to use the generated data and user feedback to constantly evolve generative AI-based software.

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 forIf 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.

This book introduces the concept of software architecture as one of the cornerstones of software in modern cars. Following a historical overview of the evolution of software in modern cars and a discussion of the main challenges driving that evolution, Chapter 2 describes the main architectural styles of automotive software and their use in cars’ software. Chapter 3 details this further by presenting two modern architectural styles, i.e. centralized and federated software architectures. In Chapter 4, readers will find a description of the software development processes used to develop software on the car manufacturers’ side. Chapter 5 then introduces AUTOSAR – an important standard in automotive software. Chapter 6 goes beyond simple architecture and describes the detailed design process for automotive software using Simulink, helping readers to understand how detailed design links to high-level design. The new chapter 7 reports on how machine learning is exploited in automotive software e.g. for image recognition and how both on-board and off-board learning are applied. Next, Chapter 8 presents a method for assessing the quality of the architecture – ATAM (Architecture Trade-off Analysis Method) – and provides a sample assessment, while Chapter 9 presents an alternative way of assessing the architecture, namely by using quantitative measures and indicators. Subsequently Chapter 10 dives deeper into one of the specific properties discussed in Chapter 8 – safety – and details an important standard in that area, the ISO/IEC 26262 norm. Lastly, Chapter 11 presents a set of future trends that are currently emerging and have the potential to shape automotive software engineering in the coming years. This book explores the concept of software architecture for modern cars and is intended for both beginning and advanced software designers. It mainly aims at two different groups of audience – professionals working with automotive software who need to understandconcepts related to automotive architectures, and students of software engineering or related fields who need to understand the specifics of automotive software to be able to construct cars or their components. Accordingly, the book also contains a wealth of real-world examples illustrating the concepts discussed and requires no prior background in the automotive domain. Compared to the first edition, besides the two new chapters 3 and 7 there are considerable updates in chapters 5 and 8 especially.

This book introduces the concept of software architecture as one of the cornerstones of software in modern cars. Following a historical overview of the evolution of software in modern cars and a discussion of the main challenges driving that evolution, Chapter 2 describes the main architectural styles of automotive software and their use in cars’ software. Chapter 3 details this further by presenting two modern architectural styles, i.e. centralized and federated software architectures. In Chapter 4, readers will find a description of the software development processes used to develop software on the car manufacturers’ side. Chapter 5 then introduces AUTOSAR – an important standard in automotive software. Chapter 6 goes beyond simple architecture and describes the detailed design process for automotive software using Simulink, helping readers to understand how detailed design links to high-level design. The new chapter 7 reports on how machine learning is exploited in automotive software e.g. for image recognition and how both on-board and off-board learning are applied. Next, Chapter 8 presents a method for assessing the quality of the architecture – ATAM (Architecture Trade-off Analysis Method) – and provides a sample assessment, while Chapter 9 presents an alternative way of assessing the architecture, namely by using quantitative measures and indicators. Subsequently Chapter 10 dives deeper into one of the specific properties discussed in Chapter 8 – safety – and details an important standard in that area, the ISO/IEC 26262 norm. Lastly, Chapter 11 presents a set of future trends that are currently emerging and have the potential to shape automotive software engineering in the coming years. This book explores the concept of software architecture for modern cars and is intended for both beginning and advanced software designers. It mainly aims at two different groups of audience – professionals working with automotive software who need to understandconcepts related to automotive architectures, and students of software engineering or related fields who need to understand the specifics of automotive software to be able to construct cars or their components. Accordingly, the book also contains a wealth of real-world examples illustrating the concepts discussed and requires no prior background in the automotive domain. Compared to the first edition, besides the two new chapters 3 and 7 there are considerable updates in chapters 5 and 8 especially.

This book addresses action research (AR), one of the main research methodologies used for academia-industry research collaborations. It elaborates on how to find the right research activities and how to distinguish them from non-significant ones. Further, it details how to glean lessons from the research results, no matter whether they are positive or negative. Lastly, it shows how companies can evolve and build talents while expanding their product portfolio.The book’s structure is based on that of AR projects; it sequentially covers and discusses each phase of the project. Each chapter shares new insights into AR and provides the reader with a better understanding of how to apply it. In addition, each chapter includes a number of practical use cases or examples. Taken together, the chapters cover the entire software lifecycle: from problem diagnosis to project (or action) planning and execution, to documenting and disseminating results, including validity assessments for AR studies.The goal of this book is to help everyone interested in industry-academia collaborations to conduct joint research. It is for students of software engineering who need to learn about how to set up an evaluation, how to run a project, and how to document the results. It is for all academics who aren’t afraid to step out of their comfort zone and enter industry. It is for industrial researchers who know that they want to do more than just develop software blindly. And finally, it is for stakeholders who want to learn how to manage industrial research projects and how to set up guidelines for their own role and expectations.

This book addresses action research (AR), one of the main research methodologies used for academia-industry research collaborations. It elaborates on how to find the right research activities and how to distinguish them from non-significant ones. Further, it details how to glean lessons from the research results, no matter whether they are positive or negative. Lastly, it shows how companies can evolve and build talents while expanding their product portfolio.The book’s structure is based on that of AR projects; it sequentially covers and discusses each phase of the project. Each chapter shares new insights into AR and provides the reader with a better understanding of how to apply it. In addition, each chapter includes a number of practical use cases or examples. Taken together, the chapters cover the entire software lifecycle: from problem diagnosis to project (or action) planning and execution, to documenting and disseminating results, including validity assessments for AR studies.The goal of this book is to help everyone interested in industry-academia collaborations to conduct joint research. It is for students of software engineering who need to learn about how to set up an evaluation, how to run a project, and how to document the results. It is for all academics who aren’t afraid to step out of their comfort zone and enter industry. It is for industrial researchers who know that they want to do more than just develop software blindly. And finally, it is for stakeholders who want to learn how to manage industrial research projects and how to set up guidelines for their own role and expectations.

This book seeks to promote the structured, standardized and accurate use of software measurement at all levels of modern software development companies. To do so, it focuses on seven main aspects: sound scientific foundations, cost-efficiency, standardization, value-maximization, flexibility, combining organizational and technical aspects, and seamless technology integration. Further, it supports companies in their journey from manual reporting to automated decision support by combining academic research and industrial practice.When scientists and engineers measure something, they tend to focus on two different things. Scientists focus on the ability of the measurement to quantify whatever is being measured; engineers, however, focus on finding the right qualities of measurement given the designed system (e.g. correctness), the system’s quality of use (e.g. ease of use), and the efficiency of the measurement process. In this book, the authors arguethat both focuses are necessary, and that the two are complementary. Thus, the book is organized as a gradual progression from theories of measurement (yes, you need theories to be successful!) to practical, organizational aspects of maintaining measurement systems (yes, you need the practical side to understand how to be successful).The authors of this book come from academia and industry, where they worked together for the past twelve years. They have worked with both small and large software development organizations, as researchers and as measurement engineers, measurement program leaders and even teachers. They wrote this book to help readers define, implement, deploy and maintain company-wide measurement programs, which consist of a set of measures, indicators and roles that are built around the concept of measurement systems. Based on their experiences introducing over 40,000 measurement systems at over a dozen companies, they share essential tips and tricks on how to do it right and how to avoid common pitfalls.

This book seeks to promote the structured, standardized and accurate use of software measurement at all levels of modern software development companies. To do so, it focuses on seven main aspects: sound scientific foundations, cost-efficiency, standardization, value-maximization, flexibility, combining organizational and technical aspects, and seamless technology integration. Further, it supports companies in their journey from manual reporting to automated decision support by combining academic research and industrial practice.When scientists and engineers measure something, they tend to focus on two different things. Scientists focus on the ability of the measurement to quantify whatever is being measured; engineers, however, focus on finding the right qualities of measurement given the designed system (e.g. correctness), the system’s quality of use (e.g. ease of use), and the efficiency of the measurement process. In this book, the authors arguethat both focuses are necessary, and that the two are complementary. Thus, the book is organized as a gradual progression from theories of measurement (yes, you need theories to be successful!) to practical, organizational aspects of maintaining measurement systems (yes, you need the practical side to understand how to be successful).The authors of this book come from academia and industry, where they worked together for the past twelve years. They have worked with both small and large software development organizations, as researchers and as measurement engineers, measurement program leaders and even teachers. They wrote this book to help readers define, implement, deploy and maintain company-wide measurement programs, which consist of a set of measures, indicators and roles that are built around the concept of measurement systems. Based on their experiences introducing over 40,000 measurement systems at over a dozen companies, they share essential tips and tricks on how to do it right and how to avoid common pitfalls.

This book introduces the concept of software architecture as one of the cornerstones of software in modern cars. Following a historical overview of the evolution of software in modern cars and a discussion of the main challenges driving that evolution, Chapter 2 describes the main architectural styles of automotive software and their use in cars’ software. In Chapter 3, readers will find a description of the software development processes used to develop software on the car manufacturers’ side. Chapter 4 then introduces AUTOSAR – an important standard in automotive software. Chapter 5 goes beyond simple architecture and describes the detailed design process for automotive software using Simulink, helping readers to understand how detailed design links to high-level design. Next, Chapter 6 presents a method for assessing the quality of the architecture – ATAM (Architecture Trade-off Analysis Method) – and provides a sample assessment, while Chapter 7 presents an alternative way ofassessing the architecture, namely by using quantitative measures and indicators. Subsequently Chapter 8 dives deeper into one of the specific properties discussed in Chapter 6 – safety – and details an important standard in that area, the ISO/IEC 26262 norm. Lastly, Chapter 9 presents a set of future trends that are currently emerging and have the potential to shape automotive software engineering in the coming years.This book explores the concept of software architecture for modern cars and is intended for both beginning and advanced software designers. It mainly aims at two different groups of audience – professionals working with automotive software who need to understand concepts related to automotive architectures, and students of software engineering or related fields who need to understand the specifics of automotive software to be able to construct cars or their components. Accordingly, the book also contains a wealth of real-world examples illustrating the concepts discussed and requires no prior background in the automotive domain.