This book discusses various open issues in software engineering, such as the efficiency of automated testing techniques, predictions for cost estimation, data processing, and automatic code generation. Many traditional techniques are available for addressing these problems. But, with the rapid changes in software development, they often prove to be outdated or incapable of handling the software’s complexity. Hence, many previously used methods are proving insufficient to solve the problems now arising in software development. The book highlights a number of unique problems and effective solutions that reflect the state-of-the-art in software engineering. Deep learning is the latest computing technique, and is now gaining popularity in various fields of software engineering. This book explores new trends and experiments that have yielded promising solutions to current challenges in software engineering. As such, it offers a valuable reference guide for a broad audience including systems analysts, software engineers, researchers, graduate students and professors engaged in teaching software engineering.

Software is an integral part of our lives today. Modern software systems are highly complex and often pose new challenges in different aspects of Software Engineering (SE).Artificial Intelligence (AI) is a growing field in computer science that has been proven effective in applying and developing AI techniques to address various SE challenges.This unique compendium covers applications of state-of-the-art AI techniques to the key areas of SE (design, development, debugging, testing, etc).All the materials presented are up-to-date. This reference text will benefit researchers, academics, professionals, and postgraduate students in AI, machine learning and software engineering.Related Link(s)

Machine learning of software artefacts is an emerging area of interaction between the machine learning and software analysis communities. Increased productivity in software engineering relies on the creation of new adaptive, scalable tools that can analyse large and continuously changing software systems. These require new software analysis techniques based on machine learning, such as learning-based software testing, invariant generation or code synthesis. Machine learning is a powerful paradigm that provides novel approaches to automating the generation of models and other essential software artifacts. This volume originates from a Dagstuhl Seminar entitled "Machine Learning for Dynamic Software Analysis: Potentials and Limits” held in April 2016. The seminar focused on fostering a spirit of collaboration in order to share insights and to expand and strengthen the cross-fertilisation between the machine learning and software analysis communities. The book provides an overview of the machine learning techniques that can be used for software analysis and presents example applications of their use. Besides an introductory chapter, the book is structured into three parts: testing and learning, extension of automata learning, and integrative approaches.

This book constitutes the proceedings of the 17th International Conference on Theoretical Aspects of Software Engineering, TASE 2023, held in Bristol, UK, July 4–6, 2023. The 19 full papers and 2 short papers included in this book were carefully reviewed and selected from 49 submissions. They cover the following areas: distributed and concurrent systems; cyber-physical systems; embedded and real-time systems; object-oriented systems; quantum computing; formal verification and program semantics; static analysis; formal methods; verification and testing for AI systems; and AI for formal methods.

Machine learning deals with the issue of how to build computer programs that improve their performance at some tasks through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This book deals with the subject of machine learning applications in software engineering. It provides an overview of machine learning, summarizes the state-of-the-practice in this niche area, gives a classification of the existing work, and offers some application guidelines. Also included in the book is a collection of previously published papers in this research area.

Create better and easy-to-use deep learning models with AutoKeras Key FeaturesDesign and implement your own custom machine learning models using the features of AutoKerasLearn how to use AutoKeras for techniques such as classification, regression, and sentiment analysisGet familiar with advanced concepts as multi-modal, multi-task, and search space customizationBook Description AutoKeras is an AutoML open-source software library that provides easy access to deep learning models. If you are looking to build deep learning model architectures and perform parameter tuning automatically using AutoKeras, then this book is for you. This book teaches you how to develop and use state-of-the-art AI algorithms in your projects. It begins with a high-level introduction to automated machine learning, explaining all the concepts required to get started with this machine learning approach. You will then learn how to use AutoKeras for image and text classification and regression. As you make progress, you'll discover how to use AutoKeras to perform sentiment analysis on documents. This book will also show you how to implement a custom model for topic classification with AutoKeras. Toward the end, you will explore advanced concepts of AutoKeras such as working with multi-modal data and multi-task, customizing the model with AutoModel, and visualizing experiment results using AutoKeras Extensions. By the end of this machine learning book, you will be able to confidently use AutoKeras to design your own custom machine learning models in your company. What you will learnSet up a deep learning workstation with TensorFlow and AutoKerasAutomate a machine learning pipeline with AutoKerasCreate and implement image and text classifiers and regressors using AutoKerasUse AutoKeras to perform sentiment analysis of a text, classifying it as negative or positiveLeverage AutoKeras to classify documents by topicsMake the most of AutoKeras by using its most powerful extensionsWho this book is for This book is for machine learning and deep learning enthusiasts who want to apply automated ML techniques to their projects. Prior basic knowledge of Python programming and machine learning is expected to get the most out of this book.

This book provides insights into how deep learning techniques impact language and speech processing applications. The authors discuss the promise, limits and the new challenges in deep learning. The book covers the major differences between the various applications of deep learning and the classical machine learning techniques. The main objective of the book is to present a comprehensive survey of the major applications and research oriented articles based on deep learning techniques that are focused on natural language and speech signal processing. The book is relevant to academicians, research scholars, industrial experts, scientists and post graduate students working in the field of speech signal and natural language processing and would like to add deep learning to enhance capabilities of their work. Discusses current research challenges and future perspective about how deep learning techniques can be applied to improve NLP and speech processing applications; Presents and escalates the research trends and future direction of language and speech processing; Includes theoretical research, experimental results, and applications of deep learning.

New challenges have arisen for the construction of contemporary AI-based systems as a result of recent developments in artificial intelligence (AI), in particular machine learning (ML) and deep learning (DL), and their incorporation into software-based systems utilized in all sectors of the economy. These systems place a heavy reliance on data, are constantly evolving and bettering themselves, and display a degree of intrinsic nondeterminism. As a consequence, their behavior displays a degree of uncertainty that is universally acknowledged. As a result of these characteristics, the field of software engineering has to devise adaptable and innovative approaches to quality assurance (QA) that are capable of both constructive and in-depth analysis. This is essential in order to guarantee the product's high quality throughout the whole development process as well as while it is being put to use in actual settings. On the other hand, as Borg has pointed out, the concept of "quality" in AI-based systems does not yet have a definitive definition at this time. As was noted before, the terminology that is utilized in the field of artificial intelligence and software engineering is distinct from one another. When developing AI-based systems, the knowledge and experiences of a wide variety of organizations are combined and utilized in the construction process. While this does lead to new and creative ways, exciting breakthroughs, and a major advancement in what can be done with current AI-based systems, it also encourages the babel of language, concepts, perceptions, and underlying assumptions and principles. While this does lead to new and creative methods, exciting breakthroughs, and a substantial advancement in what can be done with current AI-based systems, this does lead to new and creative approaches. While this does result in novel and creative methods, exciting discoveries, and a significant leap forward in terms of what can be accomplished with contemporary AI-based systems, it does so in spite of the fact that. For instance, in the field of machine learning (ML), the term "regression" may be used to refer to regression models or regression analysis, but in the field of software engineering (SE), the term "regression" is used to refer to regression testing. However, in the context of machine learning (ML), the term "testing" refers to the evaluation of performance characteristics (such as accuracy) of a trained model using a holdout validation dataset. In the context of software engineering (SE), "testing" is described as the activity of executing the system in order to uncover errors. As a consequence of this, there is an increasing amount of confusion, as well as the potential of solutions that are in contradiction with one another, about how to approach quality assurance for AI-based systems and how to deal with the challenges that are associated with it. This is because of the fact that there are a growing number of solutions that are based on AI. Although the authors of this study begin their investigation from the perspective of software engineering, their ultimate goal is to include and talk about a wide variety of different points of view, all of which will eventually come together to provide a multi-dimensional picture of quality assurance for AI-based systems. While the authors of this study begin their investigation from the perspective of software engineering, their ultimate goal is to include and talk about a wide variety of different points of view. In the first part of this study project, our primary focus is on defining the terminologies related with artificial intelligence quality assurance. In the following section, Section 3, we will discuss the challenges that are involved with QA for AI. In the last part of the inquiry, we will summarize what we found and form our conclusions.

The book constitutes proceedings of the 12th Industry Symposium held in conjunction with the 18th edition of the International Conference on Distributed Computing and Intelligent Technology (ICDCIT 2022). The focus of the industry symposium is on Mobile Application Development: Practice and Experience. This book focuses on software engineering research and practice supporting any aspects of mobile application development. The book discusses findings in the areas of mobile application analysis, models for generating these applications, testing, debugging & repair, localization & globalization, app review analytics, app store mining, app beyond smartphones and tablets, app deployment, maintenance, and reliability of apps, industrial case studies of automated software engineering for mobile apps, etc. Papers included in the book describe new or improved ways to handle these aspects or address them in a more unified manner, discussing benefits, limitations, and costs of provided solutions. The volume will be useful for master, research students as well as industry professionals.