Название: Crowdsourcing and Probabilistic Decision-Making in Software Engineering: Emerging Research and Opportunities
Автор: Varun Gupta
Издательство: IGI Global
Год: 2020
Страниц: 196
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

With todays technological advancements, the evolution of software has led to various challenges regarding mass markets and crowds. High quality processing must be capable of handling large groups in an efficient manner without error. Solutions that have been applied include Artificial Intelligence (AI) and natural language processing (NLP), but extensive research in this area has yet to be undertaken.

If the experience of the last few decades of Software Engineering is anything to go by, we seem to always be catching up with the innovations taking place in a hyperactive market, increasing the chances of developing systems that don’t fully take account of the needs of users, don’t meet legal obligations and end up compromising reliability, security and maintainability.

Fortunately, the software industry is good at learning as we go. Each innovation is typically followed by excitement in the market, some unfortunate system failures due to oversight in the requirements and, most importantly, a realisation that our Software Engineering methods need to adapt to meet the new demands. Examples of this learning process include an era where formal methods of software engineering were developed in the 90’s to meet the need to ensure reliability in safety critical applications, and more recently, the drive to adopt agile development methods to increase productivity and reduce risks. The most recent efforts to improve the software engineering process, which is the subject of this edited collection, is to utilise crowdsourcing and methods from Machine Learning (ML).

Crowdsourcing and Probabilistic Decision-Making in Software Engineering: Emerging Research and Opportunities is a pivotal reference source that provides vital research on the application of crowd-based software engineering and supports software engineers who want to improve the manner in which software is developed by increasing the accuracy of probabilistic reasoning to support their decision-making and getting automation support. While highlighting topics such as modeling techniques and programming practices, this publication is ideally designed for software developers, software engineers, computer engineers, executives, professionals, and researchers.