Mining, analyzing, and evolving data-intensive software ecosystems
Additional information
Authors
Nagy C.,
Lanza M.,
Cleve A.
Type
Book chapter
Year
2023
Language
English
Abstract
Managing data-intensive software ecosystems has long been considered an expensive and error-prone process. This is mainly due to the often implicit consistency relationships between applications and their database(s). In addition, as new technologies emerged for specialized purposes (e.g., key-value stores, document stores, graph databases), the common use of multiple database models within the same software (eco)system has also become more popular. There are undeniable benefits of such multi-database models where developers use and combine technologies. However, the side effects on database design, querying, and maintenance are not well-known. This chapter elaborates on the recent research effort devoted to mining, analyzing, and evolving data-intensive software ecosystems. It focuses on methods, techniques, and tools providing developers with automated support. It covers different processes, including automatic database query extraction, bad smell detection, self-admitted technical debt analysis, and evolution history visualization.
Book
Software ecosystems
Publisher
Springer International Publishing
Place of publication
Cham
Pages (or article number)
281–314
Diffusion
License
License undefined
Visibility
Public
Status open access
Green
