Mining, analyzing, and evolving data-intensive software ecosystems

Nagy, Csaba; Lanza, Michele; Cleve, Anthony

doi:10.1007/978-3-031-36060-2_11

Back

Book chapter

Mining, analyzing, and evolving data-intensive software ecosystems

Nagy, Csaba ORCID Istituto del software (SI), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
Lanza, Michele ORCID Istituto del software (SI), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
Cleve, Anthony Faculté d’informatique, Université de Namur, Belgium

2023

Published in:

Software ecosystems / Tom Mens ; Coen De Roover ; Anthony Cleve . - Cham : Springer International Publishing. - 2023, p. 281–314

English 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.

Collections