Mining, analyzing, and evolving data-intensive software ecosystems
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Nagy, Csaba
ORCID
Istituto del software (SI), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
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Lanza, Michele
ORCID
Istituto del software (SI), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
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Cleve, Anthony
Faculté d’informatique, Université de Namur, Belgium
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.
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Classification
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Computer science and technology
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License
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License undefined
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Open access status
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green
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Persistent URL
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https://n2t.net/ark:/12658/srd1329625
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