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Mining, Analyzing, and Evolving Data-Intensive Software Ecosystems

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Software Ecosystems

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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.

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Authors and Affiliations

  1. Software Institute, Università della Svizzera italiana, Lugano, Switzerland

    Csaba Nagy & Michele Lanza

  2. Faculté d’informatique, Université de Namur, Namur, Belgium

    Anthony Cleve

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  1. Csaba Nagy
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  2. Michele Lanza
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  3. Anthony Cleve
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Correspondence to Csaba Nagy .

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Editors and Affiliations

  1. Dept. Informatique, FS, University of Mons, Mons, Belgium

    Tom Mens

  2. Software Languages Lab, Vrije Universiteit Brussel, Brussels, Belgium

    Coen De Roover

  3. PReCISE research center, Namur Digital Institute (NaDI), University of Namur, Namur, Belgium

    Anthony Cleve

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Nagy, C., Lanza, M., Cleve, A. (2023). Mining, Analyzing, and Evolving Data-Intensive Software Ecosystems. In: Mens, T., De Roover, C., Cleve, A. (eds) Software Ecosystems. Springer, Cham. https://doi.org/10.1007/978-3-031-36060-2_11

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