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REVE 2021: 9th International Workshop on Reverse Variability Engineering

Published: 06 September 2021 Publication History

Abstract

Software Product Line (SPL) migration remains a challenging endeavour. From organizational issues to purely technical challenges, there is a wide range of barriers that complicates SPL adoption. This workshop aims to foster research about making the most of the two main inputs for SPL migration: 1) domain knowledge and 2) legacy assets. Domain knowledge, usually implicit and spread across an organization, is key to define the SPL scope and to validate the variability model and its semantics. At the technical level, domain expertise is also needed to create or extract the reusable software components. Legacy assets can be, for instance, similar product variants (e.g., requirements, models, source code, etc.) that were implemented using ad-hoc reuse techniques such as clone-and-own. More generally, the workshop REverse Variability Engineering attracts researchers and practitioners contributing to processes, techniques, tools, or empirical studies related to the automatic, semi-automatic or manual extraction or refinement of SPL assets.

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cover image ACM Conferences
SPLC '21: Proceedings of the 25th ACM International Systems and Software Product Line Conference - Volume A
September 2021
239 pages
ISBN:9781450384698
DOI:10.1145/3461001
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 September 2021

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Author Tags

  1. extractive software product line adoption
  2. reverse engineering
  3. software product lines
  4. variability management

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SPLC '21
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Overall Acceptance Rate 167 of 463 submissions, 36%

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