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P279 - Software Engineering und Software Management 2018

https://dl.gi.de/handle/20.500.12116/21117
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Auflistung P279 - Software Engineering und Software Management 2018 nach Autor:in "Behringer, Benjamin"

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  • Konferenzbeitrag
    Projectional Editing of Product Lines
    (Software Engineering und Software Management 2018, 2018) Behringer, Benjamin; Palz, Jochen; Berger, Thorsten
    Published at International Conference on Software Engineering (ICSE) 2017, main research track. The features of a software product line --a portfolio of system variants --can be realized using various implementation techniques (a.k.a., variability mechanisms). Each technique represents the software artifacts of features differently, typically classified into annotative (e.g., C preprocessor) and modular representations (e.g., feature modules), each with distinct advantages and disadvantages. Annotative representations are easy to realize, but annotations clutter source code and hinder program comprehension. Modular representations support comprehension, but are difficult to realize. Most importantly, to engineer feature artifacts, developers need to choose one representation and adhere to it for evolving and maintaining the same artifacts. We present PEoPL, an approach to combine the advantages of annotative and modular representations. When engineering a feature artifact, developers can choose the most-suited representation and even use different representations in parallel. PEoPL relies on separating a product line into an internal and external representation, the latter by providing editable projections used by the developers. We contribute a programming-language-independent internal representation of variability, five editable projections reflecting different variability representations, a supporting IDE, and a tailoring to Java. We evaluate PEoPL's expressiveness, scalability, and flexibility in eight Java-based product lines, finding that all can be realized, that projections are feasible, and that variant computation is fast (<45ms on average for our largest subject Berkeley DB).