PEoPL: Projectional Editing of Product Lines
Paper in proceeding, 2017

© 2017 IEEE. 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 of PEoPL 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 (

Author

Benjamin Behringer

University of Luxembourg

Jochen Palz

Htw Saar

Thorsten Berger

University of Gothenburg

Proceedings - 2017 IEEE/ACM 39th International Conference on Software Engineering, ICSE 2017

563-574 7985694

39th IEEE/ACM International Conference on Software Engineering, ICSE 2017
Buenos Aires, Argentina,

Subject Categories

Software Engineering

Embedded Systems

Computer Science

DOI

10.1109/ICSE.2017.58

More information

Latest update

3/14/2022