research-article

Open access

Classifying edits to variability in source code

Authors:

Paul Maximilian Bittner,

Christof Tinnes,

Alexander Schultheiß,

Sören Viegener,

Thomas ThümAuthors Info & Claims

ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Pages 196 - 208

https://doi.org/10.1145/3540250.3549108

Published: 09 November 2022 Publication History

Abstract

For highly configurable software systems, such as the Linux kernel, maintaining and evolving variability information along changes to source code poses a major challenge. While source code itself may be edited, also feature-to-code mappings may be introduced, removed, or changed. In practice, such edits are often conducted ad-hoc and without proper documentation. To support the maintenance and evolution of variability, it is desirable to understand the impact of each edit on the variability. We propose the first complete and unambiguous classification of edits to variability in source code by means of a catalog of edit classes. This catalog is based on a scheme that can be used to build classifications that are complete and unambiguous by construction. To this end, we introduce a complete and sound model for edits to variability. In about 21.5ms per commit, we validate the correctness and suitability of our classification by classifying each edit in 1.7 million commits in the change histories of 44 open-source software systems automatically. We are able to classify all edits with syntactically correct feature-to-code mappings and find that all our edit classes occur in practice.

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Classifying edits to variability in source code
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cover image ACM Conferences

ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

November 2022

1822 pages

ISBN:9781450394130

DOI:10.1145/3540250

General Chair:
Abhik Roychoudhury
National University of Singapore, Singapore
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Imperial College London, UK
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University of California at Los Angeles, USA

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Cited By

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https://dl.acm.org/doi/10.1145/3689747
Greiner SSchultheiß ABittner PThüm TKehrer T(2024)Give an Inch and Take a Mile? Effects of Adding Reliable Knowledge to Heuristic Feature TracingProceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3672593(84-95)Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1145/3646548.3672593
Schultheiß ABittner PGreiner SKehrer T(2023)Benchmark Generation with VEVOS: A Coverage Analysis of Evolution Scenarios in Variant-Rich SystemsProceedings of the 17th International Working Conference on Variability Modelling of Software-Intensive Systems10.1145/3571788.3571793(13-22)Online publication date: 25-Jan-2023
https://dl.acm.org/doi/10.1145/3571788.3571793
Kuiter EKrieter SSundermann CThüm TSaake G(2022)Tseitin or not Tseitin? The Impact of CNF Transformations on Feature-Model AnalysesProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3556938(1-13)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3551349.3556938

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