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Empirical Software Engineering

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01-03-2023

Applying declarative analysis to industrial automotive software product line models

Authors: Ramy Shahin, Rafael Toledo, Robert Hackman, Ramesh S, Joanne M. Atlee, Marsha Chechik

Published in: Empirical Software Engineering | Issue 2/2023

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Abstract

Program analysis of automotive software has several unique challenges, including that the code base is ultra large, comprising over a hundred million lines of code running on a single vehicle; the code is structured as a software product line (SPL) for managing a family of related software products from a common set of artifacts; and the analysis results (despite being numerous and despite being variable) need to be presented to the engineer in a way that is manageable. In previous work, we reported on lifting declarative analyses to apply to a software product line, rather than to an individual product variant. This paper reports on milestone results from applying lifted declarative analyses (behaviour alteration, recursion analysis, simplifiable global variable analysis, and two of their variants) to automotive software product lines from General Motors and assessing the scalability of the analyses and the effectiveness of reporting to engineers conditional analysis results (i.e., results conditioned on SPL program variants). We also reflect on some of the lessons learned throughout this project.

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Title: Applying declarative analysis to industrial automotive software product line models
Authors: Ramy Shahin
Rafael Toledo
Robert Hackman
Ramesh S
Joanne M. Atlee
Marsha Chechik
Publication date: 01-03-2023
Publisher: Springer US
Published in: Empirical Software Engineering / Issue 2/2023
Print ISSN: 1382-3256
Electronic ISSN: 1573-7616
DOI: https://doi.org/10.1007/s10664-023-10290-2

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