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Applying declarative analysis to industrial automotive software product line models

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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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Data Availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Notes

  1. We use the syntax !, ∧, ∨ for the propositional operators not, and, and or, respectively, to be consistent with the syntax of PCs used in our interactive visualization tool (please see Section 4).

  2. https://clang.llvm.org/index.html

  3. The names of the entities are simplified for this example to improve legibility. In practice, Rex creates long identifier names that capture the entity’s context (i.e., enclosing function, class, etc., up to and including filename).

  4. https://www.misra.org.uk/

  5. https://neo4j.com/developer/neo4j-browser/

  6. https://neo4j.com/

  7. https://github.com/meteor/logic-solver

  8. Node labels and presence conditions on edges have been omitted from Fig. 8(b) to avoid revealing proprietary information.

  9. Neo4j Browser manual. https://neo4j.com/docs/browser-manual/current/

  10. Configuration parameters are feature variables, which were described in Section 2.1.

  11. This measurement was aided by the fact that the presence conditions are stored as Binary Decision Diagrams (BDDs) (Bryant 1992), and BDDs have canonical representations.

  12. https://github.com/toledorafael/emse2022userSurvey

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Authors and Affiliations

  1. University of Toronto, Toronto, ON, Canada

    Ramy Shahin & Marsha Chechik

  2. University of Waterloo, Waterloo, ON, Canada

    Rafael Toledo, Robert Hackman & Joanne M. Atlee

  3. General Motors, Warren, MI, USA

    Ramesh S

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  1. Ramy Shahin
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  2. Rafael Toledo
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  3. Robert Hackman
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Correspondence to Ramy Shahin.

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Communicated by: Sigrid Eldh, Davide Falessi, Burak Turhan

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Shahin, R., Toledo, R., Hackman, R. et al. Applying declarative analysis to industrial automotive software product line models. Empir Software Eng 28, 40 (2023). https://doi.org/10.1007/s10664-023-10290-2

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