Jo Atlee, Krzysztof Czarnecki and their former students receive ten-year most influential paper award at VaMoS 2023

Friday, November 25, 2022

Computer Science Professor Jo Atlee, Electrical and Computer Engineering Professor Krzysztof Czarnecki, and their former students have been awarded a ten-year most influential paper award to be conferred at VaMoS 2023. Also known as the 17th International Working Conference on Variability Modelling of Software-Intensive Systems, the annual meeting will take place from January 25 to 27, 2023 in Odense, Denmark.

Their paper, A survey of variability modeling in industrial practice, was selected for the prestigious ten-year most influential paper award by the VaMoS 2023 program chairs because of its significant impact, both academically and industrially, on the software engineering community since its publication a decade ago.

photo of Professors Jo Atlee and Krzysztof Czarnecki in the Waterloo rock garden

L to R: Professors Jo Atlee from the Cheriton School of Computer Science and Krzysztof Czarnecki from the Department of Electrical and Computer Engineering and cross-appointed to the Cheriton School of Computer Science.

Jo Atlee’s research is in software engineering with a particular focus on improving the quality of software. Her work focuses on modelling software in terms of its features and on detecting, resolving and managing feature interactions, where the goal is to support the rapid development of new features, services and modular components that are oblivious of each other yet interoperate well.

Krzysztof Czarnecki leads the Waterloo Intelligent Systems Engineering Laboratory. His current research focuses on autonomous driving and assuring the safety of systems that rely on artificial intelligence. He serves on SAE task forces on level of driving automation, reference architecture for automated driving systems, verification and validation, and maneuvers and behaviours. As part of this research, he co-leads the development of UW Moose, Canada’s first self-driving research vehicle to be tested on public roads since 2018.

About this award-winning research

Variability modelling — the discipline of representing variability explicitly in dedicated models that describe common and variable characteristics of products in a software product line — is core to many software product line engineering methodologies in academic research and industrial practice. Despite the importance of techniques to model variability little is known about their use, a deficiency that not only undermines their validity but also hinders their improvement.

To address this gap, Professors Atlee and Czarnecki, along with their students and international colleagues, conducted an online survey of industrial variability modelling practices to answer the following questions —

  1. What variability modelling notations and tools are used?
  2. What are the scales of industrial models?
  3. What are perceived benefits and challenges of variability modelling?

The questionnaire also aimed at putting variability modeling into context by identifying the domain and adoption strategies used in product line projects, the artifacts whose variability is described by models, and the roles and experience of respondents. In total, 42 individuals from 16 countries responded, of which 35 were employed in industry, providing an overview of industrial practice.

The survey revealed that industrial product line developers use a much wider application of variability technologies than what was thought to be the case from earlier studies of open-source practice, exceeding simple product configuration and extending to variability management in requirements specification, design, and architecture planning and to product derivation.

The survey also documented a great deal of diversity in the size and extent of detail in variability models. Most respondents used models with fewer than 50 units of variability (i.e., features, variation points, calibration parameters), but about a quarter used models with more than 10,000 units, indicating that variability models have use cases specific to the organization, and that these different requirements need to be satisfied by tools.

The survey also found a great deal of variability in notations and tools, with feature models and tools based on feature modelling clearly dominating. This high diversity also suggested that industry has yet to adopt a gold standard.

Most respondents used re-active and extractive strategies for introducing product lines. In contrast, software product line engineering research primarily focused on pro-active strategies, starting with domain analysis and architectural design. This suggests that the community may need to refocus software product line engineering research towards methods and tools — such as re-engineering and reverse-engineering — that support development of product lines from existing legacy software assets. 

Product line engineers undoubtedly face many technical and organizational challenges. The survey revealed that technical challenges primarily include evolution and visualization of models, followed by dependency management. Process-oriented challenges centred on ensuring support in the organization.

To learn more about the research on which this article is based, please see Thorsten Berger, Ralf Rublack, Divya Nair, Joanne M. Atlee, Martin Becker, Krzysztof Czarnecki, Andrzej Wąsowski. A survey of variability modeling in industrial practice. VaMoS ‘13: Proceedings of the Seventh International Workshop on Variability Modelling of Software-intensive Systems, January 2013, article no. 7, pages 1–8.

  1. 2024 (57)
    1. June (11)
    2. May (15)
    3. April (9)
    4. March (13)
    5. February (1)
    6. January (8)
  2. 2023 (70)
    1. December (6)
    2. November (7)
    3. October (7)
    4. September (2)
    5. August (3)
    6. July (7)
    7. June (8)
    8. May (9)
    9. April (6)
    10. March (7)
    11. February (4)
    12. January (4)
  3. 2022 (63)
    1. December (2)
    2. November (7)
    3. October (6)
    4. September (6)
    5. August (1)
    6. July (3)
    7. June (7)
    8. May (8)
    9. April (7)
    10. March (6)
    11. February (6)
    12. January (4)
  4. 2021 (64)
  5. 2020 (73)
  6. 2019 (90)
  7. 2018 (82)
  8. 2017 (51)
  9. 2016 (27)
  10. 2015 (41)
  11. 2014 (32)
  12. 2013 (46)
  13. 2012 (17)
  14. 2011 (20)