Masterseminar

Context

Editing variational systems can be a mentally challenging task as it requires to understand and maintain multiple features and their interactions simultaneously. Variation control systems promote development of software product lines in terms of projectional editing: Instead of editing the complete software product line, developers edit a view of the software that shows only relevant parts of the code such as all code elements that belong to a particular feature or variant.

Goal / Research Questions

• Describe the workflow of developers in variation control systems.
• Describe how the workflow in vartiation control systems differs from the workflow with version control systems, and how they interact.
• Investigate challenges and edge cases.

Papers

• Eric Walkingshaw and Klaus Ostermann. “Projectional Editing of Variational Software”. In: Proc. Int’l Conf. on Generative Programming: Concepts & Experiences (GPCE). ACM, 2014, pp. 29–38. doi: 10.1145/2658761.2658766.
• Lukas Linsbauer, Alexander Egyed, and Roberto Erick Lopez-Herrejon. “A Variability Aware Configuration Management and Revision Control Platform”. In: Proc. Int’l Conf. on Software Engineering (ICSE). ACM, 2016, pp. 803–806. doi: 10.1145/2889160.2889262.
• Lukas Linsbauer, Felix Schwägerl, Thorsten Berger, and Paul Grünbacher. “Concepts of Variation Control Systems”. In: J. Systems and Software (JSS) 171 (2021), p. 110796. doi: 10.1016/j.jss.2020.110796.

Context

Changes to software product lines introduce additional complexity compared to ordinary single-systems software development. Apart from source code itself developers might also change feature-to-code mappings or the constraints among features.

Goal / Research Questions

• Identify how a software product line might be edited.
• Identify the challenges that occur when variability information as well as source code co-evolve.
• Which techniques can help developers or tools to understand changes?

Paper

• Paulo Borba, Leopoldo Teixeira, and Rohit Gheyi. “A Theory of Software Product Line Refinement”. In: Theoretical Computer Science 455.0 (2012), pp. 2–30. doi: 10.1016/j.tcs.2012.01.031.
• Paul Maximilian Bittner, Christof Tinnes, Alexander Schultheiß, Sören Viegener, Timo Kehrer, and Thomas Thüm. “Classifying Edits to Variability in Source Code”. In: Proc. Europ. Software Engineering Conf./Foundations of Software Engineering (ESEC/FSE). ACM, 2022, pp. 196–208. doi: 10.1145/3540250.3549108.
• Johannes Bürdek, Timo Kehrer, Malte Lochau, Dennis Reuling, Udo Kelter, and Andy Schürr. “Reasoning About Product-Line Evolution Using Complex Feature Model Differences”. In: Automated Software Engineering 23.4 (2015), pp. 687–733. doi: 10.1007/s10515-015-0185-3.

Context

To design and specify analyses, formal languages are needed. For analysing source code in software product lines, one such a language is the choice calculus, a minimal language to describe variation. The choice calculus has been used as a foundation for specifying numerous analyses in research, and as a basis for their implementation.

Goal / Research Questions

• Describe what the choice calculus is and how it can be used to specify and implement variational analyses.
• Give an overview of existing use cases and analyses that were formalized with the choice calculus.
• Identify commonalities and differences in different usages and dialects of the choice calculus. Why and how is it adapted to its intended use case?

Papers

• Martin Erwig and Eric Walkingshaw. “The Choice Calculus: A Representation for Software Variation”. In: Trans. on Software Engineering and Methodology (TOSEM) 21.1 (2011), 6:1–6:27. doi: 10.1145/2063239.2063245.
• Sheng Chen, Martin Erwig, and Eric Walkingshaw. “A Calculus for Variational Programming”. In: Proc. Europ. Conf. on Object-Oriented Programming (ECOOP). Vol. 56. Schloss Dagstuhl, 2016, 6:1–6:28. doi: 10.4230/LIPICS.ECOOP.2016.6.
• Jeffrey M. Young, Paul Maximilian Bittner, Eric Walkingshaw, and Thomas Thüm. “Variational Satisfiability Solving: Efficiently Solving Lots of Related SAT Problems”. In: Empirical Software Engineering (EMSE) 28 (2022), p. 53. doi: 10.1007/s10664-022-10217-3.

Context

Software product lines (SPLs) describe billions of individual software variants simultaneously. Analyzing all these variants, for example to ensure soundness properties, does not scale in practice. To this end, analysis must exploit the similarities between the variants (i.e., the features), similar to how an SPL implements billions of variants without ever explicitly enumerating them.

Goal / Research Questions

• Identify common analyses strategies for software product lines and their uses.
• Identify how the exponential explosion of the configuration space is addressed by these strategies.
• Compare the different kinds of analyses strategies with respect to their advantages and disadvantages.

Papers

• Thiago Castro, Leopoldo Teixeira, Vander Alves, Sven Apel, Maxime Cordy, and Rohit Gheyi. “A Formal Framework of Software Product Line Analyses”. In: Trans. on Software Engineering and Methodology (TOSEM) 30.3 (2021). doi: 10.1145/3442389.
• Thomas Thüm, Sven Apel, Christian Kästner, Ina Schaefer, and Gunter Saake. “A Classification and Survey of Analysis Strategies for Software Product Lines”. In: ACM Computing Surveys (CSUR) 47.1 (2014), 6:1–6:45. doi: 10.1145/2580950.
• Sergiy Kolesnikov, Alexander von Rhein, Claus Hunsen, and Sven Apel. “A Comparison of Product-Based, Feature-Based, and Family-Based Type Checking”. In: Proc. Int’l Conf. on Generative Programming: Concepts & Experiences (GPCE). ACM, 2013, pp. 115–124. doi: 10.1145/2517208.2517213.

Context

For comprehending, maintaining, and extending existing software, it is crucial to find locations of interest in a software system quickly, reliably, and exhaustively. To that end, tracing features to their implementation is one of the most common activities of developers. However, such feature traces are rarely documented in practice. Therefore, research on feature location an feature identification tries to assist developers in documenting or recovering such information.

Goal / Research Questions

• Describe the concept of feature location.
• What sources of knowledge are exploited by existing methods? How is the knowledge used?
• Identify which feature location techniques are useful for in different project types or development scenarios.

Papers

• Christian Kästner, Alexander Dreiling, and Klaus Ostermann. “Variability Mining: Consistent Semiautomatic Detection of Product-Line Features”. In: IEEE Trans. on Software Engineering (TSE) 40.1 (2014), pp. 67–82. doi: 10.1109/TSE.2013.45.
• Lukas Linsbauer, Roberto Erick Lopez-Herrejon, and Alexander Egyed. “Variability Extraction and Modeling for Product Variants”. In: Software and Systems Modeling (SoSyM) 16.4 (2017), pp. 1179–1199. doi: 10.1007/s10270-015-0512-y.
• Julia Rubin and Marsha Chechik. “A Survey of Feature Location Techniques”. In: Domain Engineering: Product Lines, Languages, and Conceptual Models. Ed. by Iris Reinhartz-Berger, Arnon Sturm, Tony Clark, Sholom Cohen, and Jorn Bettin. Springer, 2013, pp. 29–58. doi: 10.1007/978-3-642-36654-3_2.

Context

On GitHub, developers often maintain a separate, modified variant (fork) alongside the original. Shared components between variants may not be consistently updated. This can lead to inefficient patching across variants.

Goal / Research Questions

• Identify and describe why and how clone-and-own is applied in open source projects on GitHub.
• Identify and describe challenges of software reuse between open source repos.
• Identify and describe tools and techniques that might aid developers in maintaining their variant.

Papers

• John Businge, Moses Openja, Sarah Nadi, Engineer Bainomugisha, and Thorsten Berger. “Clone-Based Variability Management in the Android Ecosystem”. In: Proc. Int’l Conf. on Software Maintenance and Evolution (ICSME). IEEE, 2018, pp. 625–634. doi: 10.1109/ICSME.2018.00072.
• John Businge, Ahmed Zerouali, Alexandre Decan, Tom Mens, Serge Demeyer, and Coen De Roover. “Variant Forks - Motivations and Impediments”. In: Proc. Int’l Conf. on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2022, pp. 867–877. doi: 10.1109/SANER53432.2022.00105.
• Poedjadevie Kadjel Ramkisoen, John Businge, Brent van Bladel, Alexandre Decan, Serge Demeyer, Coen De Roover, and Foutse Khomh. “PaReco: Patched Clones and Missed Patches Among the Divergent Variants of a Software Family”. In: Proc. Europ. Software Engineering Conf./Foundations of Software Engineering (ESEC/FSE). ACM, 2022, pp. 646–658. doi: 10.1145/3540250.3549112.
• Shurui Zhou, Ştefan Stănciulescu, Olaf Leßenich, Yingfei Xiong, Andrzej Wąsowski, and Christian Kästner. “Identifying Features in Forks”. In: Proc. Int’l Conf. on Software Engineering (ICSE). ACM, 2018, pp. 105–116. doi: 10.1145/3180155.3180205.

Context

In software product line theory, feature models are a core component of a product line. In practice however, developers often do not model the variability in a system explicitly. The knowledge about features and their relationships is often implicitly encoded in configurations files, the build system, and/or solution space variability. Various techniques and tools try to extract this knowledge, which is a system-dependent and complex process.

Goal / Research Questions

• Assess the core ideas behind techniques for extracting knowledge about features and their relationships from a system.
• Identify and present technical and organizational challenges of extracting knowledge for feature modelling.

Papers

• Jessie Carbonnel, Marianne Huchard, and Clémentine Nebut. “Towards the Extraction of Variability Information to Assist Variability Modelling of Complex Product Lines”. In: Proc. Int’l Workshop on Variability Modelling of Software-Intensive Systems (VaMoS). ACM, 2018, pp. 113–120. doi: 10.1145/3168365.3168378.
• Alexander Knüppel, Thomas Thüm, Stephan Mennicke, Jens Meinicke, and Ina Schaefer. “Is There a Mismatch Between Real-World Feature Models and Product-Line Research?” In: Proc. Europ. Software Engineering Conf./Foundations of Software Engineering (ESEC/FSE). ACM, 2017, pp. 291–302. doi: 10.1145/3106237.3106252.
• Johann Mortara and Philippe Collet. “Capturing the Diversity of Analyses on the Linux Kernel Variability”. In: Proc. Int’l Systems and Software Product Line Conf. (SPLC). ACM, 2021, pp. 160–171. doi: 10.1145/3461001.3471151.
• Anjali Sree-Kumar, Elena Planas, and Robert Clarisó. “Extracting Software Product Line Feature Models From Natural Language Specifications”. In: Proc. Int’l Systems and Software Product Line Conf. (SPLC). ACM, 2018, pp. 43–53. doi: 10.1145/3233027.3233029.

Context

Software product lines let users tailor a product to their individual needs. An essential tool for efficient and successful configuration is a configurator that guides a user to the their desired product. In the web, there exist many configurator tools using different strategies for presenting decision to the user and handling dependencies between configuration options.

Goal / Research Questions

• Identify different strategies for configuring products using web configurators.
• Identify commonalities and differences of configurator tools.
• Reason about shortcomings and limitations of currently used web configurators.

Papers

• Ebrahim Khalil Abbasi, Arnaud Hubaux, Mathieu Acher, Quentin Boucher, and Patrick Heymans. “The Anatomy of a Sales Configurator: An Empirical Study of 111 Cases”. In: Proc. Int’l Conf. on Advanced Information Systems Engineering (CAiSE). Springer, 2013, pp. 162–177. doi: 10.1007/978-3-642-38709-8_11.
• Tony Leclercq, Ebrahim Khalil Abbasi, Bruno Dumas, Marie-Ange Remiche, and Patrick Heymans. “Essential Expectations of Users of Web Configurators: An Empirical Survey”. In: Proceedings of the ACM on Human Computer Interaction (PACMHCI) 6.EICS (2022). doi: 10.1145/3534519.
• Thomas Thüm, Sebastian Krieter, and Ina Schaefer. “Product Configuration in the Wild: Strategies for Conflicting Decisions in Web Configurators”. In: Proc. Configuration Workshop (ConfWS). RWTH Aachen University, 2018, pp. 1–8. Preprint on Github.

Context

Software product lines can be implemented using a wide variety of different variability mechanism, such as preprocessors, plug-in frameworks, or feature-oriented programming. All these techniques come with advantages and disadvantages, which make them feasible or not feasible for use a given use case. Thus, in practice often many factors have to be considered when developing a software product line and deciding on a variability mechanism.

Goal / Research Questions

• Identify which variability mechanism are used in practice.
• Identify and describe advantages and disadvantages of the different variability mechanisms.
• Discuss reasons that are used to justify the implementation of a particular variability mechanism.

Papers

• Thorsten Berger, Jan-Philipp Steghöfer, Tewfik Ziadi, Jacques Robin, and Jabier Martinez. “The State of Adoption and the Challenges of Systematic Variability Management in Industry”. In: Empirical Software Engineering (EMSE) 25.3 (2020), pp. 1755–1797. doi: 10.1007/S10664-019-09787-6.
• Sascha El-Sharkawy, Nozomi Yamagishi-Eichler, and Klaus Schmid. “Metrics for Analyzing Variability and its Implementation in Software Product Lines: A Systematic Literature Review”. In: J. Information and Software Technology (IST) 106 (2019), pp. 1–30. doi: 10.1016/j.infsof.2018.08.015.
• Jabier Martinez, Xhevahire Tërnava, and Tewfik Ziadi. “Software Product Line Extraction From Variability-Rich Systems: The Robocode Case Study”. In: Proc. Int’l Systems and Software Product Line Conf. (SPLC). ACM, 2018, pp. 132–142. doi: 10.1145/3233027.3233038.

Context

In software product lines, feature models specify the set of available features as well as their constraints, such as dependencies between features. Feature models can be analyzed in many different ways such as detecting defects or computing insightful metrics for developers. Many analyses can be reduced to satisfiability solving proplems, which allows the employment of dedicated solvers.

Goal / Research Questions

• List common satisfiability-based analyses for feature models and their uses.
• Identify what types of solvers can be used to implement each analysis.
• Identify and describe circumstances that make each solver preferable to others.

Papers

• David Benavides, Sergio Segura, and Antonio Ruiz-Cortés. “Automated Analysis of Feature Models 20 Years Later: A Literature Review”. In: Information Systems 35.6 (2010), pp. 615–708. doi: 10.1016/J.IS.2010.01.001.
• Elias Kuiter, Sebastian Krieter, Chico Sundermann, Thomas Thüm, and Gunter Saake. “Tseitin or not Tseitin? The Impact of CNF Transformations on Feature-Model Analyses”. In: Proc. Int’l Conf. on Automated Software Engineering (ASE). ACM, 2022, 110:1–110:13. doi: 10.1145/3551349.3556938.
• Chico Sundermann, Elias Kuiter, Tobias Heß, Heiko Raab, Sebastian Krieter, and Thomas Thüm. “On the Benefits of Knowledge Compilation for Feature-Model Analyses”. In: Annals of Mathematics and Artificial Intelligence (AMAI) 92.5 (2023), pp. 1013–1050. doi: 10.1007/s10472-023-09906-6.

Context

Software product lines typically describe billions of software variants simultaneously. Software tests can be run only on a variant (i.e., for a fixed configuration of features). Running tests for all variants is infeasible in practice. Developers are thus faced with the challenge of identifying a representative sample of variants to test that gives the best guarantees for the behavior of the other, untested variants.

Goal / Research Questions

• Identify common strategies to compute samples.
• Identify core challenges of sample computation.
• Explain trade-offs, advantages, and disadvantages of the different sampling strategies.

Papers

• Sabrina Böhm, Tim Schmidt, Sebastian Krieter, Tobias Pett, Thomas Thüm, and Malte Lochau. “Coverage Metrics for T-Wise Feature Interactions”. In: Proc. Int’l Conf. on Software Testing, Verification and Validation (ICST). To appear. IEEE, 2025. Preprint on Github.
• Axel Halin, Alexandre Nuttinck, Mathieu Acher, Xavier Devroey, Gilles Perrouin, and Benoit Baudry. “Test Them All, Is It Worth It? Assessing Configuration Sampling on the JHipster Web Development Stack”. In: Empirical Software Engineering (EMSE) 24.2 (2019), pp. 674–717. doi: 10.1007/S10664-018-9635-4.
• Mahsa Varshosaz, Mustafa Al-Hajjaji, Thomas Thüm, Tobias Runge, Mohammad Reza Mousavi, and Ina Schaefer. “A Classification of Product Sampling for Software Product Lines”. In: Proc. Int’l Systems and Software Product Line Conf. (SPLC). ACM, 2018, pp. 1–13. doi: 10.1145/3233027.3233035.

Context

In the literature, many complex feature-model constructs have been suggested that cannot be easily translated to boolean logic. Still, most work focuses on purely boolean reasoning limiting the applicability to the complex constructs. Hence, researchers and practitioners often have a hard time identifying suitable solutions for the constructs they may want to use.

Goal / Research Questions

• Identify feature-modelling constructs that cannot be easily represented with boolean logic.
• Identify reasoning proposals that may cope with those constructs.
• Derive a mapping which proposals could be solved with which reasoning approaches.
• Discuss the gap between constructs and available solutions.

Papers

• David Benavides, Pablo Trinidad, and Antonio Ruiz-Cortés. “Using Constraint Programming to Reason on Feature Models”. In: Proc. Int’l Conf. on Software Engineering and Knowledge Engineering (SEKE). 2005, pp. 677–682. Preprint on Academia.edu.
• Paul Maximilian Bittner, Thomas Thüm, and Ina Schaefer. “SAT Encodings of the At-Most-k Constraint – A Case Study on Configuring University Courses”. In: Proc. Int’l Conf. on Software Engineering and Formal Methods (SEFM). Ed. by Peter Csaba Ölveczky and Gwen Salaün. Springer, 2019, pp. 127–144. doi: 10.1007/978-3-030-30446-1_7.
• Krzysztof Czarnecki and Chang Hwan Peter Kim. “Cardinality-Based Feature Modeling and Constraints: A Progress Report”. In: Proc. Int’l Workshop on Software Factories (SF). 2005, pp. 16–20. Preprint on CiteseerX.
• Daniel-Jesus Munoz, Mónica Pinto, Lidia Fuentes, and Don Batory. “Transforming Numerical Feature Models into Propositional Formulas and the Universal Variability Language”. In: J. Systems and Software (JSS) 204 (2023). doi: 10.1016/j.jss.2023.111770.