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SEALS: A framework for building Self-Adaptive Virtual Machines
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In: SLE 2021 - 14th ACM SIGPLAN International Conference on Software Language Engineering ; https://hal.inria.fr/hal-03355253 ; SLE 2021 - 14th ACM SIGPLAN International Conference on Software Language Engineering, Oct 2021, Chicago, United States. pp.1-14, ⟨10.1145/3486608.3486912⟩ (2021)
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Verification of Program Transformations with Inductive Refinement Types
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In: ISSN: 1049-331X ; ACM Transactions on Software Engineering and Methodology ; https://hal.inria.fr/hal-03518825 ; ACM Transactions on Software Engineering and Methodology, Association for Computing Machinery, 2021, 30 (1), pp.1-33. ⟨10.1145/3409805⟩ (2021)
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Modelling, Reverse Engineering, and Learning Software Variability
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In: https://hal.inria.fr/tel-03521806 ; Software Engineering [cs.SE]. Université de Rennes 1, 2021 (2021)
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Abstract:
The society expects software to deliver the right functionality, in a short amount of time and with fewer resources, in every possible circumstance whatever are the hardware, the operating systems, the compilers, or the data fed as input. For fitting such a diversity of needs, it is common that software comes in many variants and is highly configurable through configuration options, runtime parameters, conditional compilation directives, menu preferences, configuration files, plugins, etc. As there is no one-size-fits-all solution, software variability ("the ability of a software system or artifact to be efficiently extended, changed, customized or configured for use in a particular context") has been studied the last two decades and is a discipline of its own. Though highly desirable, software variability also introduces an enormous complexity due to the combinatorial explosion of possible variants. For example, the Linux kernel has 15000+ options and most of them can have 3 values: "yes", "no", or "module". Variability is challenging for maintaining, verifying, and configuring software systems (Web applications, Web browsers, video tools, etc.). It is also a source of opportunities to better understand a domain, create reusable artefacts, deploy performance-wise optimal systems, or find specialized solutions to many kinds of problems. In many scenarios, a model of variability is either beneficial or mandatory to explore, observe, and reason about the space of possible variants. For instance, without a variability model, it is impossible to establish a sampling strategy that would satisfy the constraints among options and meet coverage or testing criteria. I address a central question in this HDR manuscript: How to model software variability? I detail several contributions related to modelling, reverse engineering, and learning software variability. I first contribute to support the persons in charge of manually specifying feature models, the de facto standard for modeling variability. I develop an algebra together with a language for supporting the composition, decomposition, diff, refactoring, and reasoning of feature models. I further establish the syntactic and semantic relationships between feature models and product comparison matrices, a large class of tabular data. I then empirically investigate how these feature models can be used to test in the large configurable systems with different sampling strategies. Along this effort, I report on the attempts and lessons learned when defining the "right" variability language. From a reverse engineering perspective, I contribute to synthesize variability information into models and from various kinds of artefacts. I develop foundations and methods for reverse engineering feature models from satisfiability formulae, product comparison matrices, dependencies files and architectural information, and from Web configurators. I also report on the degree of automation and show that the involvement of developers and domain experts is beneficial to obtain high-quality models. Thirdly, I contribute to learning constraints and non-functional properties (performance) of a variability-intensive system. I describe a systematic process "sampling, measuring, learning" that aims to enforce or augment a variability model, capturing variability knowledge that domain experts can hardly express. I show that supervised, statistical machine learning can be used to synthesize rules or build prediction models in an accurate and interpretable way. This process can even be applied to huge configuration space, such as the Linux kernel one. Despite a wide applicability and observed benefits, I show that each individual line of contributions has limitations. I defend the following answer: a supervised, iterative process (1) based on the combination of reverse engineering, modelling, and learning techniques; (2) capable of integrating multiple variability information (eg expert knowledge, legacy artefacts, dynamic observations). Finally, this work opens different perspectives related to so-called deep software variability, security, smart build of configurations, and (threats to) science.
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Keyword:
[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]; [INFO]Computer Science [cs]; logiciel variabilité configuration; software product lines variability configuration learning modelling reverse engineering
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URL: https://hal.inria.fr/tel-03521806/file/HDRAcherVariability.pdf
https://hal.inria.fr/tel-03521806/document
https://hal.inria.fr/tel-03521806
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A principled approach to REPL interpreters
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In: SPLASH 2020 - ACM SIGPLAN conference on Systems, Programming, Languages, and Applications: Software for Humanity ; https://hal.inria.fr/hal-02968938 ; SPLASH 2020 - ACM SIGPLAN conference on Systems, Programming, Languages, and Applications: Software for Humanity, Nov 2020, Chicago / Virtual, United States. pp.1-17, ⟨10.1145/3426428.3426917⟩ (2020)
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A Language Agnostic Approach to Modeling Requirements: Specification and Verification
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In: MODELS ’20 Companion ; https://hal.inria.fr/hal-02924645 ; MODELS ’20 Companion, Oct 2020, Virtual Event, Canada. ⟨10.1145/3417990.3419224⟩ (2020)
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Generation of Inductive Types from Ecore Metamodels
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In: Model-Driven Engineering and Software Development. MODELSWARD 2018. ; https://hal.archives-ouvertes.fr/hal-02021361 ; Model-Driven Engineering and Software Development. MODELSWARD 2018., pp.308-334, 2019 (2019)
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Effective Bridging Between Ecore and Coq: Case of a Type-Checker with Proof-Carrying Code
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In: Modelling and Implementation of Complex Systems ; https://hal.archives-ouvertes.fr/hal-01945245 ; Modelling and Implementation of Complex Systems, pp.259-273, 2019 (2019)
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On modularity and performance of External Domain-Specific Language implementations ; Modularité et performance des implémentations de langages dédiés externes
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In: https://hal.inria.fr/tel-02418676 ; Software Engineering [cs.SE]. Université de rennes 1, 2019. English (2019)
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On modularity and performances of external domain-specific language implementations ; Modularité et performances des langages dédiés externes
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In: https://tel.archives-ouvertes.fr/tel-02972666 ; Software Engineering [cs.SE]. Université Rennes 1, 2019. English. ⟨NNT : 2019REN1S112⟩ (2019)
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Dynamic program analysis for suggesting test improvements to developers ; Analyse dynamique du programme pour suggérer des améliorations de test aux développeurs
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In: https://hal.archives-ouvertes.fr/tel-02459572 ; Software Engineering [cs.SE]. Université de Rennes 1 [UR1], 2019. English (2019)
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Concern-Oriented Language Development (COLD): Fostering Reuse in Language Engineering
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In: ISSN: 1477-8424 ; Computer Languages, Systems and Structures ; https://hal.archives-ouvertes.fr/hal-01803008 ; Computer Languages, Systems and Structures, Elsevier, 2018, 54, pp.139-155. ⟨10.1016/j.cl.2018.05.004⟩ (2018)
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| 12 |
Specification-Based Protocol Obfuscation
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In: DSN 2018 - 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks ; https://hal.inria.fr/hal-01848573 ; DSN 2018 - 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, Jun 2018, Luxembourg City, Luxembourg. pp.1-12, ⟨10.1109/DSN.2018.00056⟩ (2018)
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| 13 |
Verification of High-Level Transformations with Inductive Refinement Types
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In: GPCE 2018 - 17th International Conference on Generative Programming: Concepts & Experience ; https://hal.inria.fr/hal-01898058 ; GPCE 2018 - 17th International Conference on Generative Programming: Concepts & Experience, Nov 2018, Boston, United States. pp.147-160, ⟨10.1145/3278122.3278125⟩ (2018)
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| 14 |
Distributing Relational Model Transformation on MapReduce
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In: ISSN: 0164-1212 ; Journal of Systems and Software ; https://hal.archives-ouvertes.fr/hal-01863885 ; Journal of Systems and Software, Elsevier, 2018, 142, pp.1-20. ⟨10.1016/j.jss.2018.04.014⟩ (2018)
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| 15 |
Shape-Diverse DSLs: Languages without Borders (Vision Paper)
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In: SLE 2018 Proceedings of the 11th ACM SIGPLAN International Conference on Software Language Engineering ; SLE 2018 - 11th ACM SGIPLAN International Conference on Software Language Engineering ; https://hal.archives-ouvertes.fr/hal-01889155 ; SLE 2018 - 11th ACM SGIPLAN International Conference on Software Language Engineering, Nov 2018, Boston, United States. pp.215-219, ⟨10.1145/3276604.3276623⟩ (2018)
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| 16 |
Modular Language Composition for the Masses
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In: SLE 2018 - 11th ACM SIGPLAN International Conference on Software Language Engineering ; https://hal.inria.fr/hal-01890446 ; SLE 2018 - 11th ACM SIGPLAN International Conference on Software Language Engineering, Nov 2018, Boston, United States. pp.1-12, ⟨10.1145/3276604.3276622⟩ ; http://www.sleconf.org/2018/ (2018)
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| 17 |
Revisiting Visitors for Modular Extension of Executable DSMLs
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In: 2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems ; https://hal.inria.fr/hal-01568169 ; 2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems, Sep 2017, Austin, United States. ⟨10.1109/MODELS.2017.23⟩ (2017)
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| 18 |
On Language Interfaces
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In: PAUSE: Present And Ulterior Software Engineering ; https://hal.inria.fr/hal-01424909 ; Bertrand Meyer; Manuel Mazzara. PAUSE: Present And Ulterior Software Engineering, Springer, 2017 (2017)
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| 19 |
Safe Model Polymorphism for Flexible Modeling
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In: ISSN: 1477-8424 ; Computer Languages, Systems and Structures ; https://hal.inria.fr/hal-01367305 ; Computer Languages, Systems and Structures, Elsevier, 2016, Computer Languages, Systems Structures, 49, pp.30. ⟨10.1016/j.cl.2016.09.001⟩ ; http://www.sciencedirect.com/science/journal/14778424 (2016)
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| 20 |
Using free modeling as an Agile method for developing domain specific modeling languages
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In: Proceedings MODELS 2016 : ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems ; MODELS 2016 : ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems ; https://hal.archives-ouvertes.fr/hal-01393781 ; MODELS 2016 : ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems, Oct 2016, Saint Malo, France. pp.24 - 34, ⟨10.1145/2976767.2976807⟩ (2016)
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