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Software Engineering Meets Systems Engineering: Conceptual Modeling Applied to Engineering Operations
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In: ISSN: 1738-7906 ; International Journal of Computer Science and Network Security ; https://hal.archives-ouvertes.fr/hal-03404932 ; International Journal of Computer Science and Network Security, International Journal of Computer Science and Network Security, 2021, ⟨10.22937/IJCSNS.2021.21.10.47⟩ (2021)
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Automating user-feedback driven requirements prioritization
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In: ISSN: 0950-5849 ; EISSN: 1873-6025 ; Information and Software Technology ; https://hal.archives-ouvertes.fr/hal-03277970 ; Information and Software Technology, Elsevier, 2021, 138, pp.106635:1-106635:16. ⟨10.1016/j.infsof.2021.106635⟩ (2021)
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How to quote Ethiopian authors in linguistic publications
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In: ISSN: 0002-0427 ; EISSN: 2749-0971 ; Afrika und Übersee ; https://hal.archives-ouvertes.fr/hal-02997055 ; Afrika und Übersee, Universität Hamburg, 2021, 94 (1), pp.80-90. ⟨10.15460/auue.2021.94.1.222⟩ ; https://journals.sub.uni-hamburg.de/hup1/afrikaunduebersee/ (2021)
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| 64 |
How to quote Ethiopian authors in linguistic publications
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In: Afrika und Übersee ; https://hal.archives-ouvertes.fr/hal-02997055 ; Afrika und Übersee, Universität Hamburg, In press (2021)
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| 65 |
Automatic Guide Generation for Stan via NumPyro
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In: PROBPROG 2021 - Third International Conference on Probabilistic Programming ; https://hal.archives-ouvertes.fr/hal-03401708 ; PROBPROG 2021 - Third International Conference on Probabilistic Programming, Oct 2021, Virtual, United States (2021)
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| 66 |
3D Serious Game Modeling and Design: Contributions to Language Learning ; Modélisation et Conception de jeu sérieux tridimensionnel : Contributions à l’apprentissage des langues
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In: https://hal.archives-ouvertes.fr/tel-03315793 ; Environnements Informatiques pour l'Apprentissage Humain. Université Ibn Tofail, Kénitra (Maroc), 2021. Français (2021)
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User-friendly automatic transcription of low-resource languages: Plugging ESPnet into Elpis
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In: ComputEL-4: Fourth Workshop on the Use of Computational Methods in the Study of Endangered Languages ; https://halshs.archives-ouvertes.fr/halshs-03030529 ; ComputEL-4: Fourth Workshop on the Use of Computational Methods in the Study of Endangered Languages, Mar 2021, Hawai‘i, United States (2021)
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| 68 |
Transdisciplinary Analysis of a Corpus of French Newsreels: The ANTRACT Project
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In: ISSN: 1938-4122 ; Digital Humanities Quarterly ; https://hal.archives-ouvertes.fr/hal-03166755 ; Digital Humanities Quarterly, Alliance of Digital Humanities, 2021, Special Issue on AudioVisual Data in DH, 15 (1) ; http://digitalhumanities.org/dhq/ (2021)
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| 69 |
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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| 70 |
Supply of engineering techniques and software design patterns in psychoanalysis and psychometrics sciences ...
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| 72 |
CorpusExplorer ... : Eine Software zur korpuspragmatischen Analyse ...
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| 73 |
Learning Activities in Colours and Rainbows for Programming Skill Development ...
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| 74 |
Neutralization of social risks of ICT implementation in object–oriented software environments C++ and Java ...
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| 75 |
Neutralization of social risks of ICT implementation in object–oriented software environments C++ and Java ...
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| 76 |
Software Engineering Meets Systems Engineering: Conceptual Modeling Applied to Engineering Operations ...
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| 77 |
A Logical Programming Language as an Instrument for Specifying and Verifying Dynamic Memory ...
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| 79 |
Exploiting the Unique Expression for Improved Sentiment Analysis in Software Engineering Text ...
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| 80 |
Replication Package - Effect of requirements specification using native language on externalsoftware quality ...
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