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10 results on '"Hugo, Martin"'

1. A comparison of performance specialization learning for configurable systems

Author

Mathieu Acher, Hugo Martin, Juliana Alves Pereira, Jean-Marc Jézéquel, Diversity-centric Software Engineering (DiverSe), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-LANGAGE ET GÉNIE LOGICIEL (IRISA-D4), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Pontifical Catholic University of Rio de Janeiro (PUC), ANR-17-CE25-0010,VaryVary,Faire Varier la Variabilité(2017), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1)

Subjects
business.industry, Computer science, Decision tree, 020207 software engineering, Feature selection, Linux kernel, Sample (statistics), 02 engineering and technology, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], Machine learning, computer.software_genre, Automation, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Software deployment, Specialization (functional), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Artificial intelligence, Software system, business, computer
Abstract

International audience; The specialization of the configuration space of a software system has been considered for targeting specific configuration profiles, usages, deployment scenarios, or hardware settings. The challenge is to find constraints among options' values that only retain configurations meeting a performance objective. Since the exponential nature of configurable systems makes a manual specialization unpractical, several approaches have considered its automation using machine learning, i.e., measuring a sample of configurations and then learning what options' values should be constrained. Even focusing on learning techniques based on decision trees for their built-in explainability, there is still a wide range of possible approaches that need to be evaluated, i.e., how accurate is the specialization with regards to sampling size, performance thresholds, and kinds of configurable systems. In this paper, we compare six learning techniques: three variants of decision trees (including a novel algorithm) with and without the use of model-based feature selection. We first perform a study on 8 configurable systems considered in previous related works and show that the accuracy reaches more than 90% and that feature selection can improve the results in the majority of cases. We then perform a study on the Linux kernel and show that these techniques performs as well as on the other systems. Overall, our results show that there is no one-size-fits-all learning variant (though high accuracy can be achieved): we present guidelines and discuss tradeoffs. CCS CONCEPTS • Software and its engineering → Software product lines; • Computing methodologies → Classification and regression trees.

Published
2021
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2. Learning Software Configuration Spaces: A Systematic Literature Review

Author

Anthony Ventresque, Jean-Marc Jézéquel, Goetz Botterweck, Juliana Alves Pereira, Hugo Martin, Mathieu Acher, Diversity-centric Software Engineering (DiverSe), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-LANGAGE ET GÉNIE LOGICIEL (IRISA-D4), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Pontifical Catholic University of Rio de Janeiro, The Irish Software Engineering Research Centre (LERO), University College Dublin [Dublin] (UCD), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and ANR-17-CE25-0010,VaryVary,Faire Varier la Variabilité(2017)

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Process (engineering), Computer science, Cloud computing, 02 engineering and technology, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], computer.software_genre, Software Product Lines, Machine Learning (cs.LG), Machine Learning, Computer Science - Software Engineering, Software, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 0202 electrical engineering, electronic engineering, information engineering, Plug-in, Software system, Android (operating system), Software configuration management, Systematic Literature Review, business.industry, Configurable Systems, 020207 software engineering, Software Engineering (cs.SE), Hardware and Architecture, 020201 artificial intelligence & image processing, Software engineering, business, computer, Combinatorial explosion, Information Systems
Abstract

International audience; Most modern software systems (operating systems like Linux or Android, Web browsers like Firefox or Chrome, video encoders like ffmpeg, x264 or VLC, mobile and cloud applications, etc.) are highly configurable. Hundreds of configuration options, features, or plugins can be combined, each potentially with distinct functionality and effects on execution time, security, energy consumption, etc. Due to the combinatorial explosion and the cost of executing software, it is quickly impossible to exhaustively explore the whole configuration space. Hence, numerous works have investigated the idea of learning it from a small sample of configurations' measurements. The pattern ``sampling, measuring, learning" has emerged in the literature, with several practical interests for both software developers and end-users of configurable systems. In this systematic literature review, we report on the different application objectives (e.g., performance prediction, configuration optimization, constraint mining), use-cases, targeted software systems, and application domains. We review the various strategies employed to gather a representative and cost-effective sample. We describe automated software techniques used to measure functional and non-functional properties of configurations. We classify machine learning algorithms and how they relate to the pursued application. Finally, we also describe how researchers evaluate the quality of the learning process. The findings from this systematic review show that the potential application objective is important; there are a vast number of case studies reported in the literature related to particular domains or software systems. Yet, the huge variant space of configurable systems is still challenging and calls to further investigate the synergies between artificial intelligence and software engineering.

Published
2021
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3. Min Cost Improvement and Max Gain Stability in Multicriteria Sorting Methods on Combinatorial Domains

Author

Nawal Benabbou, Hugo Martin, Patrice Perny, DECISION, LIP6, and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mathematical optimization, 021103 operations research, Sorting algorithm, Computer science, Strategy and Management, Max Gain Stability, 0211 other engineering and technologies, Stability (learning theory), General Decision Sciences, 02 engineering and technology, Cost improvement, Multi-Objective Combinatorial Optimization, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Min Cost Improvement, Multicriteria Sorting Method
Abstract

International audience; Various multicriteria sorting methods have been proposed in the literature to assign the feasible alternatives into predefined categories. We consider here problems involving a set of totally ordered categories representing different achievement levels in the satisfaction of criteria. As in many existing methods, the assignment rule of an alternative to a category is based on the comparison of its performance vector to reference profiles defining lower bounds of the categories. Within this standard setting we address a new problem that consists in finding how to modify a given solution, within a combinatorial set of alternatives, to upgrade it in the upper category (or higher) at minimum cost. We also consider the problem of identifying the sequence of solutions that minimize the total cost while satisfying some budget constraint at every step, and the problem of determining how to modify the current solution to save money while staying in the same category. We first propose a general approach based on mixed integer (linear or quadratic) programming to solve these problems. Then, we implement this approach on various multiobjective combinatorial problems, such as multi-agent assignment problems and multiobjective knapsack problems. Numerical tests are provided to establish the feasibility of the approach on instances of different sizes.

Published
2021
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4. Incremental Preference Elicitation with Bipolar Choquet Integrals

Author

Patrice Perny and Hugo Martin

Subjects
Mathematical optimization, Preference learning, Choquet integral, Computer science, Reference values, Multicriteria analysis, Preference elicitation, Weighted arithmetic mean, Preference, Field (computer science)
Abstract

Preference modeling and preference learning are crucial issues in multicriteria decision-making to formulate recommendations that are tailored to the Decision Maker. In the field of multicriteria analysis, various aggregation functions have been studied to scalarize performance vectors and compare solutions. Nonetheless, most of these models do not take into account the presence of reference points in the criteria scales. Since it has been observed that decision makers may exhibit different attitudes towards aggregation depending on whether evaluations are above or below reference values, we consider here bipolar extensions of well-known aggregation models and propose incremental preference elicitation methods based on these models. In particular, we consider the elicitation of a 2-additive bipolar Choquet Integral, of a bipolar Weighted Ordered Weighted Average (WOWA), and of a non-weighted bipolar OWA. We propose a general approach that is implemented in all these cases and provide numerical tests showing its practical efficiency.

Published
2021
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5. Transfer Learning Across Variants and Versions: The Case of Linux Kernel Size

Author

Juliana Alves Pereira, Djamel Eddine Khelladi, Mathieu Acher, Luc Lesoil, Jean-Marc Jézéquel, Hugo Martin, Diversity-centric Software Engineering (DiverSe), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-LANGAGE ET GÉNIE LOGICIEL (IRISA-D4), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Pontifical Catholic University of Rio de Janeiro (PUC), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and ANR-17-CE25-0010,VaryVary,Faire Varier la Variabilité(2017)

Subjects
Structure (mathematical logic), Computer science, Binary number, 020207 software engineering, Linux kernel, Index Terms-Software Product Line, 02 engineering and technology, Transfer Learning, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], Reuse, Machine Learning, Computer engineering, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Software Evolution, 0202 electrical engineering, electronic engineering, information engineering, Performance prediction, Performance Prediction, [INFO]Computer Science [cs], State (computer science), Transfer of learning, Software, Software evolution
Abstract

International audience; With large scale and complex configurable systems, it is hard for users to choose the right combination of options (i.e., configurations) in order to obtain the wanted trade-off between functionality and performance goals such as speed or size. Machine learning can help in relating these goals to the configurable system options, and thus, predict the effect of options on the outcome, typically after a costly training step. However, many configurable systems evolve at such a rapid pace that it is impractical to retrain a new model from scratch for each new version. In this paper, we propose a new method to enable transfer learning of binary size predictions among versions of the same configurable system. Taking the extreme case of the Linux kernel with its ≈ 14, 500 configuration options, we first investigate how binary size predictions of kernel size degrade over successive versions. We show that the direct reuse of an accurate prediction model from 2017 quickly becomes inaccurate when Linux evolves, up to a 32% mean error by August 2020. We thus propose a new approach for transfer evolution-aware model shifting (TEAMS). It leverages the structure of a configurable system to transfer an initial predictive model towards its future versions with a minimal amount of extra processing for each version. We show that TEAMS vastly outperforms state of the art approaches over the 3 years history of Linux kernels, from 4.13 to 5.8.

Published
2021
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6. Machine Learning and Configurable Systems: A Gentle Introduction

Author

Juliana Alves Pereira, Hugo Martin, Mathieu Acher, Paul Temple, Pontifical Catholic University of Rio de Janeiro (PUC), Diversity-centric Software Engineering (DiverSe), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-LANGAGE ET GÉNIE LOGICIEL (IRISA-D4), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Université de Namur [Namur] (UNamur), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and ANR-17-CE25-0010,VaryVary,Faire Varier la Variabilité(2017)

Subjects
Computer science, 02 engineering and technology, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], Machine learning, computer.software_genre, Con gurable Systems, Field (computer science), Software Product Lines, Machine Learning, Software, Basic knowledge, 020204 information systems, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, Product (category theory), Software product line, ComputingMilieux_MISCELLANEOUS, 050208 finance, business.industry, 05 social sciences, 020207 software engineering, Configurable systems, Specialization (logic), Artificial intelligence, business, computer
Abstract

International audience; The goal of this tutorial is to give a gentle introduction to how machine learning can be used to support software product line conguration. This is our second practical tutorial in this trending eld. The tutorial is based on a systematic literature review and includes practical tasks (specialization, performance and bug prediction) on real-world systems (Linux, VaryLaTeX, x264). The material is designed for academics and practitioners with basic knowledge in software product lines and machine learning.

Published
2020
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7. Sampling Effect on Performance Prediction of Configurable Systems: A Case Study

Author

Juliana Alves Pereira, Hugo Martin, Jean-Marc Jézéquel, Mathieu Acher, Diversity-centric Software Engineering (DiverSe), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-LANGAGE ET GÉNIE LOGICIEL (IRISA-D4), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), ACM, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)

Subjects
Computer science, Sample (statistics), 02 engineering and technology, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], computer.software_genre, Software Product Lines, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Set (abstract data type), Machine Learning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 020204 information systems, Encoding (memory), 0202 electrical engineering, electronic engineering, information engineering, Performance prediction, Software system, ComputingMilieux_MISCELLANEOUS, Additional Key Words and Phrases: Software Product Lines, Sampling (statistics), Configurable Systems, 020207 software engineering, Replicate, [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], Performance Prediction, Data mining, Encoder, computer
Abstract

Numerous software systems are highly configurable and provide a myriad of configuration options that users can tune to fit their functional and performance requirements (e.g., execution time). Measuring all configurations of a system is the most obvious way to understand the effect of options and their interactions, but is too costly or infeasible in practice. Numerous works thus propose to measure only a few configurations (a sample) to learn and predict the performance of any combination of options’ values. A challenging issue is to sample a small and representative set of configurations that leads to a good accuracy of performance prediction models. A recent study devised a new algorithm, called distance-based sampling, that obtains state-of-the-art accurate performance predictions on different subject systems. In this paper, we replicate this study through an in-depth analysis of x264, a popular and configurable video encoder. We systematically measure all 1,152 configurations of x264 with 17 input videos and two quantitative properties (encoding time and encoding size). Our goal is to understand whether there is a dominant sampling strategy over the very same subject system (x264), i.e., whatever the workload and targeted performance properties. The findings from this study show that random sampling leads to more accurate performance models. However, without considering random, there is no single “dominant" sampling, instead different strategies perform best on different inputs and non-functional properties, further challenging practitioners and researchers.

Published
2019
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8. Computational Models for Cumulative Prospect Theory: Application to the Knapsack Problem Under Risk

Author

Patrice Perny, Hugo Martin, DECISION, LIP6, and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0209 industrial biotechnology, Mathematical optimization, Computational model, Cumulative prospect theory, Computer science, Risk aversion, Context (language use), 02 engineering and technology, Framing effect, 020901 industrial engineering & automation, Prospect theory, Knapsack problem, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Expected utility hypothesis
Abstract

International audience; Cumulative Prospect Theory (CPT) is a well known model introduced by Kahneman and Tversky in the context of decision making under risk to overcome some descriptive limitations of Expected Utility. In particular CPT makes it possible to account for the framing effect (outcomes are assessed positively or negatively relatively to a reference point) and the fact that people often exhibit different risk attitudes towards gains and losses. We study here computational aspects related to the implementation of CPT for decision making in combinatorial domains. More precisely, we consider the Knapsack Problem under Risk that consists of selecting the "best" subset of alternatives (investments, projects, candidates) subject to a budget constraint. The alternatives' outcomes may be positive or negative (gains or losses) and are uncertain due to the existence of several possible scenarios of known probability. Preferences over admissible subsets are based on the CPT model and we want to determine the CPT-optimal subset for a risk-averse Decision Maker (DM). The problem requires to optimize a non-linear function over a combinatorial domain. In the paper we introduce two distinct computational models based on mixed-integer linear programming to solve the problem. These models are implemented and tested on randomly generated instances of different sizes to show the practical efficiency of the proposed approach.

Published
2019
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9. BiOWA for Preference Aggregation with Bipolar Scales: Application to Fair Optimization in Combinatorial Domains

Author

Hugo Martin, Patrice Perny, DECISION, LIP6, and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
021103 operations research, Theoretical computer science, Computer science, 0502 economics and business, 05 social sciences, 0211 other engineering and technologies, [INFO]Computer Science [cs], 02 engineering and technology, Aggregation problem, 050207 economics
Abstract

International audience; We study the biOWA model for preference aggregation and multicriteria decision making from bipolar rating scales. A biOWA is an ordered doubly weighted averaging extending standard ordered weighted averaging (OWA) and allowing a finer control of the importance attached to positive and negative evaluations in the aggregation. After establishing some useful properties of biOWA to generate balanced Pareto-optimal solutions, we address fair biOWA-optimization problems in com-binatorial domains. We first consider the use of biOWA in multi-winner elections for aggregating graded approval and disapproval judgements. Then we consider the use of biOWA for solving robust path problems with costs expressing gains and losses. A linearization of biOWA is proposed, allowing both problems to be solved by MIP. A path-ranking algorithm for biOWA optimization is also proposed. Numerical tests are provided to show the practical efficiency of our models.

Published
2019
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10. Virtual Reality Simulator for Construction workers

Author

Eric Monacelli, Mehdi Hafsia, and Hugo Martin

Subjects
Cost efficiency, Process (engineering), Computer science, 05 social sciences, 0211 other engineering and technologies, 02 engineering and technology, Virtual reality, Work related, Occupational safety and health, Field (computer science), Task (project management), Risk analysis (engineering), 021105 building & construction, Added value, 0501 psychology and cognitive sciences, 050107 human factors
Abstract

Construction industries represent the worst industries regarding injury and fatality statistics due to security and health issues. In this field, if not performed correctly, the tasks have risk of causing work related pains or cause death. Currently training sessions are available to train construction workers and are based on two different workshops: Theoretical and practical sessions. However, the risk of injury is still present in the current practical sessions. Therefore, virtual reality might offer a risk free training and raise awareness of health and safety issues amongst construction workers on the workfield. In this paper, we describe the application developed to train construction workers on the operating mode of a formwork panel and the stabilization task in particular and the tests conducted with various Bouygues Construction experts. First results showed that the application was conclusive and cost efficient. In addition the employees assessed the application as innovative, relevant and useful. In conclusion, virtual reality could be of added value in the training process to reduce safety and health issues in the construction industry.

Published
2018
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