3.2.1 Learning from software history how to design software and fix bugs

The first direction is about mining techniques in software repositories (e.g. CVS, SVN, Git). Best practices can be extracted by data mining source code and the version control history of existing software systems. The design and code of expert developers significantly vary from the artifacts of novice developers. We will learn to differentiate those design characteristics by comparing different code bases, and by observing the semantic refactoring actions from version control history. Those design rules can then feed the test-develop-refactor constant adaptation cycle of agile development.

Fault localization of bugs reported in bug repositories. We will build a solid foundation on empirical knowledge about bugs reported in bug repository. We will perform an empirical study on a set of representative bug repositories to identify classes of bugs and patterns of bug data. For this, we will build a tool to browse and annotate bug reports. Browsing will be helped with two kinds of indexing: first, the tool will index all textual artifacts for each bug report; second it will index the semantic information that is not present by default in bug management software, i.e. “contains a stacktrace”). Both indexes will be used to find particular subsets of bug reports, for instance “all bugs mentioning invariants and containing a stacktrace”. Note that queries with this kind of complexity and higher are mostly not possible with the state-of-the-art of bug management software. Then, analysts will use annotation features to annotate bug reports. The main outcome of the empirical study will be the identification of classes of bugs that are appropriate for automated localization. Then, we will run machine learning algorithms to identify the latent links between the bug report content and source code features. Those algorithms would use as training data the existing traceability links between bug reports and source code modifications from version control systems. We will start by using decision trees since they produce a model that is explicit and understandable by expert developers. Depending on the results, other machine learning algorithms will be used. The resulting system will be able to locate elements in source code related to a certain bug report with a certain confidence.

Automated bug fix generation with search-based techniques. Once a location in code is identified as being the cause of the bug, we can try to automatically find a potential fix. We envision different techniques: (1) infer fixes from existing contracts and specifications that are violated; (2) infer fixes from the software behavior specified as a test suite; (3) try different fix types one-by-one from a list of identified bug fix patterns; (4) search fixes in a fix space that consists of combinations of atomic bug fixes. Techniques 1 and 2 are explored in 59 and 83. We will focus on the latter techniques. To identify bug fix patterns and atomic bug fixes, we will perform a large-scale empirical study on software changes (also known as changesets when referring to changes across multiple files). We will develop tools to navigate, query and annotate changesets in a version control system. Then, a grounded theory will be built to master the nature of fixes. Eventually, we will decompose change sets in atomic actions using clustering on changeset actions. We will then use this body of empirical knowledge to feed search-based algorithms (e.g. genetic algorithms) that will look for meaningful fixes in a large fix space. To sum up, our research on automated bug fixing will try not only to point to source code locations responsible of a bug, but to search for code patterns and snippets that may constitute the skeleton of a valid patch. Ultimately, a blend of expert heuristics and learned rules will be able to produce valid source code that can be validated by developers and committed to the code base.

3.2.2 Run-time self-healing

The second proposed research direction is about inventing a self-healing capability at run-time. This is complementary to the previous objective that mainly deals with development time issues. We will achieve this in two steps. First, we want to define frameworks for resilient software systems. Those frameworks will help to maintain the execution even in the presence of bugs, i.e. to let the system survive. As exposed below, this may mean for example to switch to some degraded modes. Next, we want to go a step further and to define solutions for automated runtime repair, that is, not simply compensating the erroneous behavior, but also determining the correct repair actions and applying them at run-time.

Mining best effort values. A well-known principle of software engineering is the “fail-fast” principle. In a nutshell, it states that as soon as something goes wrong, software should stop the execution before entering incorrect states. This is fine when a human user is in the loop, capable of understanding the error or at least rebooting the system. However, the notion of “failure-oblivious computing” 75 shows that in certain domains, software should run in a resilient mode (i.e. capable of recovering from errors) and/or best-effort mode, i.e. a slightly imprecise computation is better than stopping. Hence, we plan to investigate data mining techniques in order to learn best-effort values from past executions (i.e. somehow learning what is a correct state, or the opposite what is not a completely incorrect state). This knowledge will then be used to adapt the software state and flow in order to mitigate the error consequences, the exact opposite of fail-fast for systems with long-running cycles.

Embedding search based algorithms at runtime. Harman recently described the field of search-based software engineering 64. We believe that certain search based approaches can be embedded at runtime with the goal of automatically finding solutions that avoid crashing. We will create software infrastructures that allow automatically detecting and repairing faults at run-time. The methodology for achieving this task is based on three points: (1) empirical study of runtime faults; (2) learning approaches to characterize runtime faults; (3) learning algorithms to produce valid changes to the software runtime state. An empirical study will be performed to analyze those bug reports that are associated with runtime information (e.g. core dumps or stacktraces). After this empirical study, we will create a system that learns on previous repairs how to produce small changes that solve standard runtime bugs (e.g. adding an array bound check to throw a handled domain exception rather than a spurious language exception). To achieve this task, component models will be used to (1) encapsulate the monitoring and reparation meta-programs in appropriate components and (2) support runtime code modification using scripting, reflective or bytecode generation techniques.

3.3.1 Monitoring software in the wild

Once deployed, developers are generally no longer aware of how their software behave. Even if they heavily use testbeds and benchmarks during the development phase, they mostly rely on the bugs explicitly reported by users to monitor the efficiency of their applications. However, it has been shown that contextual artifacts collected at runtime can help to understand performance leaks and optimize the resilience of software systems 86. Monitoring and understanding the context of software at runtime therefore represent the first building block of this research challenge. Practically, we intend to investigate crowd-sensing approaches, to smartly collect and process runtime metrics (e.g. request throughput, energy consumption, user context). Crowd-sensing can be seen as a specific kind of crowdsourcing activity, which refers to the capability of lifting a (large) diffuse group of participants to delegate the task of retrieving trustable data from the field. In particular, crowd-sensing covers not only participatory sensing to involve the user in the sensing task (e.g. surveys), but also opportunistic sensing to exploit mobile sensors carried by the user (e.g. smartphones).

While reported metrics generally enclose raw data, the monitoring layer intends to produce meaningful indicators like the Quality of Experience (QoE) perceived by users. This QoE reflects representative symptoms of software requiring to trigger appropriate decisions in order to improve its efficiency. To diagnose these symptoms, the system has to process a huge variety of data including runtime metrics, but also history of logs to explore the sources of the reported problems and identify opportunities for optimizations. The techniques we envision at this level encompass machine learning, principal component analysis, and fuzzy logic 74 to provide enriched information to the decision level.

3.3.2 Collaborative decision-making approaches

Beyond the symptoms analysis, decisions should be taken in order to improve the Quality of Service (QoS). In our opinion, collaborative approaches represent a promising solution to effectively converge towards the most appropriate optimization to apply for a given symptom. In particular, we believe that exploiting the wisdom of the crowd can help the software to optimize itself by sharing its experience with other software instances exhibiting similar symptoms. The intuition here is that the body of knowledge that supports the optimization process cannot be specific to a single software instance as this would restrain the opportunities for improving the quality and the performance of applications. Rather, we think that any software instance can learn from the experience of others.

With regard to the state-of-the-art, we believe that a multi-levels decision infrastructure, inspired from distributed systems like Spotify 61, can be used to build a decentralized decision-making algorithm involving the surrounding peers before requesting a decision to be taken by more central control entity. In the context of collaborative decision-making, peer-based approaches therefore consist in quickly reaching a consensus on the decision to be adopted by a majority of software instances. Software instances can share their knowledge through a micro-economic model 56, that would weight the recommendations of experienced instances, assuming their age reflects an optimal configuration.

Beyond the peer level, the adoption of algorithms inspired from evolutionary computations, such as genetic programming, at an upper level of decision can offer an opportunity to test and compare several alternative decisions for a given symptom and to observe how does the crowd of applications evolves. By introducing some diversity within this population of applications, some instances will not only provide a satisfying QoS, but will also become naturally resilient to unforeseen situations.

3.3.3 Smart reconfigurations in the large

Any decision taken by the crowd requires to propagate back to and then operated by the software instances. While simplest decisions tend to impact software instances located on a single host (e.g. laptop, smartphone), this process can also exhibit more complex reconfiguration scenarios that require the orchestration of various actions that have to be safely coordinated across a large number of hosts. While it is generally acknowledged that centralized approaches raise scalability issues, we think that self-optimization should investigate different reconfiguration strategies to propagate and apply the appropriate actions. The investigation of such strategies can be addressed in two steps: the consideration of scalable data propagation protocols and the identification of smart reconfiguration mechanisms.

With regard to the challenge of scalable data propagation protocols, we think that research opportunities encompass not only the exploitation of gossip-based protocols 60, but also the adoption of publish/subscribe abstractions 69 in order to decouple the decision process from the reconfiguration. The fundamental issue here is the definition of a communication substrate that can accommodate the propagation of decisions with relaxed properties, inspired by Delay Tolerant Networks (DTN), in order to reach weakly connected software instances. We believe that the adoption of asynchronous communication protocols can provide the sustainable foundations for addressing various execution environments including harsh environments, such as developing countries, which suffer from a partial connectivity to the network. Additionally, we are interested in developing the principle of social networks of applications in order to seamlessly group and organize software instances according to their similarities and acquaintances. The underlying idea is that grouping application instances can contribute to the identification of optimization profiles not only contributing to the monitoring layer, but also interested in similar reconfigurations. Social networks of applications can contribute to the anticipation of reconfigurations by exploiting the symptoms of similar applications to improve the performance of others before that problems actually happen.

With regard to the challenge of smart reconfiguration mechanisms, we are interested in building on our established experience of adaptive middleware 79 in order to investigate novel approaches to efficient application reconfigurations. In particular, we are interested in adopting seamless micro-updates and micro-reboot techniques to provide in-situ reconfiguration of pieces of software. Additionally, the provision of safe and secured reconfiguration mechanisms is clearly a key issue that requires to be carefully addressed in order to avoid malicious exploitation of dynamic reconfiguration mechanisms against the software itself. In this area, although some reconfiguration mechanisms integrate transaction models 68, most of them are restricted to local reconfigurations, without providing any support for executing distributed reconfiguration transactions. Additionally, none of the approached published in the literature include security mechanisms to preserve from unauthorized or malicious reconfigurations.

7.1.1 APISENSE

• Keywords: Mobile sensing, Crowd-sensing, Mobile application, Crowd-sourcing, Android
• Functional Description: APISENSE platform is a software solution to collect various contextual information from Android devices (client application) and automatically upload collected data to a server (deployed as a SaaS). APISENSE is based on a Cloud computing infrastructure to facilitate datasets collection from significant populations of mobile users for research purposes.
• URL: https://apisense.io
• Authors: Antoine Veuiller, Christophe Ribeiro, Julien Duribreux, Romain Rouvoy, Nicolas Haderer, Romain Sommerard, Lakhdar Meftah
• Contact: Romain Rouvoy
• Partner: Université de Lille

7.1.2 PowerAPI

• Keywords: Energy efficiency, Energy management
• Functional Description:

  PowerAPI is a library for monitoring the energy consumption of software systems.

  PowerAPI differs from existing energy process-level monitoring tools in its software orientation, with a fully customizable and modular solution that lets the user precisely define what he/she wants to monitor. PowerAPI is based on a modular and asynchronous event-driven architecture using the Akka library. PowerAPI offers an API which can be used to define requests about energy spent by a process, following its hardware resource utilization (in term of CPU, memory, disk, network, etc.).

• URL: http://powerapi.org
• Contact: Romain Rouvoy
• Participants: Adel Noureddine, Loïc Huertas, Maxime Colmant, Romain Rouvoy, Mohammed Chakib Belgaid, Arthur D'azemar

7.1.3 amiunique

• Name: amiunique
• Keywords: Privacy, Browser fingerprinting
• Scientific Description:

  The amiunique web site has been deployed in 2014 in the context of the DiverSE team research activities on browser fingerprinting to understand how software diversity can be leveraged to mitigate the impact of fingerprinting on the privacy of users. In 2018, it was migrated to the Spirals team where the research on browser fingerprinting still continues to this day.

  The web site has yielded multiple datasets of genuine fingerprints to understand the multiple facets of browser fingerprinting and how they can be used on the web to reinforce security. The web site presents regular updates to include the latest development in web technology and understand their impact of users' privacy.

  The whole source code of amiunique is open source and is distributed under the terms of the MIT license.

  Main innovative features:

  • canvas fingerprinting
  • WebGL fingerprinting
  • advanced JS features (platform, DNT, etc.)

  Impact: The website has been visited by more than 3,000,000 unique visitors since its creation and it has been showcased in several professional forums and tutorial sessions over the years. It produced multiple datasets over the years that were used in articles published in top-tier conferences. Amiunique has received in 2018 the prize “Protection de la vie privée” granted by Inria and the CNIL. The research around fingerprints in amiunique has also been a source of influence for the Brave web browser.

• Functional Description: This web site aims at informing visitors about browser fingerprinting and possible tools to mitigate its effect, as well as at collecting data about the fingerprints that can be found on the web. It collects browser fingerprints with the explicit agreement of the users (they have to click on a button on the home page). Fingerprints are composed of 17 attributes, which include regular HTTP headers as well as the most recent state of the art techniques (canvas fingerprinting, WebGL information).
• URL: https://amiunique.org/
• Authors: Pierre Laperdrix, Antonin Durey, Walter Rudametkin Ivey
• Contact: Benoit Baudry
• Partners: INSA Rennes, Université de Lille

10.1.1 Inria international partners

10.1.2 Participation in other international programs

10.2.1 Collaborations in European programs, except FP7 and H2020

10.3.1 ANR

10.3.2 CNRS Momentum

CIRRUS

Participants: Guillaume Fieni, Alexandre Garnier, Philippe Merle, Romain Rouvoy, Lionel Seinturier.

CIRRUS is a 3-year (2017–20) joint team with the Scalair cloud operator and architect company funded by the Hauts-de-France region. The CIRRUS joint team is developing novel solutions in the domains of the on demand configuration of heterogeneous cloud resources, the management of cloud elasticity for all deployed services (SaaS, PaaS, IaaS) in order to guarantee quality of service and user quality of experience, and the taming of financial costs of cloud infrastructures.

Rigorous Component-Based Design of Correct-by-Construction Software and Systems: Application to Cloud Computing

Participants: Simon Bliudze, Trinh Le Khanh, Larisa Safina.

This 24-month (2019–20) project is funded in the context of the STaRS program of Hauts-de-France region. It aims at the development of methods and tools for the rigorous design of cloud computing platforms and applications, which can be proven to be correct by construction. First results have been published in 1453, 54.

BRiCoS: Building Blocks for Rigorous Design of Concurrent Software

Participants: Simon Bliudze, Larisa Safina.

This 24-month (2021–22) project — funded by the I-SITE ULNE Foundation in the framework of its “Support for young researchers 2020” programme — addresses two fundamental challenges for the implementation of the Rigorous System Design approach to general-purpose software: 1) obtaining behavioural models of the coordinated software entities and 2) detecting the deviations between these models and the corresponding executable code in the face of software evolution.

ASCOT: Advanced Script analysis to prevent Online Tracking

Participants: Romain Fouquet, Jean Luc Intumwayase, Pierre Laperdrix, Romain Rouvoy, Walter Rudametkin Ivey.

This 36-month (2020–23) project is funded in the context of the STaRS program of Hauts-de-France region. The goal of the project is to improve web security and privacy and put back control into users hands by blocking unwanted trackers. The project will combine information flow analysis, machine learning and deobfuscation to detect a wide range of trackers. The project will also identify page breakage when blocking online trackers. All in all, this project aims to advance the actual techniques that protect users online while maintaining a high level of usability that is key to offer a comfortable browsing experience.

Indoor Analytics

Participants: Pierre Bourhis, Remy Raes, Romain Rouvoy, Lionel Seinturier.

Indoor Analytics is a 32-month (2019–21) project funded in the context of CPER Data program. Indoor Analytics aims at collaborating with the Mapwize company on the development of novel analytics for indoor location systems. In particular, Mapwize and Spirals target the joint delivery of an open-source software solution devoted to the acquisition, storage and processing of location events at scale.

COMMODE

Participants: Pierre Bourhis, Laurence Duchien, Clément Quinton.

COMMODE (Knowledge COMpilation for feature MODEls) is a 24-month (2019–21) project funded in the context of CPER Data program. COMMODE aims at using techiques from knowledge compilation, a subarea of artificial intelligence, for feature models, a representation of software products used in software engineering.

ADT FingerKit

Participants: Antoine Canda, Antonin Durey, Pierre Laperdrix, Walter Rudametkin Ivey.

ADT FingerKit (2018–20) is a technology development initiative supported by the Inria Lille - Nord Europe Center that focuses on the design and development of a new and enhanced version of the AmIUnique platform. AmIUnique is a data collection and analysis platform to better understand, analyze and vulgarize the uses and threats of browser fingerprinting. This initiative led by Inria is a key asset to better understand novel techniques that threatens the user privacy on Internet. This ADT builds on our first results with the PhD thesis of Antoine Vastel 82.

ADT e-Lens

Participants: Arthur d'Azémar, Guillaume Fieni, Romain Rouvoy.

ADT e-Lens (2018–21) is a technology development initiative supported by the Inria Lille - Nord Europe Center that aims at extending the PowerAPI energy monitoring library that we develop in the team since 2011. The extension deals with the integration of new power models (for GPU, disk, network interface), the implementation of a self-optimization algorithm, the port of the platform to embedded systems running with Raspberry Pi, ROS and Android, and the implementation of an active learning algorithm for power models. This ADT builds on our results with the defended PhD theses of Adel Noureddine 72 and Maxime Colmant 58, and with the ongoing PhD thesis of Guillaume Fieni.

11.1.1 Scientific Events: Selection

11.1.2 Journal

11.1.3 Invited Talks

11.1.4 Leadership within the Scientific Community

Simon Bliudze is co-head of the YODA (trustworthY and Optimal Dynamic Adaptation) working group of the GDR GPL. He was guest editor for the Springer Innovations in Systems and Software Engineering journal special issue with selected papers presented at VECoS '18, the Springer Software Tools for Technology Transfer journal special issue with selected papers presented at MeTRiD '18–'19, and the CCSD Logical Methods in Computer Science journal special issue with selected papers presented at COORDINATION '20. Since June 2020, he is a memeber of the Steering Committee of COORDINATION.

Laurence Duchien is a member of the scientific councils of IMT Atlantique (Nantes), Labex CIMI (Toulouse), IRT SystemX (Saclay). She is member of the steering committees of the European Conference on Software Architecture (ECSA) and of the Systems and Software Product Lines Conference (SPLC). She was guest editor for the Empirical Software Engineering Journal for a special issue on software configuration.

Philippe Merle is an elected member of the Inria scientific board (CS).

Romain Rouvoy is an elected member of the “bureau” of the French chapter of the ACM Special Interest Group in Operating Systems (SIGOPS / ASF) and elected member of the administrative council of SpecifCampus. He is co-head of the “Groupe de Travail Génie Logiciel pour les Systèmes Cyber-physiques” of the GDR GPL.

11.1.5 Scientific Expertise

Simon Bliudze was member of the recruitment committee of junior researchers (CRCN and ISFP) at Inria Lille. He was a member of the chair professor recruitement committee at UCLouvain.

Laurence Duchien was member of the recruitment committee for two researchers at the University of Luxembourg. She was president of the recruitment committee for Professor in Computer Science at ENS Paris. She was president of the CNRS Research Scientists committee for section CoNRS 6. She was member of the jury for the GDR GPL Prix de thèse. She was expert for MESRI DGRI and evaluated projects for the PHC and STIC-Asie programs.

Romain Rouvoy was member of scientific committee 25 — Infrastructures (networks, high performance computing and storage), software sciences and technologies of the French Research Agency (ANR). He was scientific expert for DRRT IDF.

Lionel Seinturier was president of the recruitment committee for Professor in Computer Science at the University of Lille, and member of the recruitment committee for Associate Professor in Computer Science at the University of Paris. He was member of the Institut Universitaire de France (IUF) Senior Jury. He was scientific expert for the Swiss National Science Foundation, DRRT IDF.

11.1.6 Research Administration

Simon Bliudze is an elected member of the comité de centre Inria Lille Nord Europe.

Laurence Duchien is member of the CNRS CoCNRS section 6 committee, and of the “bureau” of this committee. She is in charge of the Career development & Intersectoral secondments in the PEARL Project (“Programme for EArly-stage Researchers in Lille”) at I-SITE Université Lille – Nord Europe.

Antonin Durey is elected member of the CRIStAL laboratory council.

Philippe Merle is an elected member of the Inria technical committee (CTI), elected member of the Inria national committee on “hygiène, de sécurité et des conditions de travail” (CNHSCT), president of the CUMI (Comité des Utilisateurs des Moyens Informatiques), permanent secretary of the CLHSCT (Comité Local d'Hygiène, de Sécurité et de Conditions de Travail), and elected member of the centre committee for the Inria Lille – Nord Europe research center. He is member of the steering committee of the Inria’s continuous integration service. He is member of SPI doctoral school council of University of Lille.

Walter Rudametkin is an member of the CDT (Comité de Développement Technologique) of the Inria Lille – Nord Europe research center. He is an elected member of the CRIStAL laboratory council.

Lionel Seinturier is a member of the BSC (Bureau Scientifique du Centre) for the Inria Lille – Nord Europe research center. He is chair of CNU27, the Computer Science section of the “Conseil National des Universités”.

11.2.1 Teaching

Simon Bliudze is, in addition to his tenure Junior Researcher position at Inria, part-time Associate Professor at École Polytechnique, Palaiseau, France, in the Department of Computer Sciences (DIX).

• INF411: Les bases de la programmation et de l'algorithmique, 40h, $2^{\mathrm{nd}}$ year of the Engineering cycle
• INF442: Traitement des données massives, 40h, $2^{\mathrm{nd}}$ year of the Engineering cycle

Pierre Bourhis is, in addition to his tenure Junior Researcher position at CNRS, part-time Associate Professor of Data Science at École Polytechnique, Palaiseau, France, in the Department of Computer Sciences (DIX).

• Info553: Bases de données, 18h, Cycle Polytechnique
• Modal Graphe Géant, 36h
• INF517: Projet de Recherche Data Science, 20h
• INF583: System for Big Data, 20h

Laurence Duchien teaches at the Université de Lille in the FST faculty. She is project leader for doctoral studies at Université de Lille.

• Software engineering project, 60h, Level M2, Master MIAGE FI
• Software engineering project, 50h, Level M2, Master MIAGE FC/FA
• Research initiation, 20h, Level M2, Master of Computer Science

Clément Quinton teaches at the Université de Lille in the FST faculty.

• Introduction to Computer Science, 46.5h, Level L1, Licence of Computer Science
• Object-oriented programming, 36h, Level L2, Licence of Computer Science
• Object-oriented design, 42h, Level L3, Licence of Computer Science
• Design of distributed applications, 42h, Level M1, Master of Computer Science
• Advanced design of distributed applications, 37.5h, Level M2, Master MIAGE
• Infrastructure and frameworks for the Internet, 33.75h, Level M2, Master of Computer Science
• Software product lines, 7.5h, Level M2, Master of Computer Science
• Suivi de stages et de projets, 30h, Licence and Master of Computer Science

Romain Rouvoy teaches at the Université de Lille in the FST faculty. He heads the Master of Computer Science program at the Université de Lille.

• Design of distributed applications, 12h, Level M1, Master of Computer Science
• Object-oriented design, 4h, Level L3, Licence of Computer Science
• Suivi de projets, 20h, Level M2, Master of Computer Science

Walter Rudametkin Ivey teaches at the Polytech Lille engineering school.

• GIS4 Programmation par Objets, 32h
• GIS4 Architectures Logicielles, 26h
• GIS2A3 (apprentissage) Projet programmation par Objet, 24h
• IMA2A4 (apprentissage) Conception Modélisation Objet, 24h
• IMA3 Programmation Avancée, 62h
• GBIAAL4 Bases de données, 22h
• GIS5 Suivi de projets, 42h
• GIS2A (apprentissage) Suivi d'apprentis, 28h

Lionel Seinturier teaches at the Université de Lille in the FST faculty. Until July 2019, he headed the Computer Science Department at the Faculty of Science and Technology of the Université de Lille.

• Conception d'Applications Réparties, 50h, Level M1, Master MIAGE
• Infrastructures et Frameworks Internet, 70h, Level M2 E-Services IAGL TIIR, Master of Computer Science

11.2.2 Supervision

• PhD in progress: Zeinab Abou Khalil, Understanding the impact of release policies in software development processes, November 2017, Laurence Duchien & Clément Quinton, co-supervision with Tom Mens (University of Mons).
• PhD in progress: Mohammed Chakib Belgaid, Développement durable des logiciels vers une optimisation énergétique de bout en bout des systèmes logiciels, January 2018, Romain Rouvoy & Lionel Seinturier.
• PhD in progress: Sacha Brisset, Automatic Spotting and fixing or Recurrent user Experience issues. Detecting and Fixing Anomalies by applying Machine Learning on user Experience Data, November 2018, Lionel Seinturier & Romain Rouvoy & Renaud Pawlak & Yoann Couillec.
• PhD in progress: Antonin Durey, Leveraging Browser Fingerprinting to Fight Fraud on the Web, October 2018, Romain Rouvoy & Walter Rudametkin & Lionel Seinturier.
• PhD in progress: Salman Farhat, Safe Dynamic Reconfiguration of Cloud Applications, October 2020, Laurence Duchien & Simon Bliudze.
• PhD in progress: Guillaume Fieni, GreenData : Vers un traitement efficient et éco-responsable des grandes masses de données numériques, October 2017, Romain Rouvoy & Lionel Seinturier.
• PhD in progress: Romain Fouquet, Improving Online Privacy through Content Blocking and Information Restriction, October 2020, Romain Rouvoy & Pierre Laperdrix.
• PhD in progress: Omid Gheibi, Dependable Adaptive Software Systems for the Digital World, October 2020, Laurence Duchien & Clément Quinton, co-supervision with Danny Weyns (KU Leuven).
• PhD in progress: Edouard Guegain, Configuration of Large Scale Fog Environments, October 2020, Clément Quinton.
• PhD in progress: Jean-Luc Intumwayase, Improving Online Privacy through Content Blocking and Information Restriction, November 2020, Romain Rouvoy & Pierre Laperdrix.
• PhD in progress: Trình Lê Khánh, Design of Correct-by-Construction Self-Adaptive Cloud Applications using Formal Methods, October 2019, Philippe Merle & Simon Bliudze.
• PhD in progress: Naif Mehanna, Hardening Web Authentication with Browser Fingerprinting, October 2020, Walter Rudametkin.
• PhD in progress: Vikas Mishra, Collaborative Strategies to Protect Against Browser Fingerprinting, October 2018, Romain Rouvoy, Walter Rudametkin & Lionel Seinturier.
• PhD in progress: Zakaria Ournani, Eco-conception des logiciels : modélisation de l’efficience énergétique des logiciels et conception d’outils pour mesurer et réduire leur consommation d’énergie, November 2018, Romain Rouvoy.
• PhD in progress: Marion Tommasi, Collaborative Data-centric Workflows: Towards Knowledge Centric Workflows and Integrating Uncertain Data, October 2018, Pierre Bourhis & Lionel Seinturier.

11.2.3 Juries

PhD juries, or otherwise specified such as HDR.

Laurence Duchien

• Rabin Sahu (Université de Lille), president
• Cagan Arslan (Université de Lille), president
• Maverick Chardet (IMT Atlantique), president
• Lorenzo Pagliani (University of L’Aquilla, Italy), reviewer
• Neil Ayeb (Université Grenoble Alpes), reviewer
• Sami Lazreg (Université de Nice), reviewer
• Matthieu Amy (Université de Toulouse), examiner
• Hussein Mhanna (Université Paris-Saclay), examiner

Philippe Merle

• Hayet Brabra (Télécom Sud Paris), president

Romain Rouvoy

• Yewan Wang (IMT Atlantique), reviewer & president
• Jean-Émile Dartois (Université de Rennes 1), reviewer
• Dalimir Orfanus (University of Oslo, Norway), opponent
• Dorian Burihabwa (University of Neuchâtel, Switzerland), reviewer
• Emile Cadorel (IMT Atlantique), reviewer
• Florentin Delaine (INSA Lyon), reviewer
• Louison Gitzinger (Université de Rennes 1), reviewer

Lionel Seinturier

• HDR, Stéphanie Chollet (Université Grenoble Alpes), reviewer
• HDR, Anne-Cécile Orgerie (ENS Rennes), examiner
• Soulaimane Guedria (Université Grenoble Alpes), reviewer

Pierre Laperdrix

• Nampoina Andriamilanto (Université de Rennes 1), examiner

International journals

• 11 articleZeinabZ. Abou khalil, EleniE. Constantinou, TomT. Mens and LaurenceL. Duchien. On the impact of release policies on bug handling activity: A case study of EclipseJournal of Systems and Software173March 2021, 110882
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• 12 articleMehdiM. Ahmed-Nacer, SlimS. Kallel, FaiezF. Zalila, PhilippeP. Merle and WalidW. Gaaloul. Model driven simulation of elastic OCCI cloud resourcesThe Computer Journal2021, 1-22
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• 13 articleAntoineA. Amarilli, PierreP. Bourhis, StefanS. Mengel and MatthiasM. Niewerth. Constant-Delay Enumeration for Nondeterministic Document SpannersSIGMOD record491September 2020, 25-32
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• 14 articleEduardE. Baranov and SimonS. Bliudze. Expressiveness of component-based frameworks: A study of the expressiveness of BIPActa Informatica57December 2020, 761--800
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• 15 articleSenjutiS. Basu Roy, LeiL. Chen, AtsuyukiA. Morishima, James AbelloJ. Monedero, PierreP. Bourhis, FrançoisF. Charoy, MarinaM. Danilevsky, GautamG. Das, GianlucaG. Demartini, AbishekA. Dubey, ShadyS. Elbassuoni, DavidD. Gross-Amblard, EmilieE. Hoareau, MunenariM. Inoguchi, JaredJ. Kenworthy, ItaruI. Kitahara, DongwonD. Lee, YunyaoY. Li, Ria MaeR. Borromeo, PaoloP. Papotti, RaghavR. Rao, SudeepaS. Roy, PierreP. Senellart, KeishiK. Tajima, SaravananS. Thirumuruganathan, MarionM. Tommasi, KazutoshiK. Umemoto, AndreaA. Wiggins, KoichiroK. Yoshida and SihemS. Amer-Yahia. Making AI Machines Work for Humans in FoWSIGMOD record492December 2020, 30-35
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• 16 articleMichaelM. Benedikt, PierreP. Bourhis, GeorgG. Gottlob and PierreP. Senellart. Monadic Datalog, Tree Validity, and Limited Access ContainmentACM Transactions on Computational Logic2112020, 6:1-6:45
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• 17 article PierreP. Bourhis, Juan LJ. Reutter and DomagojD. Vrgoč. JSON: Data model and query languages Information Systems 89 March 2020
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• 18 articleStéphanieS. Challita, FabianF. Korte, JohannesJ. Erbel, FaiezF. Zalila, JensJ. Grabowski and PhilippeP. Merle. Model-Based Cloud Resource Management with TOSCA and OCCISoftware and Systems ModelingFebruary 2021, 1-23
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• 19 articleBenjaminB. Danglot, MartinM. Monperrus, WalterW. Rudametkin and BenoitB. Baudry. An approach and benchmark to detect behavioral changes of commits in continuous integrationEmpirical Software Engineering254July 2020, 2379-2415
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• 20 article SarraS. Habchi, NaouelN. Moha and RomainR. Rouvoy. Android Code Smells: From Introduction to Refactoring Journal of Systems and Software 2021
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• 21 articleLakhdarL. Meftah, RomainR. Rouvoy and IsabelleI. Chrisment. Empowering Mobile Crowdsourcing Apps with User Privacy ControlJournal of Parallel and Distributed ComputingAugust 2020, 15
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• 22 article ClémentC. Quinton, MichaelM. Vierhauser, RickR. Rabiser, LucianoL. Baresi, PaulP. Grünbacher and ChristianC. Schuhmayer. Evolution in Dynamic Software Product Lines Journal of Software: Evolution and Process May 2020
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International peer-reviewed conferences

• 23 inproceedings BabakB. Amin Azad, OleksiiO. Starov, PierreP. Laperdrix and NickN. Nikiforakis. Taming The Shape Shifter: Detecting Anti-fingerprinting Browsers DIMVA 2020 - 17th Conference on Detection of Intrusions and Malware & Vulnerability Assessment Lisboa / Virtual, Portugal June 2020
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• 24 inproceedings BabakB. Amin Azad, OleksiiO. Starov, PierreP. Laperdrix and NickN. Nikiforakis. Web Runner 2049: Evaluating Third-Party Anti-bot Services DIMVA 2020 - 17th Conference on Detection of Intrusions and Malware & Vulnerability Assessment Lisboa / Virtual, Portugal https://dimva2020.campus.ciencias.ulisboa.pt/ June 2020
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• 25 inproceedings PierreP. Bourhis, DanielD. Deutch and YuvalY. Moskovitch. Equivalence-Invariant Algebraic Provenance for Hyperplane Update Queries Sigmod Portland, United States June 2020
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• 26 inproceedingsPierreP. Bourhis, LoïcL. Hélouët, ZoltanZ. Miklos and RiturajR. Singh. Data Centric Workflows for CrowdsourcingPetri Nets 2020 - 41st International Conference on Application and Theory of Petri Nets and ConcurrencyParis, FranceJune 2020, 1-39
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• 27 inproceedingsJonatanJ. Enes, GuillaumeG. Fieni, Roberto RR. Expósito, RomainR. Rouvoy and JuanJ. Tourino. Power Budgeting of Big Data Applications in Container-based ClustersIEEE Cluster 2020Kobe, Japanhttps://clustercomp.org/2020/September 2020, 281-287
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• 28 inproceedings GuillaumeG. Fieni, RomainR. Rouvoy and LionelL. Seinturier. SELFWATTS: On-the-fly Selection of Performance Events to Optimize Software-defined Power Meters CCGRID 2021 - 21th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing Melbourne, Australia http://cloudbus.org/ccgrid2021/mainpage.html May 2021
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• 29 inproceedings GuillaumeG. Fieni, RomainR. Rouvoy and LionelL. Seinturier. SmartWatts: Self-Calibrating Software-Defined Power Meter for Containers CCGRID 2020 - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing Melbourne, Australia http://cloudbus.org/ccgrid2020/ May 2020
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• 30 inproceedings PierreP. Laperdrix, OleksiiO. Starov, QuanQ. Chen, AlexandrosA. Kapravelos and NickN. Nikiforakis. Fingerprinting in Style: Detecting Browser Extensions via Injected Style Sheets 30th USENIX Security Symposium Virtual, France https://www.usenix.org/conference/usenixsecurity21 August 2021
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• 31 inproceedings JulienJ. Leveau, XavierX. Blanc, LaurentL. Réveillère, Jean-RémyJ.-R. Falleri and RomainR. Rouvoy. Fostering the Diversity of Exploratory Testing in Web Applications ICST 2020 - IEEE International Conference on Software Testing, Verification and Validation Porto, Portugal https://icst2020.info/ March 2020
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• 32 inproceedings LakhdarL. Meftah, RomainR. Rouvoy and IsabelleI. Chrisment. Capturing Privacy-preserving User Contexts with IndoorHash DAIS 2020 - 20th IFIP International Conference on Distributed Applications and Interoperable Systems 12135 Valletta, Malta http://www.discotec.org/2020/dais.html June 2020
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• 33 inproceedings GordonG. Meiser, PierreP. Laperdrix and BenB. Stock. Careful Who You Trust: Studying the Pitfalls of Cross-Origin Communication ASIACCS 2021 - 16th ACM Asia Conference on Computer and Communications Security 16th ACM Asia Conference on Computer and Communications Security Hong Kong / Virtual, China https://asiaccs2021.comp.polyu.edu.hk/ June 2021
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• 34 inproceedings VikasV. Mishra, PierreP. Laperdrix, WalterW. Rudametkin and RomainR. Rouvoy. Déjà vu: Abusing Browser Cache Headers to Identify and Track Online Users PETS 2021 - The 21th International Symposium on Privacy Enhancing Technologies Virtual, France July 2021
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• 35 inproceedings VikasV. Mishra, PierreP. Laperdrix, AntoineA. Vastel, WalterW. Rudametkin, RomainR. Rouvoy and MartinM. Lopatka. Don’t count me out: On the relevance of IP addresses in the tracking ecosystem The Web Conference 2020 Security, Privacy, and Trust Proceedings of The Web Conference (WWW'20) Tapeï, Taiwan https://www2020.thewebconf.org/ April 2020
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• 36 inproceedings ZakariaZ. Ournani, Mohammed ChakibM. Belgaid, RomainR. Rouvoy, PierreP. Rust, JoëlJ. Penhoat and LionelL. Seinturier. Taming Energy Consumption Variations in Systems Benchmarking ICPE'2020 - 11th ACM/SPEC International Conference on Performance Engineering Edmonton, Canada https://icpe2020.spec.org/ April 2020
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• 37 inproceedings ZakariaZ. Ournani, RomainR. Rouvoy, PierreP. Rust and JoëlJ. Penhoat. On Reducing the Energy Consumption of Software: From Hurdles to Requirements ESEM 2020 - ACM/IEEE International Symposium on Empirical Software Engineering and Measurement Bari, Italy https://eseiw2020.di.uniba.it/ October 2020
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• 38 inproceedings Best paper AntoineA. Vastel, WalterW. Rudametkin, RomainR. Rouvoy and XavierX. Blanc. FP-Crawlers: Studying the Resilience of Browser Fingerprinting to Block Crawlers MADWeb'20 - NDSS Workshop on Measurements, Attacks, and Defenses for the Web San Diego, United States https://madweb.work/ February 2020
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• 39 inproceedings DavidD. Zeber, SarahS. Bird, CamilaC. Oliveira, WalterW. Rudametkin, IlanaI. Segall, FredrikF. Wollsén and MartinM. Lopatka. The Representativeness of Automated Web Crawls as a Surrogate for Human Browsing The Web Conference Proceedings of The Web Conference (WWW'20) Taipei, Taiwan April 2020
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Scientific book chapters

• 40 inbook BenoitB. Baudry, Yérom-DavidY.-D. Bromberg, DavideD. Frey, AlejandroA. Gómez-Boix, PierreP. Laperdrix and FrançoisF. Taïani. Profilage de navigateurs : état de l’art et contre-mesures Le profilage en ligne : entre libéralisme et régulation October 2020
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Edition (books, proceedings, special issue of a journal)

• 41 proceedings S. Bliudze M. Atig Special Issue: 12th International Conference on Verification and Evaluation of Computer and Communication Systems (VECoS 2018) June 2020
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• 42 proceedings S. Bliudze L. Bocchi 22nd IFIP WG 6.1 International Conference, COORDINATION 2020, Held as Part of the 15th International Federated Conference on Distributed Computing Techniques, DisCoTec 2020, Valletta, Malta, June 15–19, 2020, Proceedings June 2020
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Doctoral dissertations and habilitation theses

• 43 thesis ZeinabZ. Abou khalil. Understanding the Impact of Release Policies on Software Development Processes. Université de Lille; Universite de Mons February 2021
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Reports & preprints

• 44 report PierreP. Bourhis, MichelM. Leclère, Marie-LaureM.-L. Mugnier, SophieS. Tison, FedericoF. Ulliana and LilyL. Galois. Oblivious and Semi-Oblivious Boundedness for Existential Rules LIRMM (UM, CNRS) June 2020
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• 45 misc GeorgeG. Giakkoupis, Anne-MarieA.-M. Kermarrec, OlivierO. Ruas and FrançoisF. Taïani. Cluster-and-Conquer: When Randomness Meets Graph Locality October 2020
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• 46 report PhilippeP. Merle, SouhaS. Ben Rayana, LionelL. Seinturier, RogerR. Pissard-Gibollet, Jean-BernardJ.-B. Stefani and AdjaA. Ndeye Sylla. Towards a formal reference computational model for cloud configuration management INRIA January 2020
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