11 Aug 2026
Job Information
- Organisation/Company: Inria, the French national research institute for the digital sciences
Offer Description
Industrial Context:
Berger-Levrault designs and maintains software solutions for public administrations in highly regulated sectors, such as education, healthcare, social services, and regional management. These products are built for the long term: they must continue to evolve while incorporating technological, organizational, and architectural choices accumulated over several decades.
In this context, understanding software involves more than just reading its code. Major challenges also lie in its production context: change history, tickets, reviews, pipelines, knowledge sharing, and the distribution of responsibilities. The thesis aims to model and analyze this environment to produce insights useful for system maintenance and evolution.
Organization of the research work:
The doctoral student will be jointly supervised by Berger-Levrault and the EVREF team at Inria Lille, in order to align industrial needs, expertise in software maintenance and evolution, and tool development on Moose. The proposed timeline is organized into three phases:
- scientific framing and modeling,
- integration and correlation of sources,
- followed by evaluation of prototypes and dissemination of results.
References:
- [BBN+17] Lionel Briand, Domenico Bianculli, Shiva Nejati, Fabrizio Pastore, and Mehrdad Sabetzadeh. The case for context-driven software engineering research. IEEE Software, September 2017.
- [DDN02] Serge Demeyer, Stéphane Ducasse, and Oscar Nierstrasz. Object-Oriented Reengineering Patterns. Morgan Kaufmann, 2002.
- [MZ13] Tim Menzies and Thomas Zimmermann. Software analytics : So what ? IEEE Software, July 2013.
- [Per21] Quentin Perez. Gestion des contributions architecturales dans les projets logiciels : Métriques, analyses empiriques et apprentissage machine. Phd thesis, 2021.
Background and objectives of the Thesis:
Legacy information systems face challenges related to longevity, transformation, and risk [DDN02]. Code aging, knowledge loss, and technical debt are exacerbated by the fragmentation of software development across multiple tools, even though it is often this fragmentation that accounts for maintenance difficulties.
Recurring problems fall into three categories: a loss of overall visibility due to the fragmentation of information across code, tickets, reviews, and pipelines; a loss or concentration of knowledge linked to staff turnover; and high-risk changes due to the inability to link software structure, development activity, and work organization.
Thesis objectives:
The thesis aims to define a unified framework for analyzing the software production context, implement it as a Moose extension capable of linking code, tickets, reviews, and pipelines, and then produce actionable analyses and visualizations to help identify expertise, unstable areas, knowledge concentrations, and maintenance risks. These objectives will be evaluated using representative case studies from Berger-Levrault.
Industrial and scientific challenges for Berger-Levrault:
Scientific Challenges: This thesis is grounded in the framework of actionable analytics [MZ13, BBN+17]: producing contextualized, interpretable analyses that are useful for decision-making. Three key challenges structure the topic: defining an extensible metamodel of the production context, correlating heterogeneous sources to reconstruct relevant units of analysis, and transforming this data into actionable indicators and visualizations for maintenance.
Technical challenges: The main technical challenges involve defining a metamodel common to multiple platforms, establishing robust relationships between code, tickets, reviews, and pipelines, and scaling up processing in an industrial setting.
Proposed solutions: The thesis will propose an extension of Moose dedicated to analyzing the software production context. It will focus on integrating data from repositories, forges, tickets, and pipelines; linking them within a common metamodel; and then producing analyses useful to teams: identifying expertise, pinpointing knowledge concentrations, characterizing maintenance cycles, and presenting results through visualizations tailored to industrial needs.
Related work:
Research on understanding legacy systems highlights the importance of linking software structure and maintenance activities [DDN02]. More recently, Perez [Per21] has shown that ownership can be characterized by the nature of the modified artifacts. The proposed research extends this approach by aiming for a unified modeling of the production context, at the intersection of reverse engineering, metamodeling, process mining, and visualization.
Where to apply
Website: https://jobs.inria.fr/public/classic/en/offres/2026-10385
Requirements
Skills/Qualifications
Languages:
- French and English
- Spoken and written
Additional skills preferred:
- Oral presentation
- Writing (articles, reports)
Specific Requirements
Development:
- Object-oriented programming
- Metaprogramming
- Metamodeling
- Design patterns
Process:
- TDD
- Refactoring
Tools:
- Git
- Statistics
- Visualization
- Data analytics
Languages: FRENCH
Level: Basic
Languages: ENGLISH
Level: Good
Additional Information
Benefits
- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage
€2,300 gross per month
Selection process
Please provide your CV and cover letter
Website for additional job details: https://jobs.inria.fr/public/classic/en/offres/2026-10385
Work Location(s)
Number of offers available: 1
Company/Institute: Inria
Country: France
City: Villeneuve d'Ascq
Contact
City: LE CHESNAY CEDEX
Website: http://www.inria.fr
Street: Domaine de Voluceau - Rocquencourt
Postal Code: 78153
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