The team seeks to advance both empirical and model-based approaches in software engineering. Objectives include managing the world's largest archive of software source code for security and developing methods for early detection and correction of design errors in software development.
🔗Team activity between 2018 and 2023
The following text has been written by the ACES team as part of the 2018-2023 periodic HCÉRES evaluation of the LTCI lab and reflects the past activities of the team on the "Software Engineering" topic.
The team's research extends into software engineering, covering both Empirical Software Engineering (EMSE), particularly in analyzing free/open source software (FOSS) artifacts, and Model-Based Systems and Software Engineering (MBSSE) for Cyber-Physical and Embedded Systems.
In EMSE, the Software Heritage (SWH) initiative, co-founded by an ACES team member priori to joining Télécom Paris, stands out by amassing the largest public collection of software source code, with over 18 billion files and 3.8 billion commits from 280 million FOSS projects ("Software Heritage: Why and How to Preserve Software Source Code", "Building the universal archive of source code"). Our work on SWH includes developing scalable indexing for vast version control system graphs ("Robust and Scalable Content-and-Structure Indexing") and releasing a large-scale open source license text variants dataset, which received the best data paper award at MSR 2022 ("A Large-scale Dataset of (Open Source) License Text Variants", "The Software Heritage License Dataset (2022 Edition)"). Additionally, we conducted studies on the diversity of public code contributors ("Worldwide Gender Differences in Public Code Contributions", "Geographic Diversity in Public Code Contributions"), recognized by the Google Award for Inclusion Research in 2022.
In MBSSE, our focus has been on rectifying design errors early in development to avoid delays, emphasizing automatic code generation from AADL models with the RAMSES platform ("AADL: A Language to Specify the Architecture of Cyber-Physical Systems"), and advancing foundational aspects of MBSSE through Multi-Paradigm Modeling (MPM) techniques ("Multi-Paradigm Modeling for Cyber-Physical Systems: A Systematic Mapping Review", "Multi-paradigm modelling for cyber--physical systems: a descriptive framework", "An ontological foundation for multi-paradigm modelling for cyber-physical systems", "An ontology for multi-paradigm modelling", "An integrated ontology for multi-paradigm modelling for cyber-physical systems"). Our efforts also cover Model Management (MoM), focusing on model consistency management and synchronization ("A benchmark of incremental model transformation tools based on an industrial case study with AADL", "Solving the instance model-view update problem in AADL", "OSATE-DIM solves the instance model-view update problem in AADL").
Moreover, we explore formal verification of multi-agent systems, addressing the undecidability of model checking with approximations and bounded memory concepts ("An abstraction-refinement framework for verifying strategic properties in multi-agent systems with imperfect information", "Approximating Perfect Recall when Model Checking Strategic Abilities: Theory and Applications"), as well as hybrid techniques and abstraction-refinement methods to reduce complexity ("Strategy RV: A Tool to Approximate ATL Model Checking under Imperfect Information and Perfect Recall", "Towards the Verification of Strategic Properties in Multi-Agent Systems with Imperfect Information", "Scalable Verification of Strategy Logic through Three-Valued Abstraction"). Our research also includes applying these methods to real-world scenarios like auctions ("Reasoning about Human-Friendly Strategies in Repeated Keyword Auctions", "Program Semantics and~Verification Technique for~AI-Centred Programs", "Automatically Verifying Expressive Epistemic Properties of Programs").