Publications

2026

• A Decade of Software Reproducibility in the Nix Package Ecosystem
  
  • Malka Julien
  • Zacchiroli Stefano
  • Zimmermann Théo
  Empirical Software Engineering, Springer Verlag, 2026. <div><p>We report a large-scale empirical study of two aspects of software reproducibility-rebuildability and bitwise reproducibility-in the Nix package ecosystem. Using 29 evenly spaced historical snapshots over a decade of history of the nixpkgs repository (2015-2024) we attempted to rebuild and bitwise-compare the build outputs of tens of thousands of packages per snapshot. Our experiment produced a dataset of build metadata and logs for 1 321 000 package builds and preserved 166 523 diffoscopes for unreproducible outputs.</p><p>We find that functional package management enables extremely high rebuildability over time (near-universal ability to reconstitute historical build environments and rebuild software packages), while bitwise reproducibility has steadily improved and reaches a high point in recent years (up to 93% in 2024). Early years show substantially lower bitwise reproducibility, indicating that functional package management alone does not guarantee bitwiseidentical outputs, and that the observed high level of bitwise reproducibility is not solely due to the package management approach. Common causes of unreproducibility, both in the rebuildability and bitwise reproducibility dimensions, include management of dates in build and test processes; we quantify their prevalence and other common causes using manual analysis of logs of rebuild failures and automated analysis of diffoscopes.</p></div> (10.1007/s10664-026-10924-1)
  DOI : 10.1007/s10664-026-10924-1
• Convergence rate for the coupon collector's problem with Stein's method
  
  • Costacèque Bruno
  • Decreusefond Laurent
  Stochastic Processes and their Applications, Elsevier, 2026. The functional characterization of a measure, an essential but delicate aspect of Stein's method, is shown to be accessible for stable probability distributions on convex cones. This notion encompasses the usual stable distributions \textit{e.g.} Gaussian, Pareto, \textit{etc.} but also the max-stable distributions: Weibull, Gumbel and Fréchet. We use the definition of max-stability to define a Markov process whose invariant measure is the stable measure of interest. In this paper, we focus on the Gumbel distribution and show how this construction can be applied to estimate the rate of convergence in the classical coupon collector's problem. (10.48550/arXiv.2501.06535)
  DOI : 10.48550/arXiv.2501.06535
• Applying Morphological Operations to Subsets of Points for the Discovery of Repeated Musical Patterns and Their Variations
  
  • Lascabettes Paul
  • Quaetaert Nils
  • Daniel Mathys
  • Andreatta Moreno
  • Bloch Isabelle
  Journal of Mathematical Imaging and Vision, Springer Verlag, 2026. This article deals with the discovery of repeated patterns and their variations in a discrete representation of musical data. This task consists in identifying repetitions within a set of points in R2 , where each point represents a musical note whose coordinates are its onset and its pitch value. A common approach is to compute all the possible translations between points in order to discover repeated musical patterns. In this paper, we propose to start from specific subsets of points and to complete them by using morphological operations to form repeated patterns. Moreover, these operations can be extended to discover pattern variations given a particular approximation. This method not only reveals certain variations of the given subset of points, but also adds specific points to it despite the fact that they were not initially present. We apply our approach to the collection of 24 fugues from the first book of Bach's Well-Tempered Clavier. In this particular case, we consider the first m points as the subset to be completed by the morphological operators. We demonstrate that specific values of m enable the discovery of the subject and its occurrences, whereas the smallest values identify truncated versions of it. We compare our approach with previous work on the analysis of Bach's fugues and illustrate the results for different values of m with graphical representations including both exact and approximate repetitions. (10.1007/s10851-026-01301-0)
  DOI : 10.1007/s10851-026-01301-0
• Agentic Much? Adoption of Coding Agents on GitHub
  
  • Robbes Romain
  • Matricon Théo
  • Degueule Thomas
  • Hora Andre
  • Zacchiroli Stefano
  ACM Transactions on Software Engineering and Methodology, Association for Computing Machinery, 2026, pp.1-43. In the first half of 2025, coding agents have emerged as a category of development tools that have very quickly transitioned to the practice. Unlike ''traditional'' code completion LLMs such as Copilot, agents like Cursor, Claude Code, or Codex operate with high degrees of autonomy, up to generating complete pull requests starting from a developer-provided task description. This new mode of operation is poised to change the landscape in an even larger way than code completion LLMs did, making the need to study their impact critical. Also, unlike traditional LLMs, coding agents tend to leave more explicit traces in software engineering artifacts, such as co-authoring commits or pull requests. We leverage these traces to present the first large-scale study (128,018 projects) of the adoption of coding agents on GitHub, finding an estimated adoption rate of 22.20%--28.66%, which is very high for a technology only a few months old--and increasing. We carry out an in-depth study of the adopters we identified, finding that adoption is broad: it spans the entire spectrum of project maturity; it includes established organizations; and it concerns diverse programming languages or project topics. At the commit level, we find that commits assisted by coding agents are larger than commits only authored by human developers, and have a large proportion of features and bug fixes. These findings highlight the need for further investigation into the practical use of coding agents. (10.1145/3822180)
  DOI : 10.1145/3822180
• Contrastive Learning under Noisy Temporal Self-Supervision for Colonoscopy Videos
  
  • Parolari Luca
  • Gori Pietro
  • Ballan Lamberto
  • Biffi Carlo
  • Folgoc Loic Le
  , 2026. Learning robust representations of polyp tracklets is key to enabling multiple AI-assisted colonoscopy applications, from polyp characterization to automated reporting and retrieval. Supervised contrastive learning is an effective approach for learning such representations, but it typically relies on correct positive and negative definitions. Collecting these labels requires linking tracklets that depict the same underlying polyp entity throughout the video, which is costly and demands specialized clinical expertise. In this work, we leverage the sequential workflow of colonoscopy procedures to derive self-supervised associations from temporal structure. Since temporally derived associations are not guaranteed to be correct, we introduce a noise-aware contrastive loss to account for noisy associations. We demonstrate the effectiveness of the learned representations across multiple downstream tasks, including polyp retrieval and re-identification, size estimation, and histology classification. Our method outperforms prior self-supervised and supervised baselines, and matches or exceeds recent foundation models across all tasks, using a lightweight encoder trained on only 27 videos. Code is available at github.com/lparolari/ntssl.
• Event Detection and Localization Using a Multiple-Input-Multiple-Output Distributed Fiber Sensor with Birefringence and Phase Estimation
  
  • Prato Diane
  • Sheramin Mehran Mokthari
  • Gabet Renaud
  • Awwad Élie
  , 2026, pp.17. We present a numerical modeling approach for a Multiple-Input-Multiple-Output Distributed Acoustic Sensing (MIMO-DAS) system, incorporating both phase and polarization dynamics. We demonstrate the ability of the proposed architecture to provide a distributed estimation of the fiber effective linear birefringence magnitude and of the optical phase of the backscattered signal to detect and localize dynamic events with a mean spatial resolution of 1.3m. This allows for increased sensitivity to disturbances that act transversely on the fiber, since the estimated fiber effective birefringence magnitude will be responsive to perturbations that break cylindrical symmetry (anisotropic transverse strains), while the phase common to both polarization tributaries will show great sensitivity to pure longitudinal strains. Analyzing these two quantities is therefore of use to discriminate between purely axisymmetric strains and anistropic strains. (10.1117/12.3104760)
  DOI : 10.1117/12.3104760
• SCALMU: Synthetically-trained Coupling of Adaptive Learned Multiplicative Updates for Hyperspectral-Multispectral Fusion
  
  • Xu Xinxin
  • Gousseau Yann
  • Kervazo Christophe
  • Ladjal Saïd
  IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2026. HyperSpectral-MultiSpectral Image (HSI-MSI) fusion aims to recover a high-resolution hyperspectral image from a low-resolution HSI and a high-resolution MSI. Classical methods such as Coupled Nonnegative Matrix Factorization (CNMF) benefit from a strong physical interpretability but suffer from inferior results compared to their deep-learning counterparts. To address this limitation, we propose SCALMU (Synthetically-trained Coupling of Adaptive Learned Multiplicative Updates), a novel blind unrolled neural network architecture that integrates adaptive learnable matrices within the classical framework of CNMF multiplicative updates, improving its results. Due to its architectural proximity with CNMF, the resulting algorithm preserves physical interpretability and nonnegativity constraints. To overcome the scarcity of supervised training data, we generate a synthetic HSI-MSI dataset using the dead leaves model and train SCALMU end-to-end under synthetic supervision. Experiments on several datasets show that SCALMU outperforms state-of-the-art methods and highlights the potential of blind fusion trained with synthetic data. The code is available at \url{https://github.com/xinxinxu99/SCALMU.git} (10.1109/TGRS.2026.3712501)
  DOI : 10.1109/TGRS.2026.3712501