arXiv subjects

Théo Matricon

Publications and source records attributed to Théo Matricon.

10 recordsLinked to original sources

arXiv2026

Agentic Very Much! Adoption of Coding Agent in New GitHub Projects

In previous work, we investigated the adoption of coding agents in GitHub projects, finding that it was very significant. This study follows this line of work, but analyses new projects, that were created after the previous study. In this new sample, we find that the adoption of coding agents is more than twice as high. We also find that the adoption is significantly more intensive, as the proportion of AI-assisted commits is sensibly higher, despite strong signs that we do not detect all of it.

cs.SE↗

arXiv2026

Agentic Much? Adoption of Coding Agents on GitHub

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.

cs.SE↗

arXiv2026

Promises, Perils, and (Timely) Heuristics for Mining Coding Agent Activity

In 2025, coding agents have seen a very rapid adoption. Coding agents leverage Large Language Models (LLMs) in ways that are markedly different from LLM-based code completion, making their study critical. Moreover, unlike LLM-based completion, coding agents leave visible traces in software repositories, enabling the use of MSR techniques to study their impact on SE practices. This paper documents the promises, perils, and heuristics that we have gathered from studying coding agent activity on GitHub.

cs.SE↗

arXiv2026

LTL$_f$ Learning Meets Boolean Set Cover

Learning formulas in Linear Temporal Logic (LTLf) from finite traces is a fundamental research problem which has found applications in artificial intelligence, software engineering, programming languages, formal methods, control of cyber-physical systems, and robotics. We implement a new CPU tool called Bolt improving over the state of the art by learning formulas more than 100x faster over 70% of the benchmarks, with smaller or equal formulas in 98% of the cases. Our key insight is to leverage a problem called Boolean Set Cover as a subroutine to combine existing formulas using Boolean connectives. Thanks to the Boolean Set Cover component, our approach offers a novel trade-off between efficiency and formula size.

cs.AI↗

arXiv2025

Efficiently Ranking Software Variants with Minimal Benchmarks

Benchmarking is a common practice in software engineering to assess the qualities and performance of software variants, coming from multiple competing systems or from configurations of the same system. Benchmarks are used notably to compare and understand variant performance, fine-tune software, detect regressions, or design new software systems. The execution of benchmarks to get a complete picture of software variants is highly costly in terms of computational resources and time. In this paper, we propose a novel approach for reducing benchmarks while maintaining stable rankings, using test suite optimization techniques. That is, we remove instances from the benchmarks while trying to keep the same rankings of the variants on all tests. Our method, BISection Sampling, BISS, strategically retains the most critical tests and applies a novel divide-and-conquer approach to efficiently sample among relevant remaining tests. We experiment with datasets and use cases from LLM leaderboards, SAT competitions, and configurable systems for performance modeling. Our results show that our method outperforms baselines even when operating on a subset of variants. Using BISS, we reduce the computational cost of the benchmarks on average to 44% and on more than half the benchmarks by up to 99% without loss in ranking stability.

cs.SE↗

arXiv2025

Prompting for Performance: Exploring LLMs for Configuring Software

Software systems usually provide numerous configuration options that can affect performance metrics such as execution time, memory usage, binary size, or bitrate. On the one hand, making informed decisions is challenging and requires domain expertise in options and their combinations. On the other hand, machine learning techniques can search vast configuration spaces, but with a high computational cost, since concrete executions of numerous configurations are required. In this exploratory study, we investigate whether large language models (LLMs) can assist in performance-oriented software configuration through prompts. We evaluate several LLMs on tasks including identifying relevant options, ranking configurations, and recommending performant configurations across various configurable systems, such as compilers, video encoders, and SAT solvers. Our preliminary results reveal both positive abilities and notable limitations: depending on the task and systems, LLMs can well align with expert knowledge, whereas hallucinations or superficial reasoning can emerge in other cases. These findings represent a first step toward systematic evaluations and the design of LLM-based solutions to assist with software configuration.

cs.SE↗

arXiv2025

Programmatic Reinforcement Learning: Navigating Gridworlds

The field of reinforcement learning (RL) is concerned with algorithms for learning optimal policies in unknown stochastic environments. Programmatic RL studies representations of policies as programs, meaning involving higher order constructs such as control loops. Despite attracting a lot of attention at the intersection of the machine learning and formal methods communities, very little is known on the theoretical front about programmatic RL: what are good classes of programmatic policies? How large are optimal programmatic policies? How can we learn them? The goal of this paper is to give first answers to these questions, initiating a theoretical study of programmatic RL. Considering a class of gridworld environments, we define a class of programmatic policies. Our main contributions are to place upper bounds on the size of optimal programmatic policies, and to construct an algorithm for synthesizing them. These theoretical findings are complemented by a prototype implementation of the algorithm.

cs.LG↗

arXiv2024

EcoSearch: A Constant-Delay Best-First Search Algorithm for Program Synthesis

Many approaches to program synthesis perform a combinatorial search within a large space of programs to find one that satisfies a given specification. To tame the search space blowup, previous works introduced probabilistic and neural approaches to guide this combinatorial search by inducing heuristic cost functions. Best-first search algorithms ensure to search in the exact order induced by the cost function, significantly reducing the portion of the program space to be explored. We present a new best-first search algorithm called EcoSearch, which is the first constant-delay algorithm for pre-generation cost function: the amount of compute required between outputting two programs is constant, and in particular does not increase over time. This key property yields important speedups: we observe that EcoSearch outperforms its predecessors on two classic domains.

cs.LG↗

arXiv2023

WikiCoder: Learning to Write Knowledge-Powered Code

We tackle the problem of automatic generation of computer programs from a few pairs of input-output examples. The starting point of this work is the observation that in many applications a solution program must use external knowledge not present in the examples: we call such programs knowledge-powered since they can refer to information collected from a knowledge graph such as Wikipedia. This paper makes a first step towards knowledge-powered program synthesis. We present WikiCoder, a system building upon state of the art machine-learned program synthesizers and integrating knowledge graphs. We evaluate it to show its wide applicability over different domains and discuss its limitations. WikiCoder solves tasks that no program synthesizers were able to solve before thanks to the use of knowledge graphs, while integrating with recent developments in the field to operate at scale.

cs.LG↗

arXiv2021

Scaling Neural Program Synthesis with Distribution-based Search

We consider the problem of automatically constructing computer programs from input-output examples. We investigate how to augment probabilistic and neural program synthesis methods with new search algorithms, proposing a framework called distribution-based search. Within this framework, we introduce two new search algorithms: Heap Search, an enumerative method, and SQRT Sampling, a probabilistic method. We prove certain optimality guarantees for both methods, show how they integrate with probabilistic and neural techniques, and demonstrate how they can operate at scale across parallel compute environments. Collectively these findings offer theoretical and applied studies of search algorithms for program synthesis that integrate with recent developments in machine-learned program synthesizers.

cs.LG↗