Identifying Latent Declarative Representations of Code for Assisting Repository Migration

• URL: http://arxiv.org/abs/2608.23619v1
• Date: Sat, 22 Aug 2026 08:23:14 GMT
• Title: Identifying Latent Declarative Representations of Code for Assisting Repository Migration
• Abstract summary: ADFD-Migrate approximates the latent representation with an annotated data-flow diagram (ADFD) of processes, data stores, external entities, flows, and behavioral contracts.<n>We evaluate ADFD-Migrate on f2x50, a new benchmark of 50Fortran repositories spanning 1.5k--1.6M lines of code and three complexity tiers.
• Score: 0.7763235307178853
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Legacy software repositories embed decades of domain knowledge in undocumented code, making understanding and modernization difficult. We treat a program as the implementation of an unobserved, declarative description of its computation and investigate whether making this latent declarative representation explicit improves repository-scale porting. ADFD-Migrate approximates the latent representation with an annotated data-flow diagram (ADFD) of processes, data stores, external entities, flows, and behavioral contracts. An LLM infers the source ADFD from bounded repository context, guided by static-analysis coverage checks. Dependency-aware chunking orders bounded process groups for target-language generation. Differences between the source ADFD and a statically recovered target ADFD then guide regeneration. We evaluate ADFD-Migrate on f2x50, a new benchmark of 50~Fortran repositories spanning 1.5k--1.6M lines of code and three complexity tiers, and assess the resulting ports along two dimensions: porting soundness, measured by source-oracle behavioral agreement, and porting completeness, measured by a composite migration outcome index. Against 382 curated Fortran-oracle probes, the generated Python passes 327 (85.6\%), with 40 repositories passing every attempted probe. ADFD-Migrate exposes all 382 planned behaviors as runnable targets, compared with 99 and 98 for direct and repository-context translation and 69 and 30 for the static-profile and dependency-chunking ablations. It also achieves a 93.1\% mean migration outcome index and a 17--59 percentage-point outcome-index advantage over direct translation on 47 repositories. These results suggest that an inspectable semantic bottleneck can improve the coverage and integration of repository-scale migration while enabling lower-cost generation for many repositories.

Related papers

• DenMark: Robust Semantic Watermarking for Diffusion Language Models [56.836684262732994]
  DenMark is a semantic watermarking framework for diffusion language models (DLMs)<n>It injects key-dependent signals directly into the DLM denoising process.<n>It partitions the output into fixed token regions and uses temporary rollouts as semantic lookahead.<n>For detection, DenMark uses calibrated scanning over candidate unit sizes to remain robust to boundary shifts introduced by semantic attacks.
  arXiv  Detail & Related papers  (2026-09-13T03:37:48Z)
• SeGDeP: Semantic- and Geometric-Aware Decoupled Prompts for Reasoning Segmentation [51.46828526392219]
  Reasoning segmentation converts an implicit linguistic conclusion into a precise mask.<n>Existing MLLM-segmenter interfaces either use a special trigger or compress both signals into one context.<n>We present SeGDeP, an explicit what-where interface.
  arXiv  Detail & Related papers  (2026-09-08T15:11:32Z)
• A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination [51.06539604709775]
  We propose A-SR, a self-evolving agentic framework that shifts the control unit from expression edits to role-conditioned evidence views.<n>A-SR coordinates formula discovery through routing among coordination protocols, an online evaluator-reward role policy, and state-routed process memory.
  arXiv  Detail & Related papers  (2026-08-05T14:01:10Z)
• MASTOR: A Multi-Agent Approach to Semantic Test Oracle Generation for RESTful APIs [8.248920932579876]
  Existing automated API testing approaches rely on simple checks.<n>We propose MASTOR, a Multi-Agent approach for generating Semantic Test Oracles for APIs based on implementation source code.
  arXiv  Detail & Related papers  (2026-06-09T06:35:21Z)
• RepoMirage: Probing Repository Context Reasoning in Code Agents with Perturbations [51.43574078961796]
  We introduce RepoMirage, a two-stage evaluation suite built on SWE-Bench Verified.<n>RepoMirage-Perturb applies three types of semantics-preserving repository-level perturbations, revealing a clear performance drop when correct solving requires broader context access.<n>We propose RepoAnchor, a structure-first prototype workflow that separates repository exploration from downstream problem solving.
  arXiv  Detail & Related papers  (2026-05-25T06:26:43Z)
• Code2UML: Agentic LLMs with context engineering for scalable software visualization [7.069738194755401]
  This paper introduces an agentic architecture with context engineering for automated diagram generation from source code repositories.<n>It employs a hierarchy of five specialized agents: PlannerAgent, Analyzer DiagramAgent, CorrectorAgent and DependencyAnalyzerAgent, built on the Claude Agent SDK.<n>A deterministic, importance-weighted IR compaction layer transforms full project IRs into diagram-specific views guaranteed to fit within token constraints.
  arXiv  Detail & Related papers  (2026-05-23T08:01:26Z)
• LLM-based Schema-Guided Extraction and Validation of Missing-Person Intelligence from Heterogeneous Data Sources [0.7734726150561088]
  Missing-person and child-safety investigations rely on heterogeneous case documents, including structured forms, bulletin-style posters, and narrative web profiles.<n>Variations in layout, terminology, and data quality impede rapid triage, large-scale analysis, and search-planning.<n>This paper introduces the Guardian Pack, an AI-driven parsing and normalization pipeline that transforms multi-source investigative documents into a unified, schema-compliant representation.
  arXiv  Detail & Related papers  (2026-04-08T01:35:56Z)
• DiscoUQ: Structured Disagreement Analysis for Uncertainty Quantification in LLM Agent Ensembles [5.647839536820347]
  We introduce DiscoUQ, a framework that extracts and leverages the structure of inter-agent disagreement to produce well-calibrated confidence estimates.<n>DiscoUQ-LLM achieves an average AUROC of 0.802, outperforming the best baseline.<n>The learned features generalize across benchmarks with near-zero performance degradation.
  arXiv  Detail & Related papers  (2026-03-21T23:24:12Z)
• Diffploit: Facilitating Cross-Version Exploit Migration for Open Source Library Vulnerabilities [13.559398564795048]
  We propose Diffploit, an iterative, diff-driven exploit migration method structured around two key modules.<n>We evaluate Diffploit on a large-scale dataset containing 102 Java CVEs and 689 version-migration tasks across 79 libraries.<n>It successfully migrates 84.2% exploits, outperforming the change-aware test repair tool TARGET by 52.0% and the rule-based tool in IDEA by 61.6%.
  arXiv  Detail & Related papers  (2025-11-17T04:06:01Z)
• Eigen-1: Adaptive Multi-Agent Refinement with Monitor-Based RAG for Scientific Reasoning [53.45095336430027]
  We develop a unified framework that combines implicit retrieval and structured collaboration.<n>On Humanity's Last Exam (HLE) Bio/Chem Gold, our framework achieves 48.3% accuracy.<n>Results on SuperGPQA and TRQA confirm robustness across domains.
  arXiv  Detail & Related papers  (2025-09-25T14:05:55Z)
• RustRepoTrans: Repository-level Code Translation Benchmark Targeting Rust [50.65321080814249]
  RustRepoTrans is the first repository-level context code translation benchmark targeting incremental translation.<n>We evaluate seven representative LLMs, analyzing their errors to assess limitations in complex translation scenarios.
  arXiv  Detail & Related papers  (2024-11-21T10:00:52Z)
• ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
  ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks. To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
  arXiv  Detail & Related papers  (2023-11-16T12:03:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.