DepRepair: LLM-Based Source-Code Repair for Dependency Breaking Changes

• URL: http://arxiv.org/abs/2607.17957v1
• Date: Mon, 20 Jul 2026 13:58:13 GMT
• Title: DepRepair: LLM-Based Source-Code Repair for Dependency Breaking Changes
• Abstract summary: DepRepair is a single-call LLM approach that grounds repair in structured upstream evidence through three components.<n>It attains the highest executable pass rate on each backbone, achieving 89.5% with GPT-5.5 and 82.1% with Claude Opus 4.6.
• Score: 26.444574129479463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern software projects depend on numerous third-party libraries, whose updates often introduce breaking changes. Adapting consumer code to such changes remains labor-intensive and error-prone. Existing work either characterizes dependency breaking changes without producing a verified consumer-side patch, or studies automated repair only in settings where the failure and repair context are contained within the target repository. However, dependency breaking changes violate this assumption: the decisive repair evidence lies upstream in release notes and API diffs, and no failing test localizes where the consumer breaks, leaving the repair under-informed. To study this cross-repository problem on real data, we introduce DepBench, a benchmark of 95 real-world dependency-update instances across four ecosystems, each paired with a Docker-based executable oracle that runs the consumer's own tests. To address these challenges, we propose DepRepair, a single-call LLM approach that grounds repair in structured upstream evidence through three components: an evidence filter that distills relevant upstream changes, a usage locator that identifies affected consumer sites, and a subcategory-aware guide that tailors repairs to the breaking-change type. Evaluated on DepBench, DepRepair attains the highest executable pass rate on each backbone, achieving 89.5% with GPT-5.5 and 82.1% with Claude Opus 4.6. We further find that raw upstream evidence reduces LLM and agent pass rates by 7--23 percentage points, whereas structured evidence consistently improves them.

Related papers

• SWE-Gate: Passing Functional Tests Is Not Enough for Software Engineering Agents [63.65682461568621]
  We introduce SWE-Gate, a repository-level benchmark for software engineering agents that explicitly evaluates review constraint compliance alongside functional correctness.<n>SWE-Gate derives review constraints from real pull request review comments and synthesizes repository-level repair instances around these constraints.<n>Each instance provides separate functional and constraint tests, together with non-compliant and gold patches, enabling explicit separation between issue resolution capability and review constraint compliance.
  arXiv  Detail & Related papers  (2026-09-03T17:53:34Z)
• Independent Patch Verification for Coding Agents with a Bidirectional Reconstruct-and-Verify Framework [49.16055123488827]
  coding agents powered by large language models can now generate code patches directly from bug reports.<n>No mechanism independently verifies whether it truly resolves the reported problem.<n>We propose RETRACE, a training-free post-generation verification framework.
  arXiv  Detail & Related papers  (2026-08-09T22:59:13Z)
• Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes [55.114939648705395]
  Large language models (LLMs) have made automated program repair increasingly practical for real-world bugs.<n>Bug reproduction tests (BRTs) help close this gap by turning a bug report into an executable, bug-specific signal.<n>We propose CoHarden, a co-generation framework that uses the Lax signal as an in-loop convergence criterion.
  arXiv  Detail & Related papers  (2026-07-22T07:30:07Z)
• PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents [62.36339598951133]
  We present PhoenixRepair, a multi-agent framework that explores multiple candidate edit locations and generates better patches.<n>PhoenixRepair achieves the largest relative improvement of 7.8% over SWE-agent under DeepSeek-V3.1, and attains the highest resolved rate of 76.0% Pass@1 under MiniMax-M2.5.
  arXiv  Detail & Related papers  (2026-07-21T08:49:30Z)
• EviACT: An Evidence-to-Action Framework for Agentic Program Repair [21.78498442884825]
  EviACT is an agentic APR framework that coordinates three evidence-driven guardrails across repair stages.<n>It improves resolve rate over the strongest reported comparable baselines by 1.6-6.0 percentage points.<n>It shows 70.1-88.6% lower reported per-bug API cost where baseline costs are available.
  arXiv  Detail & Related papers  (2026-05-26T16:17:47Z)
• MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repair [10.709112246848058]
  Modern software ecosystems face a rapidly growing number of disclosed vulnerabilities.<n>Most existing systems treat vulnerability repair as a single generation step over the currently visible code context.<n>We present MemRepair, a memoryaugmented agentic framework that formulates vulnerability repair as an iterative, experience-driven process.
  arXiv  Detail & Related papers  (2026-05-17T13:29:46Z)
• EvidenT: An Evidence-Preserving Framework for Iterative System-Level Package Repair [26.445772656438752]
  We propose EvidenT, an evidence-preserving repair framework that decouples evidence management from tool execution.<n>We evaluate EvidenT on 219 real-world RISC-V package build failures.<n>Preliminary experiments achieve success rates of 41.77% on aarch64 and 46.99% on x86_64, demonstrating robustness across diverse hardware ecosystems.
  arXiv  Detail & Related papers  (2026-05-09T02:29:57Z)
• QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization [50.722636027998]
  Large Language Models (LLMs) achieve strong program repair performance but often suffer from over-editing.<n>We propose PRepair, a framework that mitigates over-editing and improves repair accuracy.
  arXiv  Detail & Related papers  (2026-04-07T14:56:38Z)
• RepoRepair: Leveraging Code Documentation for Repository-Level Automated Program Repair [30.23781155493087]
  We propose RepoRepair, a novel documentation-enhanced approach for repository-level fault localization and program repair.<n>Our core insight is to leverage LLMs to generate hierarchical code documentation (from functions to files) for code repositories.<n>RepoRepair first employs a text-based LLM to generate file/function-level code documentation for repositories, which serves as auxiliary knowledge to guide fault localization.
  arXiv  Detail & Related papers  (2026-03-01T11:06:24Z)
• Outrunning LLM Cutoffs: A Live Kernel Crash Resolution Benchmark for All [57.23434868678603]
  Live-kBench is an evaluation framework for self-evolving benchmarks that scrapes and evaluates agents on freshly discovered kernel bugs.<n> kEnv is an agent-agnostic crash-resolution environment for kernel compilation, execution, and feedback.<n>Using kEnv, we benchmark three state-of-the-art agents, showing that they resolve 74% of crashes on the first attempt.
  arXiv  Detail & Related papers  (2026-02-02T19:06:15Z)
• Outcome-Conditioned Reasoning Distillation for Resolving Software Issues [49.16055123488827]
  We present an Outcome-Conditioned Reasoning Distillation(O-CRD) framework that uses resolved in-repository issues with verified patches as supervision.<n>Starting from a historical fix, the method reconstructs a stage-wise repair trace backward from the verified outcome.<n>On SWE-Bench Lite, this approach increases Pass@1 by 10.4% with GPT-4o, 8.6% with DeepSeek-V3, and 10.3% with GPT-5.
  arXiv  Detail & Related papers  (2026-01-30T18:25:39Z)
• 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)
• Specification-Guided Repair of Arithmetic Errors in Dafny Programs using LLMs [79.74676890436174]
  We present an APR tool for Dafny that uses formal specifications as oracles for fault localization and repair.<n>We localize faults through a series of steps, which include using Hoare logic to determine the state of each statement within the program.<n>Our tool achieves 89.6% fault localization coverage and GPT-4o mini yields the highest repair success rate of 74.18%.
  arXiv  Detail & Related papers  (2025-07-04T15:36:12Z)

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.