Tables & Resources: Knowledge Explorer for Automated Issue Resolution¶
This page is the public query hub for the reviewed corpus. Instead of mirroring the survey chapter-by-chapter, it is organized around four reader tasks: understanding the field quickly, finding papers efficiently, comparing methods meaningfully, and locating reusable resources.
Quick Insights¶
These summary rows are intended to answer the most common first-pass questions a reader has when arriving at the survey site.
| Question | Answer | Evidence |
|---|---|---|
| Most dominant knowledge type | Existing Code Knowledge remains the universal core of current issue-resolution systems. | 128 / 133 papers |
| Fastest-rising procedural factor | Development Tool Knowledge and execution-grounded interaction increasingly dominate post-2025 systems. | 49 papers in latest-half slice |
| Main shift in recent work | The field is moving from static code retrieval toward feedback-driven, process-aware, and history-aware agent workflows. | Development Tool Knowledge + Existing Code Knowledge is the top co-occurrence |
| Most underexplored knowledge | Domain, language, and architecture knowledge remain sparse despite their importance in real-world repair settings. | Each remains below 10% of the included corpus |
| Workflow hotspots | Localization, patch generation, and patch validation absorb the largest share of knowledge-intensive systems. | These stages dominate the corpus-wide usage-stage distribution |
Explore the Field¶
Knowledge Coverage Summary¶
This table compresses the survey's core coding dimensions into a single field map, showing how each knowledge type is typically sourced, represented, used, and applied.
| Knowledge Type | Share | Main Source | Main Stage | Common Representation | Common Usage | Trend | Key Papers |
|---|---|---|---|---|---|---|---|
| Existing Code Knowledge | 96.2% | Codebase | Localization | Unstructured Text | Tool Invocation | Stable core |
GraphLocator: Graph-guided Causal Reasoning for Issue Localization
Agentic Rubrics as Contextual Verifiers for SWE Agents
Dars: Dynamic action re-sampling to enhance coding agent performance by adaptive tree traversal
|
| Development Tool Knowledge | 77.4% | Codebase | Localization | Unstructured Text | Tool Invocation | Rising |
Otter: Generating tests from issues to validate swe patches
SWE-Replay: Efficient Test-Time Scaling for Software Engineering Agents
|
| Experiential Knowledge | 51.1% | Dynamic Execution Results | Localization | Unstructured Text | Tool Invocation | Rising fast |
Swe-exp: Experience-driven software issue resolution
SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training
|
| Repository Evolution Knowledge | 32.3% | Codebase | Localization | Unstructured Text | Direct Prompt | Rising |
BLAZE: Cross-language and cross-project bug localization via dynamic chunking and hard example learning
Closing the Loop: Universal Repository Representation with RPG-Encoder
SIADAFIX: issue description response for adaptive program repair
|
| Design Architecture Knowledge | 9.8% | Codebase | Patch Generation | Unstructured Text | Direct Prompt | Emerging |
BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?
Architecture-Aware Multi-Design Generation for Repository-Level Feature Addition
|
| Technology Stack Knowledge | 3.0% | Codebase | Localization | Unstructured Text | Tool Invocation | Sparse but important |
BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?
ArkEval: Benchmarking and Evaluating Automated CodeRepair for ArkTS
|
| Programming Language Knowledge | 2.3% | Codebase | Localization | Unstructured Text | Direct Prompt | Sparse but important |
Evaluating and Improving Automated Repository-Level Rust Issue Resolution with LLM-based Agents
ArkEval: Benchmarking and Evaluating Automated CodeRepair for ArkTS
|
| Domain-specific Knowledge | 0.8% | Codebase | Environment Building | Unstructured Text | Direct Prompt | Highly underexplored |
BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?
|
Underexplored Opportunities¶
These rows translate low-coverage knowledge dimensions into concrete research opportunities rather than leaving them as isolated counts.
| Gap | Current Coverage | Why It Matters | Candidate Papers | Opportunity |
|---|---|---|---|---|
| Domain-specific knowledge | 0.8% of papers | Real-world issue resolution often depends on domain semantics that generic repository reasoning cannot recover from code alone. | BeyondSWE | High |
| Programming-language knowledge | 2.3% of papers | Multilingual and low-resource repair settings need stronger language-semantic grounding than current Python-heavy pipelines provide. | ArkEval Rust issue resolution SWE-Rank+ |
High |
| Design / architecture knowledge | 9.8% of papers | Passing tests does not guarantee alignment with repository-wide constraints, module responsibilities, or intended architecture. | RPG-Encoder Agentic Rubrics ToM-SWE |
Very high |
| Document-grounded reasoning | 11 document-linked papers | Specifications, migration notes, and framework guidance are increasingly necessary in non-trivial issue settings. | BeyondSWE ArkEval Agentic Rubrics |
Medium-high |
Reading Entry Table¶
If a reader enters the field from a specific angle, this table gives a better starting path than a raw tag directory.
| If You Care About... | Start With | Why |
|---|---|---|
| Execution feedback | Swe-agent: Agent-computer interfaces enable automated software engineering R2e-gym: Procedural environments and hybrid verifiers for scaling open-weights swe agents Otter: Generating tests from issues to validate swe patches |
These systems make tool interaction, runtime evidence, and executable validation central to the repair loop. |
| Memory / experience | EXPEREPAIR: Dual-Memory Enhanced LLM-based Repository-Level Program Repair Swe-exp: Experience-driven software issue resolution Reasoningbank: Scaling agent self-evolving with reasoning memory |
They show how prior trajectories, reflections, and reusable strategy memory can guide future issue resolution. |
| Repository history | KGCompass: Knowledge Graph Enhanced Repository-Level Software Repair SweRank: Software Issue Localization with Code Ranking Improving Code Localization with Repository Memory |
These works foreground commits, issues, historical fixes, and repository memory as explicit retrieval signals. |
| Architecture-aware repair | Closing the Loop: Universal Repository Representation with RPG-Encoder Agentic Rubrics as Contextual Verifiers for SWE Agents Tom-swe: User mental modeling for software engineering agents |
They expose how design intent and global repository constraints enter planning, patching, and validation. |
| Benchmark / training resources | SWE-Factory: Your Automated Factory for Issue Resolution Training Data and Evaluation Benchmarks Training software engineering agents and verifiers with swe-gym R2e-gym: Procedural environments and hybrid verifiers for scaling open-weights swe agents |
These papers are the best entry points if the goal is to build training environments, evaluation infrastructure, or reusable baselines. |
Find Papers¶
Knowledge Taxonomy Explorer¶
The taxonomy explorer remains as a fast navigation entry point, but its role here is to drive filtering rather than duplicate the whole survey narrative.
L1 Background Knowledge¶
Programming Language Knowledge¶
3 papers / 2.3%This category captures language-level constraints that a repository-grounded agent cannot ignore, including syntax, type systems, runtime semantics, and idiomatic repair patterns. In the survey corpus, it appears most clearly when issue resolution must generalize across languages or obey language-specific rules such as Rust ownership or ArkTS compilation constraints.
Domain-specific Knowledge¶
1 papers / 0.8%This category covers application semantics that live outside generic software engineering knowledge, such as scientific concepts, platform-specific business rules, or field-specific constraints. Although rare in the current corpus, it becomes essential once issue resolution moves beyond standard single-repository bug fixing into tasks where correct patches depend on understanding the target domain itself.
Technology Stack Knowledge¶
4 papers / 3.0%This category refers to libraries, frameworks, APIs, platform conventions, and dependency ecosystems that shape what counts as a compatible repair. It is especially important in migration, compatibility, and platform-constrained tasks, where an agent must reason about external components rather than only the repository's local implementation.
L2 Repository Knowledge¶
Existing Code Knowledge¶
128 papers / 96.2%This is the dominant knowledge type in the survey and forms the core of most issue-resolution systems. It includes concrete repository-native artifacts such as files, classes, functions, call sites, dependency relations, and local implementation context that agents rely on for localization, editing, and validation.
Design Architecture Knowledge¶
13 papers / 9.8%This category captures higher-level repository intent: architectural decomposition, component responsibilities, design constraints, and cross-module coordination logic. It becomes important when systems must produce patches that preserve global structure, satisfy implicit project conventions, or align edits with intended repository organization rather than only local code correctness.
Repository Evolution Knowledge¶
43 papers / 32.3%This category covers the repository's historical memory, including issues, pull requests, commits, prior patches, and longer development-process traces. It allows systems to retrieve precedents, infer repair patterns, and ground current decisions in how the project has changed over time.
L3 Procedural Knowledge¶
Development Tool Knowledge¶
103 papers / 77.4%This category describes operational knowledge about tools, interfaces, environments, and execution feedback channels used during issue resolution. It includes how agents search, run tests, interact with shells and sandboxes, interpret validation results, and orchestrate tool-assisted workflows across the repair loop.
Experiential Knowledge¶
68 papers / 51.1%This category focuses on reusable experience distilled from prior trajectories, failures, reflections, and successful repair behaviors. In the survey, it appears both as explicit memory structures and as training-time data that teaches agents how to plan, retrieve, edit, and validate more effectively over time.
Preset Filters¶
These presets expose higher-level reading intentions directly, so readers do not need to build every query manually from raw dropdowns.
Master Paper Table¶
The controls below support shareable prefilters through the page URL, so links from Home or preset cards can open this table already narrowed to a knowledge type, source, stage, or method family.
Code = Existing Code Knowledge
Tool = Development Tool Knowledge
History = Repository Evolution Knowledge
Memory = Experiential Knowledge
Arch = Design Architecture Knowledge
Lang = Programming Language Knowledge
Stack = Technology Stack Knowledge
Domain = Domain-specific Knowledge
Showing 133 of 133 papers.
| Paper | Year | Venue | Focus | Knowledge Profile | Method Style | Benchmark / Setting | Link |
|---|---|---|---|---|---|---|---|
| 2026 | arXiv preprint arXiv:2603.03194 | Validation / testing | Tool-centric | BeyondSWE | arXiv:2603.03194 | ||
| 2026 | arXiv preprint arXiv:2603.03823 | Benchmark / infrastructure | Agentic | — | arXiv:2603.03823 | ||
| 2026 | arXiv preprint arXiv:2601.22129 | Validation / testing | Tool-centric | — | arXiv:2601.22129 | ||
| 2026 | arXiv preprint arXiv:2602.22124 | Training / post-training | Training-based / RL | — | arXiv:2602.22124 | ||
| 2026 | arXiv preprint arXiv:2603.01814 | Architecture / constraint-aware | Graph-based | — | arXiv:2603.01814 | ||
| 2026 | arXiv preprint arXiv:2603.22048 | Validation / testing | Tool-centric | — | arXiv:2603.22048 | ||
| 2026 | arXiv preprint arXiv:2602.02084 | History-aware reasoning | Graph-based | — | arXiv:2602.02084 | ||
| 2026 | arXiv preprint arXiv:2601.04171 | Training / post-training | Training-based / RL | — | arXiv:2601.04171 | ||
| 2026 | arXiv preprint arXiv:2602.03411 | Training / post-training | Training-based / RL | — | arXiv:2602.03411 | ||
| 2026 | arXiv preprint arXiv:2601.13713 | History-aware reasoning | Training-based / RL | — | arXiv:2601.13713 | ||
| 2026 | arXiv preprint arXiv:2601.01426 | History-aware reasoning | Training-based / RL | — | arXiv:2601.01426 | ||
| 2026 | arXiv preprint arXiv:2604.24212 | Localization | Tool-centric | — | arXiv:2604.24212 | ||
| 2026 | arXiv preprint arXiv:2602.22764 | Benchmark / infrastructure | Tool-centric | Language-specific | arXiv:2602.22764 | ||
| 2026 | arXiv preprint arXiv:2602.08866 | Benchmark / infrastructure | Tool-centric | ArkTS / HarmonyOS | arXiv:2602.08866 | ||
| 2026 | arXiv preprint arXiv:2604.05955 | Benchmark / infrastructure | Pipeline | — | arXiv:2604.05955 | ||
| 2026 | arXiv preprint arXiv:2602.11210 | Training / post-training | Training-based / RL | Evaluation / infra | arXiv:2602.11210 | ||
| 2025 | arXiv preprint arXiv:2503.14269 | Training / post-training | Graph-based | — | arXiv:2503.14269 | ||
| 2025 | arXiv preprint arXiv:2502.05368 | History-aware reasoning | Tool-centric | — | arXiv:2502.05368 | ||
| 2025 | arXiv preprint arXiv:2510.16059 | History-aware reasoning | Training-based / RL | — | arXiv:2510.16059 | ||
| 2025 | IEEE Transactions on Software Engineering | History-aware reasoning | Graph-based | — | Scholar | ||
| 2025 | arXiv preprint arXiv:2502.15292 | History-aware reasoning | Graph-based | — | arXiv:2502.15292 | ||
| 2025 | arXiv preprint arXiv:2503.09089 | Training / post-training | Graph-based | — | arXiv:2503.09089 | ||
| 2025 | arXiv preprint arXiv:2510.18270 | Benchmark / infrastructure | Tool-centric | — | arXiv:2510.18270 | ||
| 2025 | arXiv preprint arXiv:2507.19942 | Validation / testing | Graph-based | Language-specific | arXiv:2507.19942 | ||
| 2025 | arXiv preprint arXiv:2507.23361 | Memory / experience | Tool-centric | — | arXiv:2507.23361 | ||
| 2025 | arXiv preprint arXiv:2510.02387 | History-aware reasoning | Training-based / RL | — | arXiv:2510.02387 | ||
| 2025 | arXiv preprint arXiv:2506.11425 | Training / post-training | Training-based / RL | — | arXiv:2506.11425 | ||
| 2025 | arXiv preprint arXiv:2509.25873 | Validation / testing | Tool-centric | — | arXiv:2509.25873 | ||
| 2025 | arXiv preprint arXiv:2501.14723 | Validation / testing | Tool-centric | — | arXiv:2501.14723 | ||
| 2025 | arXiv preprint arXiv:2509.02360 | Validation / testing | Tool-centric | — | arXiv:2509.02360 | ||
| 2025 | arXiv preprint arXiv:2507.23370 | Validation / testing | Tool-centric | — | arXiv:2507.23370 | ||
| 2025 | arXiv preprint arXiv:2508.03501 | Training / post-training | Training-based / RL | — | arXiv:2508.03501 | ||
| 2025 | arXiv preprint arXiv:2506.10954 | Benchmark / infrastructure | Training-based / RL | Evaluation / infra | arXiv:2506.10954 | ||
| 2025 | arXiv preprint arXiv:2506.20081 | Benchmark / infrastructure | RAG-based | — | arXiv:2506.20081 | ||
| 2025 | arXiv preprint arXiv:2506.16136 | Validation / testing | RAG-based | — | arXiv:2506.16136 | ||
| 2025 | arXiv preprint arXiv:2504.07164 | Benchmark / infrastructure | Training-based / RL | Training environment | arXiv:2504.07164 | ||
| 2025 | arXiv preprint arXiv:2503.22424 | Patching | Graph-based | — | arXiv:2503.22424 | ||
| 2025 | arXiv preprint arXiv:2505.08120 | Validation / testing | Tool-centric | — | arXiv:2505.08120 | ||
| 2025 | arXiv preprint arXiv:2505.10887 | Validation / testing | Tool-centric | — | arXiv:2505.10887 | ||
| 2025 | arXiv preprint arXiv:2511.16004 | Validation / testing | Tool-centric | — | arXiv:2511.16004 | ||
| 2025 | arXiv preprint arXiv:2502.02747 | History-aware reasoning | Tool-centric | — | arXiv:2502.02747 | ||
| 2025 | arXiv preprint arXiv:2507.23348 | Validation / testing | Multi-agent | — | arXiv:2507.23348 | ||
| 2025 | arXiv preprint arXiv:2508.02085 | Validation / testing | Memory-based | — | arXiv:2508.02085 | ||
| 2025 | arXiv preprint arXiv:2508.21433 | History-aware reasoning | Tool-centric | — | arXiv:2508.21433 | ||
| 2025 | arXiv preprint arXiv:2512.22087 | Training / post-training | Training-based / RL | — | arXiv:2512.22087 | ||
| 2025 | arXiv preprint arXiv:2512.22469 | Localization | Graph-based | — | arXiv:2512.22469 | ||
| 2025 | Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering | Validation / testing | Graph-based | — | Scholar | ||
| 2025 | arXiv preprint arXiv:2502.20127 | History-aware reasoning | Training-based / RL | — | arXiv:2502.20127 | ||
| 2025 | 2025 40th IEEE/ACM International Conference on Automated Software Engineering (ASE) | History-aware reasoning | Training-based / RL | — | Scholar | ||
| 2025 | arXiv preprint arXiv:2508.03012 | History-aware reasoning | Training-based / RL | — | arXiv:2508.03012 | ||
| 2025 | arXiv preprint arXiv:2506.10484 | Memory / experience | Memory-based | — | arXiv:2506.10484 | ||
| 2025 | arXiv preprint arXiv:2503.16320 | Validation / testing | Tool-centric | — | arXiv:2503.16320 | ||
| 2025 | arXiv preprint arXiv:2509.25140 | Memory / experience | Memory-based | — | arXiv:2509.25140 | ||
| 2025 | arXiv preprint arXiv:2506.16650 | Validation / testing | Tool-centric | — | arXiv:2506.16650 | ||
| 2025 | arXiv preprint arXiv:2501.07811 | Validation / testing | Multi-agent | — | arXiv:2501.07811 | ||
| 2025 | arXiv preprint arXiv:2502.15850 | Validation / testing | Tool-centric | — | arXiv:2502.15850 | ||
| 2025 | arXiv preprint arXiv:2509.25193 | Training / post-training | Training-based / RL | — | arXiv:2509.25193 | ||
| 2025 | arXiv preprint arXiv:2505.07849 | History-aware reasoning | Training-based / RL | — | arXiv:2505.07849 | ||
| 2025 | arXiv preprint arXiv:2512.20482 | History-aware reasoning | Training-based / RL | Language-specific | arXiv:2512.20482 | ||
| 2025 | 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE) | Validation / testing | Tool-centric | — | Scholar | ||
| 2025 | arXiv preprint arXiv:2507.11988 | Patching | Multi-agent | — | arXiv:2507.11988 | ||
| 2025 | arXiv preprint arXiv:2512.21919 | Training / post-training | Training-based / RL | — | arXiv:2512.21919 | ||
| 2025 | Forty-second International Conference on Machine Learning | History-aware reasoning | Graph-based | — | Scholar | ||
| 2025 | arXiv preprint arXiv:2506.03011 | Validation / testing | Tool-centric | — | arXiv:2506.03011 | ||
| 2025 | arXiv preprint arXiv:2510.19898 | Training / post-training | Training-based / RL | — | arXiv:2510.19898 | ||
| 2025 | arXiv preprint arXiv:2501.10893 | Training / post-training | Training-based / RL | — | arXiv:2501.10893 | ||
| 2025 | arXiv preprint arXiv:2510.11967 | Training / post-training | Training-based / RL | — | arXiv:2510.11967 | ||
| 2025 | arXiv preprint arXiv:2507.06229 | Memory / experience | RAG-based | — | arXiv:2507.06229 | ||
| 2025 | arXiv preprint arXiv:2506.03921 | Training / post-training | Training-based / RL | — | arXiv:2506.03921 | ||
| 2025 | arXiv preprint arXiv:2505.18955 | History-aware reasoning | Training-based / RL | — | arXiv:2505.18955 | ||
| 2025 | Findings of the Association for Computational Linguistics: ACL 2025 | Validation / testing | Graph-based | — | Scholar | ||
| 2025 | arXiv preprint arXiv:2505.16901 | History-aware reasoning | Graph-based | — | arXiv:2505.16901 | ||
| 2025 | arXiv preprint arXiv:2508.02611 | Patching | RAG-based | — | arXiv:2508.02611 | ||
| 2025 | arXiv preprint arXiv:2509.00971 | Validation / testing | Tool-centric | — | arXiv:2509.00971 | ||
| 2025 | arXiv preprint arXiv:2506.08173 | Validation / testing | Tool-centric | — | arXiv:2506.08173 | ||
| 2025 | Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering | Validation / testing | Tool-centric | — | Scholar | ||
| 2025 | arXiv preprint arXiv:2509.21427 | Localization | RAG-based | — | arXiv:2509.21427 | ||
| 2025 | arXiv preprint arXiv:2510.01003 | Memory / experience | Graph-based | — | arXiv:2510.01003 | ||
| 2025 | arXiv preprint arXiv:2506.12728 | History-aware reasoning | Training-based / RL | — | arXiv:2506.12728 | ||
| 2025 | arXiv preprint arXiv:2510.01132 | Training / post-training | Training-based / RL | — | arXiv:2510.01132 | ||
| 2025 | arXiv preprint arXiv:2508.11553 | History-aware reasoning | Training-based / RL | — | arXiv:2508.11553 | ||
| 2025 | arXiv preprint arXiv:2506.07636 | History-aware reasoning | Training-based / RL | — | arXiv:2506.07636 | ||
| 2025 | arXiv preprint arXiv:2502.18449 | History-aware reasoning | Training-based / RL | — | arXiv:2502.18449 | ||
| 2025 | arXiv preprint arXiv:2512.18552 | History-aware reasoning | Training-based / RL | — | arXiv:2512.18552 | ||
| 2025 | arXiv preprint arXiv:2512.10398 | Validation / testing | Tool-centric | — | arXiv:2512.10398 | ||
| 2025 | arXiv preprint arXiv:2508.00031 | Validation / testing | Tool-centric | — | arXiv:2508.00031 | ||
| 2025 | Proceedings of the ACM on Software Engineering | Validation / testing | Tool-centric | — | Scholar | ||
| 2025 | arXiv preprint arXiv:2511.13646 | Validation / testing | Tool-centric | — | arXiv:2511.13646 | ||
| 2025 | arXiv preprint arXiv:2509.23586 | Validation / testing | Tool-centric | — | arXiv:2509.23586 | ||
| 2025 | arXiv preprint arXiv:2501.05040 | History-aware reasoning | Training-based / RL | — | arXiv:2501.05040 | ||
| 2025 | Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track | Benchmark / infrastructure | Training-based / RL | — | Scholar | ||
| 2025 | arXiv preprint arXiv:2503.21710 | History-aware reasoning | Graph-based | — | arXiv:2503.21710 | ||
| 2025 | N/A | History-aware reasoning | Graph-based | — | Scholar | ||
| 2025 | arXiv preprint arXiv:2509.23045 | History-aware reasoning | Training-based / RL | — | arXiv:2509.23045 | ||
| 2025 | arXiv preprint arXiv:2510.11838 | History-aware reasoning | Tool-centric | — | arXiv:2510.11838 | ||
| 2025 | arXiv preprint arXiv:2504.21798 | History-aware reasoning | Training-based / RL | — | arXiv:2504.21798 | ||
| 2025 | arXiv preprint arXiv:2509.12434 | Training / post-training | Training-based / RL | — | arXiv:2509.12434 | ||
| 2025 | arXiv preprint arXiv:2502.00350 | Validation / testing | Graph-based | — | arXiv:2502.00350 | ||
| 2025 | arXiv preprint arXiv:2506.09289 | Benchmark / infrastructure | Tool-centric | SWE-bench | arXiv:2506.09289 | ||
| 2025 | arXiv preprint arXiv:2505.13652 | History-aware reasoning | Training-based / RL | — | arXiv:2505.13652 | ||
| 2025 | arXiv preprint arXiv:2505.23604 | History-aware reasoning | Training-based / RL | — | arXiv:2505.23604 | ||
| 2025 | arXiv preprint arXiv:2506.19290 | History-aware reasoning | Training-based / RL | — | arXiv:2506.19290 | ||
| 2025 | arXiv preprint arXiv:2506.15655 | Benchmark / infrastructure | RAG-based | — | arXiv:2506.15655 | ||
| 2025 | arXiv preprint arXiv:2505.22954 | Validation / testing | Tool-centric | — | arXiv:2505.22954 | ||
| 2025 | arXiv preprint arXiv:2512.20957 | Training / post-training | Training-based / RL | — | arXiv:2512.20957 | ||
| 2025 | arXiv preprint arXiv:2503.18455 | Training / post-training | Graph-based | — | arXiv:2503.18455 | ||
| 2025 | arXiv preprint arXiv:2510.21903 | Validation / testing | Tool-centric | — | arXiv:2510.21903 | ||
| 2025 | arXiv preprint arXiv:2512.12216 | Training / post-training | Training-based / RL | — | arXiv:2512.12216 | ||
| 2024 | arXiv preprint arXiv:2410.20285 | Validation / testing | Tool-centric | — | arXiv:2410.20285 | ||
| 2024 | arXiv preprint arXiv:2406.11638 | Architecture / constraint-aware | Multi-agent | — | arXiv:2406.11638 | ||
| 2024 | arXiv preprint arXiv:2407.21787 | Validation / testing | Tool-centric | — | arXiv:2407.21787 | ||
| 2024 | arXiv preprint arXiv:2406.01304 | History-aware reasoning | Multi-agent | — | arXiv:2406.01304 | ||
| 2024 | arXiv preprint arXiv:2410.04485 | Validation / testing | Tool-centric | SWE-bench | arXiv:2410.04485 | ||
| 2024 | arXiv preprint arXiv:2409.11190 | Validation / testing | Tool-centric | — | arXiv:2409.11190 | ||
| 2024 | arXiv preprint arXiv:2411.01114 | Validation / testing | Tool-centric | — | arXiv:2411.01114 | ||
| 2024 | arXiv preprint arXiv:2411.04329 | Patching | RAG-based | — | arXiv:2411.04329 | ||
| 2024 | arXiv preprint arXiv:2411.13941 | Validation / testing | Tool-centric | — | arXiv:2411.13941 | ||
| 2024 | arXiv preprint arXiv:2411.12644 | Benchmark / infrastructure | Training-based / RL | — | arXiv:2411.12644 | ||
| 2024 | arXiv preprint arXiv:2408.03910 | Patching | Graph-based | — | arXiv:2408.03910 | ||
| 2024 | arXiv preprint arXiv:2409.00899 | Validation / testing | Graph-based | — | arXiv:2409.00899 | ||
| 2024 | arXiv preprint arXiv:2411.00622 | History-aware reasoning | Training-based / RL | — | arXiv:2411.00622 | ||
| 2024 | arXiv preprint arXiv:2412.19031 | History-aware reasoning | Training-based / RL | — | arXiv:2412.19031 | ||
| 2024 | arXiv preprint arXiv:2410.14684 | Patching | Graph-based | — | arXiv:2410.14684 | ||
| 2024 | arXiv preprint arXiv:2412.21139 | Benchmark / infrastructure | Training-based / RL | Training environment | arXiv:2412.21139 | ||
| 2024 | arXiv preprint arXiv:2409.16299 | Validation / testing | Tool-centric | — | arXiv:2409.16299 | ||
| 2024 | arXiv preprint arXiv:2411.03455 | Localization | Memory-based | — | arXiv:2411.03455 | ||
| 2024 | arXiv preprint arXiv:2412.01007 | Benchmark / infrastructure | Training-based / RL | — | arXiv:2412.01007 | ||
| 2024 | Advances in Neural Information Processing Systems | History-aware reasoning | Multi-agent | — | Scholar | ||
| 2024 | arXiv preprint arXiv:2407.16741 | Validation / testing | Tool-centric | — | arXiv:2407.16741 | ||
| 2024 | Advances in Neural Information Processing Systems | Validation / testing | Tool-centric | — | Scholar | ||
| 2024 | Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis | Validation / testing | Tool-centric | — | Scholar | ||
| 2024 | arXiv preprint arXiv:2412.17315 | Patching | Pipeline | — | arXiv:2412.17315 | ||
| 2024 | arXiv preprint arXiv:2408.07060 | Validation / testing | Agentic | — | arXiv:2408.07060 | ||
| No papers match the current filters. | |||||||
Compare Methods¶
Method Comparison Table¶
This table is designed for related-work comparison and baseline selection, so it foregrounds strategy differences rather than the full coding schema.
| Paper | Agent Type | Main Knowledge | Retrieval Style | Feedback Loop | Memory / History | Benchmark / Setting | Best Use Case |
|---|---|---|---|---|---|---|---|
| Swe-agent: Agent-computer interfaces enable automated software engineering | Agentic runtime | Tool, Code, Memory | Active + tool-centric | Strong | Weak explicit memory | — | Classic repository-level baseline |
| EXPEREPAIR: Dual-Memory Enhanced LLM-based Repository-Level Program Repair | Memory-based repair | Tool, Code, Memory | Retrieval + memory | Medium | Strong experience memory | — | Experience reuse |
| KGCompass: Knowledge Graph Enhanced Repository-Level Software Repair | Graph-guided retrieval | Tool, Code, History | Structured historical retrieval | Medium | Strong history | — | History-aware localization and repair |
| R2e-gym: Procedural environments and hybrid verifiers for scaling open-weights swe agents | Training environment | Tool, Code, Memory, History | Tool-mediated | Strong | Medium | Training environment | Agent training infrastructure |
| ArkEval: Benchmarking and Evaluating Automated CodeRepair for ArkTS | Benchmark / evaluation | Tool, Code, Lang, History | RAG + repo context | Medium | Weak | ArkTS / HarmonyOS | Language- and platform-specific evaluation |
| Agentic Rubrics as Contextual Verifiers for SWE Agents | Verifier-centered | Arch, Tool, Code | Structured contextual retrieval | Strong | Constraint-aware | — | Architecture / validation constraints |
Focused Cross-Dimension Tables¶
Only the most action-oriented cross-dimension views are kept here. They answer concrete navigation and comparison questions instead of repeating every RQ matrix from the manuscript.
Source × Extraction Method¶
| Source | Rule-based Extraction | Model-based Extraction | Manual Extraction |
|---|---|---|---|
| Codebase |
125
Knowledge: Existing Code Knowledge, Development Tool Knowledge
Examples:
|
88
Knowledge: Existing Code Knowledge, Development Tool Knowledge
Examples:
|
38
Knowledge: Existing Code Knowledge, Development Tool Knowledge
Examples:
|
| Dynamic Execution Results |
101
Knowledge: Development Tool Knowledge, Existing Code Knowledge
Examples:
|
74
Knowledge: Existing Code Knowledge, Development Tool Knowledge
Examples:
|
35
Knowledge: Development Tool Knowledge, Existing Code Knowledge
Examples:
|
| Expert |
42
Knowledge: Existing Code Knowledge, Development Tool Knowledge
Examples:
|
30
Knowledge: Existing Code Knowledge, Development Tool Knowledge
Examples:
|
35
Knowledge: Existing Code Knowledge, Development Tool Knowledge
Examples:
|
| Repository History |
43
Knowledge: Existing Code Knowledge, Repository Evolution Knowledge
Examples:
|
31
Knowledge: Existing Code Knowledge, Repository Evolution Knowledge
Examples:
|
11
Knowledge: Existing Code Knowledge, Repository Evolution Knowledge
Examples:
|
| Document |
9
Knowledge: Existing Code Knowledge, Development Tool Knowledge
Examples:
|
9
Knowledge: Existing Code Knowledge, Development Tool Knowledge
Examples:
|
7
Knowledge: Development Tool Knowledge, Existing Code Knowledge
Examples:
|
Stage × Dominant Knowledge¶
| Stage | Dominant Knowledge | Representative Works | Typical Artifacts |
|---|---|---|---|
| Localization | Existing Code Knowledge, Development Tool Knowledge, Experiential Knowledge | Suspicious files, functions, modules, or ranked locations. | |
| Patch Generation | Existing Code Knowledge, Development Tool Knowledge, Experiential Knowledge | Candidate patches or edit plans. | |
| Patch Validation | Existing Code Knowledge, Development Tool Knowledge, Experiential Knowledge | Test results, rubric decisions, and patch-selection signals. | |
| Model Training | Existing Code Knowledge, Development Tool Knowledge, Experiential Knowledge | Training trajectories, preference data, or reward signals. | |
| Reproduction Test Generation | Existing Code Knowledge, Development Tool Knowledge, Experiential Knowledge | Failing tests, reproduction scripts, or issue reproductions. | |
| Task Planning | Development Tool Knowledge, Existing Code Knowledge, Experiential Knowledge | Plans, decomposition traces, or retrieval strategies. | |
| Environment Building | Development Tool Knowledge, Existing Code Knowledge, Experiential Knowledge | Sandbox setup, test environment, executable workspace. |
RQ6 Trends¶
These lightweight views are retained as advanced analytics. They summarize temporal emphasis and recurring knowledge pairings, but the primary workflow for finding concrete papers remains the master table and method-comparison sections above.
Temporal Trend¶
| Year | Included Papers |
|---|---|
| 2024 | 25 |
| 2025 | 92 |
| 2026 | 16 |
Latest-Half Knowledge Frequency¶
| Knowledge Type | Count |
|---|---|
| Existing Code Knowledge | 65 |
| Development Tool Knowledge | 49 |
| Experiential Knowledge | 36 |
| Repository Evolution Knowledge | 23 |
| Design Architecture Knowledge | 4 |
| Technology Stack Knowledge | 1 |
Pairwise Knowledge Combinations¶
| Knowledge A | Knowledge B | Count |
|---|---|---|
| Development Tool Knowledge | Existing Code Knowledge | 101 |
| Existing Code Knowledge | Experiential Knowledge | 65 |
| Development Tool Knowledge | Experiential Knowledge | 62 |
| Existing Code Knowledge | Repository Evolution Knowledge | 43 |
| Development Tool Knowledge | Repository Evolution Knowledge | 31 |
| Experiential Knowledge | Repository Evolution Knowledge | 21 |
| Design Architecture Knowledge | Existing Code Knowledge | 13 |
| Design Architecture Knowledge | Development Tool Knowledge | 11 |
| Design Architecture Knowledge | Experiential Knowledge | 6 |
| Development Tool Knowledge | Technology Stack Knowledge | 4 |
| Existing Code Knowledge | Technology Stack Knowledge | 4 |
| Design Architecture Knowledge | Repository Evolution Knowledge | 4 |
| Experiential Knowledge | Technology Stack Knowledge | 3 |
| Existing Code Knowledge | Programming Language Knowledge | 3 |
| Development Tool Knowledge | Programming Language Knowledge | 2 |
| Programming Language Knowledge | Repository Evolution Knowledge | 2 |
| Repository Evolution Knowledge | Technology Stack Knowledge | 2 |
| Design Architecture Knowledge | Domain-specific Knowledge | 1 |
| Design Architecture Knowledge | Technology Stack Knowledge | 1 |
| Development Tool Knowledge | Domain-specific Knowledge | 1 |
| Domain-specific Knowledge | Existing Code Knowledge | 1 |
| Domain-specific Knowledge | Experiential Knowledge | 1 |
| Domain-specific Knowledge | Technology Stack Knowledge | 1 |
| Programming Language Knowledge | Technology Stack Knowledge | 1 |
| Experiential Knowledge | Programming Language Knowledge | 1 |
Resources¶
Benchmark Index¶
| Benchmark | Scope | Language | Task | Execution | Good For |
|---|---|---|---|---|---|
| BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing? | Cross-repository issue resolution | Multi / domain-heavy | Benchmark | Mixed | Domain and stack constraints |
| ArkEval: Benchmarking and Evaluating Automated CodeRepair for ArkTS | ArkTS repair | ArkTS | Benchmark | Yes | Low-resource language evaluation |
| R2e-gym: Procedural environments and hybrid verifiers for scaling open-weights swe agents | Training environment | Multi | Gym / environment | Yes | Procedural training and verification |
| Training software engineering agents and verifiers with swe-gym | SWE-Gym | Repo-level | Training + evaluation | Yes | Agent training baselines |
| SWE-Factory: Your Automated Factory for Issue Resolution Training Data and Evaluation Benchmarks | Data / benchmark factory | Repo-level | Infrastructure | Yes | Benchmark construction |
Tool / Framework Index¶
| Tool / Framework | Type | Main Stage | Open Style | Strength |
|---|---|---|---|---|
| Swe-agent: Agent-computer interfaces enable automated software engineering | Agent runtime | Full pipeline | Paper-linked | Classic agent-computer interface baseline |
| Openhands: An open platform for ai software developers as generalist agents | Generalist platform | Full pipeline | Paper-linked | Sandbox-based software agent runtime |
| Autocoderover: Autonomous program improvement | Repair agent | Localization + patching | Paper-linked | Repository exploration and autonomous improvement |
| KGCompass: Knowledge Graph Enhanced Repository-Level Software Repair | Graph retrieval system | Localization + patching | Paper-linked | History-aware repository retrieval |
Knowledge Graph / Memory Index¶
| Resource | Category | Emphasis |
|---|---|---|
| KGCompass: Knowledge Graph Enhanced Repository-Level Software Repair | Repository history graph | History + code alignment |
| CoSIL: Software Issue Localization via LLM-Driven Code Repository Graph Searching | Repository graph search | Localization via structure-aware navigation |
| Codexgraph: Bridging large language models and code repositories via code graph databases | Code graph database | Graph-backed repository reasoning |
| EXPEREPAIR: Dual-Memory Enhanced LLM-based Repository-Level Program Repair | Experience memory | Dual-memory repair guidance |
| Reasoningbank: Scaling agent self-evolving with reasoning memory | Reasoning memory | Reusable strategy bank |
Representative Works Profiles¶
Cross-repository, domain-specific, and dependency-migration aware. Tests agents beyond single-repo bug fixing with domain knowledge and technology stack constraints.
Low-resource language (ArkTS) + platform (HarmonyOS) constraints. Benchmarks code repair under language-specific and ecosystem constraints.
Procedural environments and hybrid verifiers for scaling open-weights SWE agents. Executable environments with deterministic and LLM-based verification.
Extracts procedural knowledge from historical data, demonstrating how repository history can inform current issue resolution.
Repository-aware knowledge graph connecting issues, PRs, files, classes, and functions. Structured history retrieval for localization and repair.
Episodic memory (concrete demonstrations) + semantic memory (abstract reflections). Dual-memory architecture for experience-driven repair.
Agent-computer interface (ACI) design. Demonstrates how structured tool interfaces constrain agent actions for repository-level tasks.
General sandbox platform for code agents. Supports multi-turn interaction, tool invocation, and experience accumulation.
Extracts general reasoning strategies from success/failure experiences. Reasoning strategy bank for cross-task transfer.
Graph-structured integrated model. Embeds repository code as a graph into the LLM for structure-aware code understanding.