{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"swe-bench-verified","slug":"swe-bench-verified","name":"SWE-bench Verified","type":"benchmark","url":"https://www.swebench.com","page_url":"https://unfragile.ai/swe-bench-verified","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"swe-bench-verified__cap_0","uri":"capability://planning.reasoning.real.world.github.issue.resolution.evaluation","name":"real-world github issue resolution evaluation","description":"Evaluates AI coding agents' ability to autonomously resolve authentic GitHub issues from popular Python repositories by executing multi-step reasoning and code modification workflows in sandboxed Docker environments. The benchmark measures binary resolution outcomes (issue resolved or not) by validating that agent-generated code changes pass the repository's existing test suite, providing a task-oriented evaluation of end-to-end software engineering capability rather than isolated code generation.","intents":["Measure how well an AI coding agent can handle real-world bug fixes and feature requests from production codebases","Compare different AI agents and models on their ability to navigate complex repositories and implement working solutions","Establish baseline performance metrics for autonomous software engineering systems before deployment","Identify which types of GitHub issues (by repository, complexity, or domain) are solvable by current AI agents"],"best_for":["AI research teams developing and benchmarking autonomous coding agents","Model providers (OpenAI, Anthropic, open-source) evaluating coding capabilities across model versions","Software engineering teams assessing whether AI agents can augment their development workflows"],"limitations":["Binary metric with no partial credit — agents receive 0% for incomplete solutions even if they make significant progress toward resolution","Python-only for Verified subset (500 instances); separate Multilingual variant required for non-Python evaluation","Definition of 'resolved' not explicitly documented in provided material — likely requires passing test suite but exact criteria unknown","No statistical significance testing or confidence intervals provided — cannot determine if performance differences between agents are meaningful","Potential training data contamination — GitHub issues may appear in LLM training sets, inflating performance metrics","Evaluation time and cost per instance not documented — cannot budget computational resources for full benchmark runs","Human verification methodology for Verified subset not disclosed — annotation quality and inter-rater agreement unknown"],"requires":["Docker runtime for sandboxed code execution","Python 3.x environment with repository-specific dependencies","Access to GitHub issue data and corresponding repository code","Test suite execution capability within sandboxed environment","Agent framework capable of multi-step reasoning and code modification (e.g., SWE-agent, mini-SWE-agent)"],"input_types":["GitHub issue text (bug reports, feature requests, enhancement requests)","Repository source code and file structure","Repository test suites and validation scripts","Issue metadata (repository name, issue number, creation date)"],"output_types":["Binary resolution outcome (resolved/not resolved)","Percentage of instances resolved (aggregated metric)","Per-repository resolution rates","Cost and step metrics for each agent attempt"],"categories":["planning-reasoning","code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swe-bench-verified__cap_1","uri":"capability://data.processing.analysis.multi.variant.benchmark.suite.with.specialized.subsets","name":"multi-variant benchmark suite with specialized subsets","description":"Provides four distinct benchmark variants (Verified: 500 instances, Lite: 300 instances, Full: 2,294 instances, Multilingual: 300 instances across 9 languages, Multimodal: 517 instances with visual elements) allowing evaluation at different cost/coverage tradeoffs and across different programming languages and modalities. Each variant maintains the same core task structure (resolve GitHub issues via code modification) but targets different evaluation scenarios — Verified for high-confidence results, Lite for rapid iteration, Full for comprehensive assessment, Multilingual for language coverage, and Multimodal for visual understanding.","intents":["Run quick agent evaluations on Lite (300 instances) during development without full benchmark cost","Conduct comprehensive evaluation on Full (2,294 instances) for publication-quality results with broader coverage","Evaluate coding agents on non-Python languages using Multilingual variant (9 languages)","Test agents' ability to understand and resolve issues involving visual elements (diagrams, screenshots) via Multimodal variant","Use Verified subset (500 instances) for high-confidence performance claims with human-verified solvability"],"best_for":["Research teams with varying computational budgets — can start with Lite for rapid iteration, graduate to Full for final results","Model providers supporting multiple programming languages — Multilingual variant enables cross-language capability comparison","Teams evaluating agents on real-world issues that include visual documentation or diagrams","Organizations publishing benchmarking results — Verified subset provides defensible, human-verified performance claims"],"limitations":["Variants are separate benchmarks with different instance counts — cannot directly compare agent performance across variants (e.g., 65% on Verified does not equal 65% on Full)","Multilingual variant (300 instances) is significantly smaller than Verified (500) — may have higher variance and lower statistical power","Multimodal variant (517 instances) requires agents with vision capabilities — not all coding agents support multimodal input","No documented guidance on which variant to use for different evaluation scenarios — researchers must make ad-hoc choices","Train/test split and contamination analysis not documented for any variant — cannot assess whether results reflect true generalization"],"requires":["Agent framework capable of handling the specific variant's requirements (e.g., vision model for Multimodal)","Docker runtime for all variants (code execution in sandboxed environment)","Repository-specific dependencies for each instance (Python packages, language runtimes, build tools)","Computational budget proportional to variant size (Lite: ~300 evaluations, Full: ~2,294 evaluations)"],"input_types":["GitHub issues (text-based for Verified/Lite/Full/Multilingual)","GitHub issues with embedded images, diagrams, or visual elements (Multimodal)","Repository code in Python (Verified/Lite/Full), multiple languages (Multilingual), or with visual documentation (Multimodal)"],"output_types":["Resolution rate (% of instances resolved) per variant","Per-repository resolution breakdown","Per-language resolution breakdown (Multilingual)","Cost and step metrics per variant"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swe-bench-verified__cap_10","uri":"capability://image.visual.multimodal.issue.resolution.with.visual.elements","name":"multimodal issue resolution with visual elements","description":"The Multimodal variant (517 instances) includes GitHub issues that contain visual elements such as diagrams, screenshots, or images that are relevant to understanding and resolving the issue. This variant requires agents with vision capabilities (e.g., multimodal LLMs) to process both text and visual information, extending evaluation beyond text-only code understanding.","intents":["Evaluate agents with vision capabilities on realistic issues that include visual documentation","Assess whether agents can leverage visual information to better understand and resolve issues","Test multimodal LLMs on practical software engineering tasks","Identify challenges in visual understanding for coding tasks"],"best_for":["Teams developing multimodal coding agents with vision capabilities","Model providers evaluating vision-language models on practical tasks","Researchers studying the role of visual information in software engineering"],"limitations":["Multimodal variant is separate from Verified subset — cannot compare multimodal vs. text-only performance on same instances","Only 517 instances total — smaller than Verified (500) or Full (2,294), potentially insufficient for reliable metrics","Visual element types not documented — unclear what types of images/diagrams are included (screenshots, diagrams, charts, etc.)","Visual element relevance not documented — unclear whether visual elements are essential for resolving issues or supplementary","Requires agents with vision capabilities — not all coding agents support multimodal input","No analysis of how visual information impacts resolution rate compared to text-only evaluation"],"requires":["Agent framework with vision capabilities (multimodal LLM, vision encoder)","Image processing and embedding infrastructure","Docker environment capable of handling image data"],"input_types":["GitHub issues with embedded images, diagrams, or visual elements","Repository source code","Visual elements (images, diagrams, screenshots)"],"output_types":["Resolution rate on multimodal instances","Per-instance analysis of whether visual information was leveraged","Comparison of multimodal vs. text-only performance (if available)"],"categories":["image-visual","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swe-bench-verified__cap_11","uri":"capability://tool.use.integration.agent.framework.integration.and.standardized.evaluation.interface","name":"agent framework integration and standardized evaluation interface","description":"SWE-bench defines a standardized evaluation interface that agent frameworks (SWE-agent, mini-SWE-agent, custom agents) must implement to be evaluated on the benchmark. This interface specifies how agents receive GitHub issues, interact with the repository, execute code modifications, and report results. The standardization enables fair comparison across different agent architectures and frameworks by ensuring all agents operate under the same constraints and evaluation protocol.","intents":["Ensure fair comparison across different agent frameworks by standardizing evaluation interface","Enable new agents to be evaluated on SWE-bench by implementing standard interface","Facilitate reproducibility by defining exact evaluation protocol","Support both open-source and proprietary agents on same benchmark"],"best_for":["Agent framework developers implementing SWE-bench evaluation support","Researchers comparing agents from different frameworks","Benchmark organizers ensuring fair evaluation across diverse agents"],"limitations":["Standardized interface specification not documented in provided material — cannot determine exact interface requirements","Interface may constrain agent design — agents must conform to interface rather than using optimal architecture","No documentation of how interface handles different agent paradigms (e.g., planning-based vs. reactive agents)","Interface evolution not documented — unclear how interface changes are managed and communicated to agent developers"],"requires":["Agent framework implementation of standardized evaluation interface","Documentation of interface specification (not provided in material)"],"input_types":["GitHub issue (text, metadata)","Repository context (code, file structure, test suite)"],"output_types":["Code modifications (patches, file edits)","Execution results (test output, error messages)","Resolution outcome (resolved/not resolved)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swe-bench-verified__cap_12","uri":"capability://data.processing.analysis.benchmark.dataset.curation.and.issue.selection","name":"benchmark dataset curation and issue selection","description":"SWE-bench curates GitHub issues from popular Python repositories, selecting issues that are suitable for autonomous resolution (e.g., bug fixes, feature requests, but excluding infrastructure-only changes or documentation-only updates). The curation process filters issues based on solvability, complexity, and relevance to software engineering tasks. The Verified subset (500 instances) underwent additional human verification to confirm solvability, while the Full set (2,294 instances) includes all curated instances without verification.","intents":["Obtain a representative set of real-world software engineering tasks for agent evaluation","Ensure benchmark instances are solvable by code modification (not infrastructure or documentation changes)","Balance benchmark across different repositories and issue types","Provide multiple dataset sizes (Lite: 300, Verified: 500, Full: 2,294) for different evaluation scenarios"],"best_for":["Benchmark organizers curating high-quality evaluation datasets","Researchers wanting realistic software engineering tasks for agent evaluation","Teams assessing whether benchmark instances are representative of their codebase"],"limitations":["Repository selection criteria not documented — unclear what makes a repository 'popular' or suitable for inclusion","Issue selection criteria not documented — unclear what types of issues are included/excluded","Curation methodology not documented — cannot assess potential biases in issue selection","Repository distribution not documented — unclear if instances are evenly distributed or biased toward certain repositories","Issue complexity distribution not documented — cannot determine if benchmark is balanced across difficulty levels","Temporal bias not addressed — issue creation dates not documented; potential for model training data overlap"],"requires":["GitHub API access to retrieve issues and repository metadata","Curation infrastructure (filtering, deduplication, quality assessment)","Human annotators for Verified subset verification"],"input_types":["GitHub issues from popular Python repositories","Repository metadata (language, popularity, domain)"],"output_types":["Curated dataset of GitHub issues (Lite: 300, Verified: 500, Full: 2,294)","Per-issue metadata (repository, issue type, complexity, solvability)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swe-bench-verified__cap_2","uri":"capability://search.retrieval.leaderboard.based.agent.performance.ranking.and.filtering","name":"leaderboard-based agent performance ranking and filtering","description":"Provides a web-based leaderboard (swebench.com) that ranks AI coding agents by resolution rate across multiple benchmark variants, with filtering capabilities by agent type (mini-SWE-agent, SWE-agent, OSS agents, all agents), model category (open-source vs. proprietary), scaffold type, and tags. The leaderboard visualizes performance across multiple dimensions including resolution rate, per-repository breakdown, cost-efficiency (resolved vs. cost scatter plots), and temporal trends (resolved vs. model release date), enabling comparative analysis of agent capabilities and cost-performance tradeoffs.","intents":["Compare performance of different AI agents (mini-SWE-agent v2, SWE-agent, custom agents) on the same benchmark","Filter leaderboard to show only open-source agents or proprietary agents depending on evaluation focus","Identify which repositories or languages have highest/lowest agent success rates","Analyze cost-efficiency tradeoffs — find agents that achieve high resolution rates with low computational cost","Track performance trends over time as new models are released"],"best_for":["AI researchers comparing agent architectures and model choices on standardized benchmarks","Model providers (OpenAI, Anthropic, open-source communities) tracking their agents' leaderboard position","Teams selecting which agent framework to adopt based on published performance metrics","Benchmark organizers monitoring benchmark health and identifying saturation or ceiling effects"],"limitations":["Leaderboard submission process not documented in provided material — cannot determine submission requirements, deadlines, or verification procedures","No statistical significance testing or confidence intervals — cannot determine if performance differences between agents are meaningful or due to variance","Filtering options (agent type, model category, scaffold type, tags) are not fully documented — unclear what each filter includes or excludes","Leaderboard visualizations show aggregate metrics only — no per-instance analysis or failure mode categorization","No documentation of how ties are broken or how leaderboard is sorted — ranking methodology unclear","Potential for overfitting to benchmark — public leaderboard since 03/2024 creates incentive for agents to optimize specifically for SWE-bench instances"],"requires":["Web browser with JavaScript support to access interactive leaderboard","Agent framework that can be evaluated on SWE-bench (e.g., SWE-agent, mini-SWE-agent, custom agent with compatible interface)","Submission credentials or API access to leaderboard (submission process unknown)"],"input_types":["Agent evaluation results (resolution rate, cost, steps, per-repository breakdown)","Agent metadata (name, type, model, release date, scaffold type)"],"output_types":["Ranked list of agents by resolution rate","Filtered subsets of leaderboard (by agent type, model category, scaffold, tags)","Visualizations: bar charts (resolved %), per-repository heatmaps, cost-efficiency scatter plots, temporal trend plots","Per-agent metrics: resolution rate, cost, step count, per-repository breakdown"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swe-bench-verified__cap_3","uri":"capability://automation.workflow.docker.sandboxed.code.execution.and.test.validation","name":"docker-sandboxed code execution and test validation","description":"Executes agent-generated code modifications within isolated Docker containers that replicate the target repository's environment, including all dependencies, build tools, and test suites. This sandboxing approach ensures that code changes are validated against the actual test suite in a controlled environment, preventing agents from gaming the benchmark through environment-specific hacks and ensuring reproducibility across different evaluation machines. The Docker infrastructure was added in 06/2024 to standardize evaluation environments.","intents":["Safely execute untrusted agent-generated code without risking the evaluation system","Validate that agent-generated code changes actually pass the repository's test suite in a realistic environment","Ensure reproducible evaluation results across different machines and evaluation runs","Prevent agents from exploiting environment-specific quirks or hardcoding solutions"],"best_for":["Benchmark organizers ensuring safe, reproducible evaluation of untrusted agent code","Research teams running local evaluations of custom agents without relying on centralized leaderboard submission","Organizations with security requirements that mandate sandboxed code execution"],"limitations":["Docker overhead adds latency to evaluation — exact timing impact not documented","Container setup time for each instance (installing dependencies, building code) not quantified — affects total evaluation duration","Docker requires significant disk space for storing container images and instance artifacts — storage requirements not documented","Network access within containers not documented — unclear if agents can access external APIs or only local repository code","Container resource limits (CPU, memory, disk) not documented — cannot determine if resource constraints affect agent behavior","Evaluation time per instance not provided — cannot budget computational resources for full benchmark runs"],"requires":["Docker runtime (version not specified, likely Docker 20.10+)","Repository-specific dependencies (Python packages, language runtimes, build tools) pre-configured in container images","Test suite execution capability within container (pytest, unittest, or language-specific test runners)","Sufficient disk space for container images and instance artifacts","Agent framework capable of executing code modifications and reading test output"],"input_types":["Agent-generated code modifications (patches, file edits, new files)","Repository source code and test suite","Repository configuration (setup.py, requirements.txt, Dockerfile, etc.)"],"output_types":["Test execution results (pass/fail, test output, error messages)","Binary resolution outcome (issue resolved if all tests pass)","Execution logs and artifacts"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swe-bench-verified__cap_4","uri":"capability://safety.moderation.human.verified.solvability.filtering.for.verified.subset","name":"human-verified solvability filtering for verified subset","description":"The Verified subset (500 instances) underwent explicit human verification to confirm that each GitHub issue is actually solvable by code modification, filtering out unsolvable issues (e.g., issues requiring infrastructure changes, documentation-only fixes, or issues with conflicting requirements). This verification process was completed by 08/2024 in collaboration with OpenAI, reducing false negatives from unsolvable issues that would artificially inflate baseline difficulty and make agent performance metrics less reliable.","intents":["Obtain high-confidence performance metrics by evaluating only on confirmed-solvable issues","Publish benchmark results with defensible claims about agent capability (e.g., '65% of solvable issues resolved')","Reduce variance in agent evaluation by eliminating unsolvable instances that no agent could resolve","Identify which types of issues are solvable vs. unsolvable for future benchmark design"],"best_for":["Research teams publishing results that require high-confidence performance claims","Model providers making public statements about agent capability","Organizations with limited evaluation budgets that want to maximize signal-to-noise ratio"],"limitations":["Human verification methodology not documented — cannot assess annotation quality, inter-rater agreement, or potential biases","Verification criteria not specified — unclear what makes an issue 'solvable' (e.g., does it require only code changes, or can it require configuration changes?)","Verification process may introduce human bias — annotators may have different interpretations of solvability","Verified subset is smaller (500 instances) than Full (2,294 instances) — lower statistical power and potentially higher variance","Verification is one-time process — no ongoing quality assurance or re-verification as new agents emerge","No documentation of which issues were filtered out or why — cannot analyze filtering bias"],"requires":["Human annotators with software engineering expertise to assess solvability","Clear solvability criteria and annotation guidelines (not provided in documentation)","Annotation infrastructure (e.g., labeling platform, inter-rater agreement tracking)"],"input_types":["GitHub issues from popular Python repositories","Repository source code and context"],"output_types":["Binary solvability assessment (solvable/unsolvable) per issue","Filtered dataset of 500 solvable instances","Potentially: annotation rationale or solvability criteria"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swe-bench-verified__cap_5","uri":"capability://data.processing.analysis.cost.and.efficiency.metrics.tracking.and.visualization","name":"cost and efficiency metrics tracking and visualization","description":"Tracks and visualizes multiple efficiency dimensions for each agent evaluation: total cost (API calls, compute), step count (number of agent actions), and resolved instances achieved within cost/step budgets. The leaderboard provides scatter plot visualizations of resolved vs. cost, resolved vs. average cost, resolved vs. cost limit, and resolved vs. step limit, enabling analysis of cost-performance tradeoffs and identification of efficient agents that achieve high resolution rates with minimal computational overhead.","intents":["Compare agents not just on resolution rate but on cost-efficiency — find agents that achieve high performance with low API costs","Analyze cost-performance tradeoffs — understand how much additional cost is required to improve resolution rate","Identify agents that are efficient for resource-constrained environments (e.g., limited API budgets)","Track how agent efficiency improves over time as new models and architectures are developed"],"best_for":["Teams deploying AI agents in production with limited API budgets or computational resources","Model providers optimizing inference cost and latency","Researchers analyzing the relationship between agent complexity and performance gains"],"limitations":["Cost metrics definition not documented — unclear whether cost includes API calls, compute time, or both","Step count definition not documented — unclear what constitutes a 'step' (agent action, API call, code modification?)","Cost tracking methodology not specified — cannot determine if costs are actual (from API providers) or estimated","No per-instance cost breakdown — cannot identify which types of issues are expensive to resolve","Cost comparison across different models/APIs not standardized — cannot directly compare cost-efficiency across different agent implementations","No documentation of cost limits or step limits used for filtering — cannot understand what 'cost limit' and 'step limit' filters mean"],"requires":["Cost tracking infrastructure in agent framework (API call logging, compute time measurement)","Standardized cost metrics across different agent implementations","Leaderboard infrastructure to collect and visualize cost data"],"input_types":["Agent evaluation results (resolution rate, cost, steps)","Agent metadata (model, framework, configuration)"],"output_types":["Cost metrics per agent (total cost, average cost per instance, cost per resolved instance)","Step metrics per agent (total steps, average steps per instance, steps per resolved instance)","Visualizations: scatter plots (resolved vs. cost, resolved vs. steps), trend plots (cost over time)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swe-bench-verified__cap_6","uri":"capability://data.processing.analysis.per.repository.and.per.language.performance.breakdown","name":"per-repository and per-language performance breakdown","description":"Provides granular performance analysis by breaking down agent resolution rates by individual repository and by programming language (for Multilingual variant). The leaderboard includes visualizations for 'resolved by repository' and 'resolved by language', enabling identification of which repositories or languages are easier/harder for agents and revealing potential biases in benchmark composition or agent capabilities.","intents":["Identify which repositories have highest/lowest agent success rates to understand domain-specific challenges","Detect language-specific performance gaps in Multilingual variant (e.g., agents may perform better on Python than Go)","Analyze whether benchmark is balanced across repositories or biased toward certain codebases","Understand which types of code (by repository or language) agents struggle with most"],"best_for":["Researchers analyzing agent performance across different domains and programming languages","Benchmark organizers assessing benchmark balance and identifying potential biases","Teams developing language-specific or domain-specific coding agents"],"limitations":["Per-repository breakdown not documented — unclear how many repositories are included or how instances are distributed","Per-language breakdown only available for Multilingual variant (separate from Verified) — cannot compare language performance on same agent","No analysis of why certain repositories/languages have higher/lower success rates — cannot determine if differences are due to agent capability or issue difficulty","Repository selection bias not addressed — 'popular' repositories may not represent typical codebases","No per-repository or per-language confidence intervals — cannot determine if performance differences are statistically significant"],"requires":["Repository metadata (name, language, domain) for each instance","Leaderboard infrastructure to aggregate and visualize per-repository and per-language metrics"],"input_types":["Agent evaluation results with per-instance repository and language labels","Repository metadata"],"output_types":["Per-repository resolution rates (% resolved per repository)","Per-language resolution rates (% resolved per language, Multilingual variant only)","Visualizations: heatmaps (resolved instances matrix), bar charts (resolved by repository/language)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swe-bench-verified__cap_7","uri":"capability://data.processing.analysis.temporal.trend.analysis.and.model.release.date.correlation","name":"temporal trend analysis and model release date correlation","description":"Tracks agent performance over time and correlates resolution rates with model release dates, enabling analysis of how agent capability improves as new models and architectures are developed. The leaderboard includes visualizations for 'resolved vs. model release date', showing the relationship between model recency and benchmark performance.","intents":["Analyze whether newer models consistently achieve higher resolution rates","Identify inflection points where agent capability makes significant jumps","Predict future agent capability based on historical trends","Assess whether benchmark is saturating or has room for improvement"],"best_for":["Researchers tracking progress in AI coding agents over time","Model providers understanding how their model releases impact benchmark performance","Benchmark organizers assessing whether benchmark is saturating"],"limitations":["Temporal trend analysis limited to leaderboard history (since 03/2024) — insufficient data for long-term trend analysis","Model release date correlation not documented — unclear how release dates are determined or if all agents have documented release dates","No analysis of causality — cannot determine if performance improvements are due to model improvements or agent architecture improvements","Potential for survivorship bias — only agents that were evaluated and submitted to leaderboard are included","No confidence intervals or statistical significance testing — cannot determine if trends are meaningful or due to variance"],"requires":["Model release date metadata for each agent","Leaderboard infrastructure to track performance over time","Sufficient historical data (currently limited to ~1 year of leaderboard history)"],"input_types":["Agent evaluation results with timestamps","Model release dates"],"output_types":["Temporal trend plots (resolution rate over time)","Scatter plots (resolved vs. model release date)","Trend analysis (e.g., average improvement per month)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swe-bench-verified__cap_8","uri":"capability://automation.workflow.open.source.benchmark.infrastructure.and.local.evaluation.support","name":"open-source benchmark infrastructure and local evaluation support","description":"SWE-bench is open-source and supports local evaluation of custom agents without relying on centralized leaderboard submission. The benchmark infrastructure (Docker-based evaluation, test validation, metrics computation) is publicly available, enabling researchers to run evaluations on their own machines and reproduce results. This open-source approach contrasts with proprietary benchmarks and enables community contributions and extensions.","intents":["Run local evaluations of custom agents without submitting to public leaderboard","Reproduce published results by running benchmark locally","Extend benchmark with custom instances or evaluation logic","Contribute improvements to benchmark infrastructure"],"best_for":["Research teams developing custom agents that may not be ready for public leaderboard submission","Organizations with privacy requirements that prevent submitting results to public leaderboard","Researchers wanting to modify benchmark evaluation logic or add custom metrics","Community members contributing improvements to benchmark infrastructure"],"limitations":["Local evaluation requires significant setup effort — Docker, dependencies, test suite configuration","Evaluation time and computational cost not documented — cannot budget resources for local runs","No documentation of how to extend benchmark with custom instances or metrics","Reproducibility depends on exact Docker image versions and dependency versions — potential for environment drift over time","No official support or SLA for local evaluation — community-driven support only"],"requires":["Docker runtime","Python 3.x environment","Repository-specific dependencies (language runtimes, build tools, test frameworks)","Agent framework compatible with SWE-bench evaluation interface","Significant computational resources (CPU, memory, disk) for full benchmark runs"],"input_types":["SWE-bench benchmark instances (GitHub issues, repository code)","Agent implementation"],"output_types":["Local evaluation results (resolution rate, cost, steps)","Test execution logs and artifacts"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swe-bench-verified__cap_9","uri":"capability://code.generation.editing.multi.language.support.via.multilingual.variant","name":"multi-language support via multilingual variant","description":"The Multilingual variant (300 instances across 9 programming languages) extends SWE-bench beyond Python to evaluate agent capability across different languages. This variant maintains the same task structure (resolve GitHub issues via code modification) but includes instances from repositories in languages like JavaScript, Java, Go, C++, Rust, and others, enabling evaluation of language-agnostic agent architectures.","intents":["Evaluate whether agents trained primarily on Python can generalize to other programming languages","Compare agent performance across different languages to identify language-specific challenges","Assess whether language-specific agents (e.g., JavaScript-focused) outperform general agents","Develop and test language-agnostic agent architectures"],"best_for":["Teams developing multi-language coding agents","Researchers studying language generalization in AI coding systems","Model providers evaluating cross-language capability"],"limitations":["Multilingual variant is separate from Verified subset — cannot directly compare language performance on same agent","Only 300 instances total across 9 languages — ~33 instances per language on average, potentially insufficient for reliable per-language metrics","Language distribution not documented — unclear if instances are evenly distributed or biased toward certain languages","Language-specific test frameworks and build tools may not be fully supported in Docker environment","No analysis of language-specific challenges or failure modes","Smaller dataset than Verified (500) or Full (2,294) — higher variance and lower statistical power"],"requires":["Agent framework capable of handling multiple programming languages","Language-specific test frameworks and build tools (pytest for Python, Jest for JavaScript, JUnit for Java, etc.)","Docker images with language-specific runtimes and dependencies"],"input_types":["GitHub issues from repositories in 9 programming languages","Repository source code in multiple languages"],"output_types":["Per-language resolution rates","Overall resolution rate across all languages","Per-language breakdown of agent performance"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"swe-bench-verified__headline","uri":"capability://testing.quality.ai.coding.agent.evaluation.benchmark","name":"ai coding agent evaluation benchmark","description":"SWE-bench Verified is a benchmark that evaluates AI coding agents on their ability to resolve real-world software engineering tasks using human-verified GitHub issues, providing a reliable assessment for developers seeking effective AI solutions.","intents":["best AI coding benchmark","benchmark for evaluating AI coding agents","AI coding agent performance evaluation","real-world coding task benchmark","GitHub issue resolution benchmark"],"best_for":["evaluating AI coding solutions","assessing AI performance on real tasks"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":62,"verified":false,"data_access_risk":"high","permissions":["Docker runtime for sandboxed code execution","Python 3.x environment with repository-specific dependencies","Access to GitHub issue data and corresponding repository code","Test suite execution capability within sandboxed environment","Agent framework capable of multi-step reasoning and code modification (e.g., SWE-agent, mini-SWE-agent)","Agent framework capable of handling the specific variant's requirements (e.g., vision model for Multimodal)","Docker runtime for all variants (code execution in sandboxed environment)","Repository-specific dependencies for each instance (Python packages, language runtimes, build tools)","Computational budget proportional to variant size (Lite: ~300 evaluations, Full: ~2,294 evaluations)","Agent framework with vision capabilities (multimodal LLM, vision encoder)"],"failure_modes":["Binary metric with no partial credit — agents receive 0% for incomplete solutions even if they make significant progress toward resolution","Python-only for Verified subset (500 instances); 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