LLM Evaluation Checklist for Code Assistant Teams in 2026

Measure the percentage of generated code that compiles or parses without errors across all supported languages. This is the bare minimum quality bar. Track syntax correctness rates per language, as performance varies significantly across languages.

Write unit tests for common coding tasks and measure how often generated code passes them on the first attempt. Build a test suite of at least 200 coding tasks spanning algorithms, API usage, and data manipulation. Track pass rates by task category.

Test code generation on tasks extracted from actual development workflows, not just competitive programming problems. Include tasks like API integration, database queries, configuration, and data transformation. Real-world tasks reveal weaknesses that synthetic benchmarks miss.

Evaluate whether generated code handles edge cases: null inputs, empty collections, boundary values, Unicode characters, and concurrent access. LLMs often generate happy-path code that fails on edge cases. Test with a dedicated edge case dataset per language.

Separately measure accuracy for inline completions (completing a partial line), block completions (completing a function body), and full generation (writing from a docstring). Each mode has different accuracy profiles. Set separate quality thresholds for each.

Test whether the assistant correctly uses imported libraries, existing functions, defined types, and project conventions when generating code. Code that ignores the surrounding context feels AI-generated and requires manual fixes. Evaluate context utilization accuracy.

Verify that code generated across multiple files maintains consistent interfaces, naming conventions, and architectural patterns. Inconsistency across files creates integration headaches. Test with multi-file generation tasks.

Evaluate the quality of suggested refactorings: do they preserve behavior, improve readability, and follow language idioms? Bad refactoring suggestions erode developer trust. Test with 50+ refactoring scenarios including complex cases.

For typed languages, measure whether generated code includes correct and useful type annotations. Incorrect types cause compile errors; overly broad types (e.g., any) reduce type safety. Evaluate type precision and recall separately.

Assess whether generated code follows language-specific idioms and best practices. Pythonic Python, idiomatic Go, and modern JavaScript patterns indicate higher quality. Rate idiom adherence on a rubric per language.

Scan all generated code for OWASP Top 10 vulnerabilities: injection, broken authentication, sensitive data exposure, XXE, broken access control, and more. Use automated security analysis tools and track vulnerability density per 1000 lines of generated code.

Test whether the code assistant generates parameterized queries versus string-concatenated SQL. Use test prompts that describe database operations in various ways and verify parameterization. Any SQL injection vulnerability in generated code is a critical failure.

Verify that generated code never hardcodes secrets, API keys, passwords, or tokens. The assistant should suggest environment variables, secret managers, or configuration files. Test with prompts that could lead to hardcoded secrets.

Evaluate whether generated code includes appropriate input validation and sanitization. Code that trusts user input is inherently vulnerable. Test with prompts for user-facing features and measure input validation coverage.

When the assistant suggests third-party libraries, verify it recommends actively maintained packages without known critical CVEs. Suggesting deprecated or vulnerable packages introduces supply chain risk. Cross-reference suggestions against vulnerability databases.

Evaluate whether generated auth code follows security best practices: proper password hashing, token expiration, CSRF protection, and role-based access control. Insecure auth patterns are among the most dangerous code generation failures.

Verify that generated cryptographic code uses secure algorithms, proper key management, and correct implementations. LLMs frequently suggest deprecated algorithms (MD5, SHA1) or implement crypto incorrectly. Flag all crypto suggestions for security review.

Test generated frontend code for XSS vulnerabilities including improper HTML escaping, unsafe innerHTML usage, and missing Content Security Policy headers. Evaluate across React, Vue, and vanilla JavaScript contexts.

Verify that generated error handling does not leak sensitive information like stack traces, database schemas, or internal paths. Error messages should be user-friendly in production. Test error handling patterns across 30+ scenarios.

If the assistant flags potential security issues, measure the precision and recall of these flags. Too many false positives cause alert fatigue; missed vulnerabilities create real risk. Calibrate flagging thresholds with security team input.

Measure time-to-first-suggestion for inline completions under real-world conditions. Developers expect suggestions within 200-400ms; anything above 500ms breaks flow. Track P50, P95, and P99 latencies across different completion types.

Benchmark the runtime performance of generated code against hand-written alternatives for equivalent tasks. LLMs sometimes generate algorithmically inefficient code (O(n^2) instead of O(n log n)). Test with performance-critical tasks.

Evaluate memory allocation patterns in generated code, especially for data processing and file handling tasks. LLMs may generate code that loads entire files into memory instead of streaming. Profile memory usage on realistic data volumes.

Measure the percentage of suggestions that developers accept versus dismiss. Low acceptance rates indicate poor suggestion quality. Track acceptance rates by language, completion type, and time of day to identify patterns.

Test how the amount of surrounding code context affects suggestion quality. More context generally improves suggestions but increases latency and cost. Find the optimal context window size for your latency and quality requirements.

Verify that the code assistant does not degrade IDE performance: no UI freezes, no excessive memory consumption, and no network timeouts. Developer experience depends on IDE responsiveness. Test under low-bandwidth and high-latency network conditions.

If using streaming for longer code generation, evaluate partial response quality. Early tokens should be usable even if the full response changes. Poor streaming can cause visual jank and confuse developers mid-editing.

When generating database queries, evaluate query plans and execution performance. LLMs commonly generate N+1 queries, missing indexes, or unnecessary subqueries. Test generated queries against representative data volumes.

Load test the code assistant backend under concurrent user counts matching peak usage. Measure latency degradation and error rates as user count scales. Plan infrastructure scaling based on results.

Test code assistant behavior during network outages or API degradation. The experience should degrade gracefully, not crash or show cryptic errors. Measure time to detect and communicate connectivity issues.

Scan generated code for segments that closely match GPL, AGPL, or other copyleft-licensed code that would impose licensing obligations on your product. Use code similarity tools to compare against known open-source repositories. Any copyleft match requires legal review.

Identify when generated code requires attribution under permissive licenses (MIT, Apache, BSD). Even permissive licenses have attribution requirements that must be met. Track attribution obligations for all generated code entering production.

Verify that your code assistant respects repository opt-out mechanisms (e.g., .gitignore patterns, robots.txt) and does not reproduce code from repositories that have opted out of AI training. Document your compliance approach for legal review.

Test that the code assistant does not include proprietary code from your codebase in suggestions to other users or in transmitted telemetry. Internal code leakage is a serious IP risk. Audit data transmission and model fine-tuning pipelines.

When refactoring or modifying existing files, verify that the assistant preserves copyright notices, license headers, and attribution comments. Removing these creates legal compliance issues. Test with files containing various license headers.

Evaluate whether generated code implements algorithms or patterns covered by known software patents. While automated detection is limited, maintain a list of patent-sensitive areas for your domain and flag suggestions in those areas.

Implement systems to track the provenance of all generated code: which model, which prompt, and what context produced each suggestion. Provenance records are essential for license dispute resolution. Log provenance for all accepted suggestions.

When the assistant suggests code using third-party APIs, verify that the suggested usage patterns comply with those APIs' terms of service. Rate limit handling, data usage restrictions, and commercial use limitations all matter.

For organizations subject to export controls, verify that generated code does not include cryptographic or dual-use technology that requires export licenses. Screen suggestions against controlled technology lists relevant to your jurisdiction.

When generated code combines multiple third-party dependencies, verify license compatibility. Mixing incompatible licenses (e.g., GPL with proprietary) creates legal conflicts. Automate dependency license compatibility checking.

Conduct monthly surveys measuring developer satisfaction with code suggestions, covering accuracy, relevance, speed, and workflow integration. Track Net Promoter Score for the code assistant. Satisfaction trends predict adoption better than usage metrics.

Measure actual developer time savings using controlled experiments or before-after analysis on matched tasks. Self-reported time savings are unreliable. Target measurable time savings of at least 20% on supported task categories.

Evaluate suggestion relevance across different coding contexts: greenfield development, debugging, test writing, documentation, and maintenance. Performance often varies dramatically across contexts. Optimize for the highest-impact contexts first.

Measure time from initial access to productive use of the code assistant. If the learning curve exceeds 2 days, adoption will suffer. Provide onboarding materials and track feature discovery rates.

Track instances where the code assistant disrupts developer flow: unwanted pop-ups, slow responses, incorrect auto-completions that require undo. Disruptions accumulate frustration and drive developers to disable the tool.

Track which features developers actually use versus which they ignore. Unused features may indicate poor discoverability or low value. Focus evaluation efforts on the features with highest usage and highest potential impact.

Measure whether AI-generated code receives more, fewer, or different types of code review comments compared to human-written code. If AI code increases review burden, net productivity gains may be negative. Track review cycles per PR.

Compare bug rates in code that was AI-assisted versus manually written, controlling for task complexity. If AI-assisted code introduces more bugs, the time saved in writing is lost in debugging. Track over a 3-month window.

Analyze adoption patterns across teams, experience levels, and tech stacks. Identify champion teams and resistant teams. Understanding adoption barriers helps focus improvement efforts. Segment usage data by team characteristics.

Benchmark your code assistant against competing tools on key quality and feature dimensions. Developers will switch to better tools quickly. Conduct quarterly competitive evaluations on a standardized task set.