This guide provides an observable and quantifiable framework to measure the impact of adopting Tongyi Lingma on development efficiency, code quality, and developer experience.
This document describes a core framework that uses three dimensions to comprehensively and objectively evaluate the value of an Artificial Intelligence (AI) coding tool.
1. Core evaluation principles
Before you begin the evaluation, follow these three core principles to ensure objective and valid results.
Principle one: Use a multi-dimensional perspective, not a single metric
Combine data from multiple dimensions, such as development efficiency, code quality, and developer experience. A single metric can be misleading and cause you to overlook the value and potential issues of the AI tool. A comprehensive approach is necessary to obtain a complete picture of the tool's impact.
Principle two: Establish a baseline to dynamically measure changes
Before adopting the AI coding tool, collect and record your team's key metrics, such as code delivery cycle, output per person, and defect rate. This "before" state serves as your baseline. Compare all subsequent evaluations against this baseline to quantify the changes introduced by the AI tool.
Principle three: Focus on people and empower developers
A focus on people is key to successful tool adoption. Encourage developers to use the tool extensively and provide honest, valuable feedback. This feedback helps optimize the tool and contributes to establishing company-wide best practices.
2. Evaluation method: A three-dimensional quantitative evaluation model
The evaluation is based on the following three dimensions:
Dimension one: Changes in development efficiency
This dimension measures whether the tool helps the team "write more and deliver faster."
Metric | Calculation method | Interpretation and insights |
Effective code output per person | Average number of non-comment, non-blank lines of code per person, compared to the same period before adoption. | Core metric. Use this to observe macro trends in code volume, but it must be interpreted together with quality metrics. |
Code delivery cycle | Average time from when a task status changes to "In Progress" to "Ready for Testing", compared to the same period before adoption. | Supporting metric. Measures efficiency gains in the coding phase, excluding variables from other stages, such as requirements review and testing. |
Number of requirements delivered | Total number of requirements completed within the period, compared to the same period before adoption. | Supporting metric. This metric indicates whether the team is delivering more functional units. |
Cost per requirement delivered | Total development cost in the period / Total number of requirements completed in the period, compared to the same period before adoption. | Supporting metric. Directly links technical output to financial cost and can be used to measure return on investment (ROI). |
Dimension two: Changes in development quality
This dimension measures whether the code generated by the tool has "higher quality and is easier to maintain."
Metric | Calculation method | Interpretation and insights |
Code defect density | (Number of new bugs in production during the period / Thousands of new or changed lines of code in the same period), compared to the same period before adoption. The denominator should be the amount of code actually changed during the period, not the total size of the codebase. | Core metric. "Defects per KLOC" is a globally recognized gold standard for measuring the intrinsic quality of code. |
Code test coverage and quality | 1. Changes in unit test line/branch coverage. 2. Sample and evaluate the effectiveness of AI-generated test cases. | Supporting metric. Use code reviews to spot-check and evaluate whether tests are effective. This prevents the generation of meaningless tests just to increase coverage. |
Code review efficiency | Average number of comments, review duration, and first-pass acceptance rate per merge request (MR/PR), compared to the same period before adoption. | Supporting metric. Measures whether AI-generated code is easier to understand and maintain. |
Dimension three: Developer experience
This dimension measures whether the tool is "popular and genuinely useful."
Metric | Calculation method | Interpretation and insights |
Tool activity rate | Average daily number of active developers using the tool / Total number of developers in the team. | Core metric. Measures the tool's popularity and the effectiveness of its rollout. |
Developer satisfaction survey | Conduct an anonymous survey. Sample questions:
| Systematically collect developers' subjective feelings about efficiency, quality, and mental load. |
In-depth qualitative interviews | Conduct one-on-one interviews with developers of different experience levels. Interview outline:
| Uncover the stories and reasons behind the data. Collect specific success and failure cases. This provides direct input for tool optimization and helps establish internal best practices. Use these findings to promote effective development practices and empower the team. |
3. Case study
Background: Hello Inc. successfully integrated Tongyi Lingma into its development workflow. Through a gradual rollout, the company achieved significant improvements in efficiency, quality, and developer experience.
Core conclusion: The results demonstrated a positive correlation between the scale of AI adoption and code output. While efficiency increased, the code defect rate gradually decreased. The tool empowered developers with cross-technology-stack capabilities, improved code comprehension and documentation completeness, and enhanced internal collaboration.
Key results in numbers:
Efficiency improvements
42% year-over-year increase in code output efficiency
58% year-over-year increase in requirement delivery efficiency
Quality improvements
0.54% code defect rate, an improvement from 0.62% in the same period last year
Overall capability improvements
Code quality: More standardized naming and fewer basic mistakes.
Documentation completeness: AI assistance encouraged developers to write more comments and documentation.
Employee skills: Junior engineers could become productive faster, and the barrier to cross-technology-stack development was lowered.
Conclusion
To effectively evaluate the value of Tongyi Lingma:
Establish multi-dimensional evaluation principles with a clear baseline.
Use the "efficiency-quality-experience" three-dimensional model to make data-driven decisions for management and operations.
Combine data with developer feedback, create positive incentives, and improve the team's overall AI coding practices.
We hope this guide helps your team better embrace AI and unlock greater development potential.