New Rules for Estimating Software Development Time in AI-era

Introduction

Traditional software estimation techniques, like COCOMO, Function Point Analysis and expert judgment, relied on parameters such as lines of code, complexity, team experience and project scope to predict development time. These methods assumed human-driven development processes with predictable task durations. However, AI advancements, particularly in generative AI and automation tools, are reshaping how software development time is estimated by introducing new dynamics and parameters. Below, I outline the impact of AI on estimation, new parameters to consider and whether new formulae have emerged.

Impact of AI Advancements on Software Estimation

AI tools, such as GitHub Copilot, large language models (LLMs) for code generation, and automated testing frameworks, accelerate coding, debugging and testing. This reduces the time required for certain tasks but introduces variability that traditional models struggle to account for.

Key impacts include:

• Increased Productivity: AI can generate boilerplate code, suggest optimizations and automate repetitive tasks, reducing development time by 20–50% for specific tasks, according to studies like those from McKinsey in 2023
• Shift in Effort Distribution: AI shifts effort from coding to higher-level tasks like requirements analysis, prompt engineering and validation of AI-generated outputs
• Uncertainty in AI Reliability: AI tools may produce inconsistent results, requiring additional time for review and correction, which complicates estimation
• Reduced Team Size Dependency: AI can augment smaller teams, making traditional parameters like team size less predictive of project duration

So have new parameters emerged?

Yes.

New Parameters to Consider

AI-driven development introduces parameters that traditional models didn’t emphasize. These include:

1. AI Tool Proficiency: The team’s familiarity with AI tools (e.g., Microsoft’s Copilot, Google’s Gemini and Amazon’s Code Whisperer) and their ability to craft effective prompts significantly affect productivity
2. Task Suitability for AI: Not all tasks benefit equally from AI. For instance, algorithmic problem-solving or creative UI design may see less AI impact than CRUD applications. Estimators must assess which tasks can be AI-accelerated
3. AI Output Quality: The accuracy and reliability of AI-generated code or artifacts vary by tool and context, impacting review and refactoring time
4. Integration Complexity: AI-generated code may require additional effort to integrate with existing systems, especially in legacy environments. Retro-fitting AI in legacy systems consumes roughly 25–30% of developer’s time (conservatively)
5. Learning Curve: Teams adopting AI tools face an initial learning curve, which may temporarily inflate estimates
6. Ethical and Compliance Overhead: AI-generated code may need extra scrutiny for security, bias or licensing issues, adding time to validation phases
7. Infrastructure Dependence: Access to high-performance computing resources (e.g., GPUs for running LLMs) can affect development speed, especially for on-premises AI deployments

Has a New Formula Emerged?

No widely adopted, standardized formula for AI-driven software estimation has emerged as of April 2025, based on available information. However, research and industry practices are evolving to incorporate AI’s impact:

• Modified COCOMO Variants: Some organizations adapt COCOMO by adjusting effort multipliers to account for AI productivity gains. For instance, a 2023 study proposed a COCOMO extension incorporating an “AI augmentation factor” (AAF) ranging from 0.5 to 1.5, where lower values reflect higher AI productivity. The formula becomes:

Effort=a⋅(KLOC)b⋅EAF⋅AAF

where,

KLOC — is thousands of lines of code

EAF — is the effort adjustment factor and

AAF — reflects AI tool impact. However, this is not yet standardized

• Machine Learning-Based Estimation: Some teams use ML models trained on historical project data to predict effort, incorporating AI-specific parameters like tool usage and task automation levels. These models outperform traditional formulae but are organization-specific and lack universal applicability
• Agile and Iterative Approaches: Many teams shift away from formulaic estimation, relying on agile sprints with real-time feedback. AI’s impact is assessed empirically by tracking velocity improvements in AI-assisted sprints
• Custom Heuristics: Organizations develop bespoke heuristics, such as estimating a 30–40% time reduction for AI-assisted coding tasks, based on internal experiments. These are not formalized into universal models

Challenges and Gaps

• Lack of Standardization: Unlike COCOMO or function points, no consensus exists on how to quantify AI’s impact across diverse projects
• Data Scarcity: Historical data on AI-assisted projects is limited, hindering the development of robust models
• Dynamic AI Evolution: Rapid improvements in AI tools (e.g., newer LLMs) make static estimation models obsolete quickly

Recommendations for Estimation in the AI Era

1. Hybrid Approach: Combine traditional methods (like function points) with AI-specific adjustments based on empirical data from past projects
2. Task Segmentation: Break projects into AI-suitable and non-AI-suitable tasks, estimating each separately
3. Pilot Projects: Run small AI-assisted projects to calibrate productivity gains before scaling estimates
4. Continuous Monitoring: Use agile metrics (e.g., sprint velocity) to refine estimates as teams gain AI proficiency
5. Tool Benchmarking: Evaluate AI tools’ performance on representative tasks to quantify their impact on effort

Conclusion

AI advancements are transforming software development estimation by introducing new parameters like tool proficiency, task suitability, and output quality, while reducing reliance on traditional factors like lines of code. No universal formula has emerged, but modified models like COCOMO with AI augmentation factors and ML-based approaches show promise. For now, organizations should adopt hybrid, data-driven methods and agile practices to navigate the evolving landscape of AI-assisted development.

If you liked my article, consider giving it a few claps & follow me on https://www.linkedin.com/in/toashishagarwal15/.