You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Copy file name to clipboardExpand all lines: _posts/2025-01-08-how-google-uses-ai-for-code-migrations-insights-for-legacy-system-modernization.md
+52-28Lines changed: 52 additions & 28 deletions
Display the source diff
Display the rich diff
Original file line number
Diff line number
Diff line change
@@ -1,5 +1,5 @@
1
1
---
2
-
title: "2025-01-08-How Google Uses AI for Code Migrations: Insights for Legacy
2
+
title: "How Google Uses AI for Code Migrations: Insights for Legacy
3
3
System Modernization"
4
4
description: Google employs Large Language Models (LLMs) to automate large-scale
5
5
code migrations, reducing manual intervention and accelerating timelines.
@@ -10,7 +10,6 @@ author: Shyam Mohan
10
10
category: AIML
11
11
date: 2025-01-08T00:51:00.000Z
12
12
---
13
-
### **How Google Uses AI for Code Migrations: Detailed Insights for Legacy System Modernization**
14
13
15
14
Over the weekend, I delved into Google’s research paper on utilizing **AI for internal code migrations**, which is packed with fascinating insights into modernizing legacy systems. The strategies highlighted in the paper offer a comprehensive framework for addressing challenges related to outdated codebases, technical debt, and system upgrades. In this blog, I will explore these insights in detail and discuss how organizations can leverage similar approaches for modernization.
16
15
@@ -31,9 +30,11 @@ This synergy of AI and engineering tools demonstrates the importance of building
31
30
#### **3. Reusable Migration Workflows**
32
31
Google has developed **modular workflows** for its code migrations, enabling them to standardize and reuse processes across projects. By building a library of customizable tools and prompts for LLMs, they’ve streamlined recurring migration tasks such as:
33
32
34
-
- Framework updates (e.g., JUnit3 to JUnit4)
35
-
- API deprecations
36
-
- Library migrations (e.g., Joda Time to Java Time)
33
+
1) Framework updates (e.g., JUnit3 to JUnit4)
34
+
35
+
2) API deprecations
36
+
37
+
3) Library migrations (e.g., Joda Time to Java Time)
37
38
38
39
These workflows allow teams to onboard new projects quickly and ensure consistent results across diverse product areas. Organizations facing frequent modernization challenges can benefit from adopting a similar toolkit approach to reduce overhead and enhance efficiency.
39
40
@@ -50,8 +51,9 @@ For organizations handling complex migrations, this controlled rollout strategy
50
51
#### **6. Strategic Use of AI Models**
51
52
Google employs a combination of **custom fine-tuned AI models** and general-purpose tools, tailoring their approach based on the complexity of the task. For instance:
52
53
53
-
-**Fine-tuned models** are used for domain-specific migrations (e.g., Ads code migrations).
54
-
-**General-purpose models** handle simpler, repetitive tasks across different teams.
54
+
**1) Fine-tuned models** are used for domain-specific migrations (e.g., Ads code migrations).
55
+
56
+
**2) General-purpose models** handle simpler, repetitive tasks across different teams.
55
57
56
58
This strategy provides flexibility and scalability, ensuring resources are allocated efficiently without compromising quality.
57
59
@@ -63,34 +65,48 @@ Here’s how organizations can implement Google’s approach to streamline their
63
65
64
66
#### **1. Leverage AI Tools**
65
67
Utilize AI models like LLMs to automate tedious tasks such as:
66
-
- Refactoring legacy code.
67
-
- Replacing deprecated libraries.
68
-
- Resolving dependencies.
68
+
69
+
1) Refactoring legacy code.
70
+
71
+
2) Replacing deprecated libraries.
72
+
73
+
3) Resolving dependencies.
69
74
70
75
These tools can dramatically reduce manual workloads and accelerate project timelines.
71
76
72
77
#### **2. Adopt a Hybrid Approach**
73
78
Combine AI-powered automation with engineering tools like ASTs to:
74
-
- Ensure the accuracy of transformations.
75
-
- Validate changes through static analysis.
76
-
- Safeguard critical systems from errors.
79
+
80
+
1) Ensure the accuracy of transformations.
81
+
82
+
2) Validate changes through static analysis.
83
+
84
+
3) Safeguard critical systems from errors.
77
85
78
86
#### **3. Build Modular Workflows**
79
87
Develop reusable workflows tailored to your organization’s modernization needs. For example:
80
-
- Create scripts for common migration patterns.
81
-
- Design prompts for AI tools that reflect your specific codebase.
88
+
89
+
1) Create scripts for common migration patterns.
90
+
91
+
2) Design prompts for AI tools that reflect your specific codebase.
82
92
83
93
#### **4. Align with Business Goals**
84
94
Define success metrics that focus on measurable business outcomes, such as:
85
-
- Faster time-to-market.
86
-
- Improved system performance.
87
-
- Cost savings from reduced technical debt.
95
+
96
+
1) Faster time-to-market.
97
+
98
+
2) Improved system performance.
99
+
100
+
3) Cost savings from reduced technical debt.
88
101
89
102
#### **5. Plan for Phased Rollouts**
90
103
Develop a roadmap for deploying AI-driven changes gradually. Include:
91
-
- Rigorous testing phases.
92
-
- Human reviews for high-risk updates.
93
-
- Monitoring mechanisms to track stability and performance.
104
+
105
+
1) Rigorous testing phases.
106
+
107
+
2) Human reviews for high-risk updates.
108
+
109
+
3) Monitoring mechanisms to track stability and performance.
94
110
95
111
#### **6. Tailor AI Models to Your Needs**
96
112
Evaluate when to invest in custom fine-tuned models versus using off-the-shelf AI tools. For complex migrations, customized models can provide better accuracy and context.
@@ -101,23 +117,31 @@ Evaluate when to invest in custom fine-tuned models versus using off-the-shelf A
101
117
102
118
Google’s paper highlights several case studies that demonstrate the effectiveness of their approach:
103
119
104
-
-**JUnit3 to JUnit4 Migration:** Google migrated 5,359 test files, modifying over 149,000 lines of code in just three months using their AI toolkit. This migration addressed long-standing technical debt and simplified future updates.
120
+
**1) JUnit3 to JUnit4 Migration:** Google migrated 5,359 test files, modifying over 149,000 lines of code in just three months using their AI toolkit. This migration addressed long-standing technical debt and simplified future updates.
105
121
106
-
-**Joda Time to Java Time Migration:** Tackling one of their largest migrations, Google used LLMs to replace outdated Joda Time APIs with Java Time. By clustering related changes and automating validation, they achieved significant time savings while ensuring code quality.
122
+
**2) Joda Time to Java Time Migration:** Tackling one of their largest migrations, Google used LLMs to replace outdated Joda Time APIs with Java Time. By clustering related changes and automating validation, they achieved significant time savings while ensuring code quality.
107
123
108
-
-**Experimental Flag Cleanup:** Google’s AI-powered tools removed thousands of stale experimental flags, reducing technical debt and improving code maintainability.
124
+
**3) Experimental Flag Cleanup:** Google’s AI-powered tools removed thousands of stale experimental flags, reducing technical debt and improving code maintainability.
109
125
110
126
---
111
127
112
128
### **Conclusion: The Future of Modernization with AI**
113
129
114
130
Google’s innovative use of AI for code migrations provides a compelling blueprint for modernizing legacy systems. By combining AI-driven efficiency with engineering best practices, organizations can achieve:
115
-
- Faster migrations.
116
-
- Safer deployments.
117
-
- Greater alignment with business goals.
131
+
132
+
1) Faster migrations.
133
+
134
+
2) Safer deployments.
135
+
136
+
3) Greater alignment with business goals.
118
137
119
138
Whether you’re facing outdated frameworks, complex dependencies, or technical debt, Google’s strategies offer actionable insights to tackle these challenges effectively. For a deeper understanding, I’ve attached the full research paper—let’s discuss how these ideas can be applied to your projects!
120
139
140
+
<br>
141
+
142
+
## [Click here to read Google.com white pager](https://codecrux.com/How-is-Google-using-AI-for-internal-code-migrations.pdf)
143
+
121
144
145
+
<br>
146
+
<br>
122
147
123
-
## Click here to read Google.com white pager https://codecrux.com/How-is-Google-using-AI-for-internal-code-migrations.pdf
0 commit comments