AI Case Studies in Practice: Understanding and Optimizing a Legacy System Without Documentation

Explore More AI Use Cases

Each scenario reflects real-world pressure points where applied AI delivers measurable impact across engineering workflows.

Case 1: Handling a Late Architecture Change Before Release
Case 2: When Secure Communication Breaks. Resolving a Critical Java Issue
Case 3: Delivering a Complex Software Analysis Under Severe Time Constraints
Case 4: Understanding and Optimizing a Legacy System Without Documentation

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Business Context

Legacy systems are one of the biggest risks in enterprise IT environments.

They are often poorly documented, difficult to modify, and any change can introduce instability.

When a team does not fully understand a legacy system, every change becomes slow, expensive and risky.

The ability to analyze and understand such a system faster has a direct impact on:

• system stability
• speed of future changes
• incident resolution time
• long-term maintenance costs

From a business perspective, faster understanding of legacy systems means:

• lower operational risk
• faster delivery of changes
• lower cost of maintaining legacy systems
• easier modernization in the future

This is why work with legacy systems is not just a technical topic, but a business risk topic.

Situation

• A legacy Java application had a critical issue when accessing IBM Storage Protect.
  It was necessary to adjust the connection pool and understand when and how the application accessed the database.

Why it was hard

• This was a legacy system without documentation.
  The team had to decompile the application, analyze database access points, SQL queries and configuration behavior.

How we used AI

• The application was decompiled and analyzed with AI support:
• AI helped identify configuration parameters
• AI identified database access points and SQL queries
• AI helped reconstruct the processing flow
• Based on this analysis, the connection pool configuration and database settings were optimized

Outcome

• Configuration parameters and database access points identified
• Connection pool issue resolved
• Database performance optimized
• Reverse engineering significantly accelerated compared to a standard approach

Delivery Impact – What this means for software delivery

• Legacy systems are often one of the biggest delivery risks because they are poorly documented and difficult to modify.
  AI made it possible to understand the system faster, identify the root cause and optimize performance.
  This reduces the risk associated with legacy systems and makes future changes faster and safer.

What made the difference

• Structured context
• Engineering experience
• Iterative approach
• Validation and testing
• Domain knowledge

AI significantly accelerated reverse engineering, but the key was the ability to interpret and apply the results correctly.