Enterprise AI Analysis
Embedding Software Intent: Lightweight Java Module Recovery
Traditional software architecture recovery struggles with maintaining consistency and scalability. The Java Platform Module System (JPMS) addresses this by enabling explicit module specification. ClassLAR, a novel lightweight approach, leverages language models and undersized module repair to recover Java modules from monolithic systems using only fully-qualified class names. This innovation significantly improves architectural resemblance and efficiency.
Executive Impact: Quantifiable Gains for Your Enterprise
ClassLAR's approach delivers tangible improvements in software architecture recovery, directly translating to enhanced development efficiency, reduced technical debt, and optimized system performance. Here's a quick look at the core benefits:
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Max Improvement in Architectural Similarity (a2a)
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Times Faster Recovery Speed
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Improvement in Module Completeness (c-score)
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Lead in Modularization Quality (MQ) over most techniques
Deep Analysis & Enterprise Applications
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Core Methodology
Performance & Impact
Ablation & Granularity
ClassLAR: Lightweight Java Module Recovery
ClassLAR introduces a novel, lightweight, and efficient approach to recover Java modules from monolithic systems using only fully-qualified class names. By leveraging language models (LMs) to extract semantic information from package and class names, ClassLAR captures both structural and functional intent, making the process highly accurate without requiring complex code analysis.
Enterprise Process Flow
Extract Fully-Qualified Class Names
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Generate Semantic Vectors (LM)
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Unsupervised Clustering (HDBSCAN)
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Undersized Module Repair
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Recovered Java Modules
The recovery process culminates in the creation of well-encapsulated Java modules that strongly resemble developer-created modules, reducing architectural decay and improving system maintainability.
Unmatched Performance in Module Recovery
ClassLAR consistently outperforms state-of-the-art architecture recovery techniques across various metrics, demonstrating superior accuracy and efficiency. This makes it an ideal solution for modernizing legacy Java applications.
20.63pp
Maximum Percentage Point Improvement in Architectural Similarity (a2a score)
ClassLAR significantly enhances the resemblance of recovered architectures to developer-created modules, providing a clear path to maintainable, modular systems.
In addition to superior similarity scores, ClassLAR also exhibits remarkable runtime efficiency:
- 3.99 to 10.50 times faster than existing techniques, enabling rapid architecture analysis for large projects.
Below is a comparative overview of ClassLAR against other techniques, highlighting its leading position:
| Metric | ACDC | SARIF | ClassLAR |
|---|---|---|---|
| a2a (Architectural Similarity) | 70.73% | 74.32% | 85.78% |
| c-score (Module Completeness) | 35.60% | 36.88% | 56.27% |
| h-score (Module Homogeneity) | 59.07% | 30.74% | 77.44% |
| MQ (Modularization Quality) | 8.11% | 19.28% | 15.61% |
While SARIF shows a higher raw MQ, this is often inflated by producing fewer, larger modules. ClassLAR still maintains a 7.5 pp lead in MQ over most other techniques, demonstrating robust encapsulation for balanced modularity.
Understanding Key Components: Ablation & Input Granularity
To optimize ClassLAR's effectiveness, a detailed ablation study was conducted, revealing the critical roles of its components and input granularity.
Critical Role of UMR and Language Models
Removing Undersized Module Repair (UMR) significantly degrades performance:
- Encapsulation (MQ) worsens by 10.13 pp.
- Architectural similarity (a2a) drops by 4.34 pp.
- Module completeness (c-score) falls by 11.49 pp.
This highlights UMR's necessity for consolidating fragmented clusters and ensuring high-quality module boundaries, despite a minor trade-off in h-score.
Replacing the LM embedding model with a traditional LDA model also negatively impacts all metrics, reinforcing that Language Models (LMs) are crucial for encoding rich semantic information essential for Java module recovery.
The study also found that ClassLAR's performance is sensitive to input granularity. Both including complete source code and reducing input to only package names resulted in degradation. This confirms that fully-qualified class names are the optimal input, providing the right balance of semantic information without introducing excessive noise.
These findings underscore the importance of ClassLAR's design choices in achieving its superior performance, emphasizing that lightweight, semantically rich inputs combined with intelligent repair mechanisms are key to effective Java module recovery.
Calculate Your Potential ROI with ClassLAR
Discover the significant operational efficiencies and cost savings your organization could achieve by implementing ClassLAR for Java module recovery. Input your team's details below to get a personalized estimate.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your ClassLAR Implementation Roadmap
Our structured approach ensures a smooth integration of ClassLAR into your development workflow, delivering value at every step. This timeline outlines a typical engagement:
Phase 1: Initial Assessment & Data Preparation
We begin by analyzing your existing monolithic Java systems and preparing the fully-qualified class names for ClassLAR processing.
Phase 2: Model Integration & Tuning
ClassLAR's language models are integrated and fine-tuned for optimal performance with your specific codebase characteristics.
Phase 3: Module Recovery & Validation
The module recovery process is executed, and the resulting architectural modules are rigorously validated against your existing structures and requirements.
Phase 4: Integration & Continuous Improvement
Recovered modules are integrated into your JPMS environment. We establish a framework for ongoing monitoring and architectural maintenance, ensuring long-term benefits.
Ready to Transform Your Java Architecture?
Don't let architectural decay hinder your enterprise's innovation. Partner with us to leverage ClassLAR for efficient, accurate, and scalable Java module recovery. Book a complimentary consultation to explore how our expertise can drive your software modernization initiatives.
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