Toward Semantically-Seeded, Graph-Propagated Impact Analysis Across Software Artifacts: A Vision

2023-03-08 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Expert, long

Summary

A training-free, interpretable impact analyzer is proposed that fuses semantic similarity and typed dependency propagation across heterogeneous software artifacts. This approach addresses limitations of existing change-impact-analysis (CIA) tools, which typically rely on either semantic similarity from text embeddings or structural dependencies from call graphs, each having characteristic blind spots. The system models software as a heterogeneous artifact graph with typed edges (e.g., Requirement → Config → Service → Test), computes a semantic prior using cosine similarity, propagates impact multi-hop with decay, and blends these signals with a tunable weight λ. A proof-of-concept on a payment subsystem (13 artifacts, 14 edges, 5 change scenarios) demonstrated that this fusion achieves perfect recall (1.000) and covers both semantic and structural blind spots, recovering artifacts with zero textual overlap and helper functions unreachable by propagation alone. The prototype, using a TF-IDF prior, reported a macro-averaged F1 of 0.883 for the λ=0.5 blend, outperforming pure semantic (0.566) and pure structural (0.849) baselines on this specific benchmark. The vision extends to operational artifacts like container images, database engines, metrics, and data schemas.

Key takeaway

For MLOps Engineers or Software Architects evaluating change impact analysis tools, you should consider solutions that fuse semantic and structural signals. This approach overcomes blind spots of single-signal tools, ensuring critical operational artifacts like container images or database schemas are not missed. Implement a training-free, interpretable analyzer to maintain auditability and explicitly control precision/recall with a tunable λ parameter. This enhances reliability in complex, evolving systems.

Key insights

Fusing semantic similarity and structural propagation in a training-free, interpretable analyzer overcomes blind spots in change impact analysis.

Principles

• Fuse semantic and structural signals for comprehensive impact analysis.
• Model systems as heterogeneous artifact graphs for broader scope.
• Prioritize training-free, interpretable methods for auditability.

Method

Model system as a typed artifact graph. Compute semantic prior via cosine similarity. Propagate impact multi-hop with decay. Blend signals with a tunable weight λ for combined impact score.

In practice

• Extend analysis to operational artifacts (images, metrics, data schemas).
• Use λ to balance precision and recall for specific scenarios.
• Recover explicit propagation paths for audit and explanation.

Topics

• Change Impact Analysis
• Semantic Similarity
• Graph Propagation
• Heterogeneous Graphs
• Software Artifacts
• Operational Artifacts

Code references

• momil-seedat/artifact-impact-lab

Best for: Research Scientist, AI Scientist, Software Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.