SEER: Spectral Entropy Encoding of Roles for Context-Aware Attention-Based Design Pattern Detection

• URL: http://arxiv.org/abs/2601.13334v1
• Date: Mon, 19 Jan 2026 19:13:40 GMT
• Title: SEER: Spectral Entropy Encoding of Roles for Context-Aware Attention-Based Design Pattern Detection
• Authors: Tarik Houichime, Younes El Amrani,
• Abstract summary: This paper presents an upgraded version of our prior method Context Is All You Need for detecting Gang of Four (GoF) design patterns from source code.<n> SEER addresses these limitations with two principled additions: (i) a spectral-entropy role encoder that derives per-member role embeddings from the Laplacian spectrum of each class's interaction graph, and (ii) a time-weighted calling context that assigns empirically calibrated duration priors to method categories.<n>We evaluate SEER on PyDesignNet (1,832 files, 35,000 sequences, 23 GoF patterns) and observe consistent gains over our previous system
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper presents SEER, an upgraded version of our prior method Context Is All You Need for detecting Gang of Four (GoF) design patterns from source code. The earlier approach modeled code as attention-ready sequences that blended lightweight structure with behavioral context; however, it lacked explicit role disambiguation within classes and treated call edges uniformly. SEER addresses these limitations with two principled additions: (i) a spectral-entropy role encoder that derives per-member role embeddings from the Laplacian spectrum of each class's interaction graph, and (ii) a time-weighted calling context that assigns empirically calibrated duration priors to method categories (e.g., constructors, getters/setters, static calls, virtual dispatch, cloning). Together, these components sharpen the model's notion of "who does what" and "how much it matters," while remaining portable across languages with minimal adaptation and fully compatible with Transformer-based sequence encoders. Importantly, SEER does not "force" a win by capacity or data; it nudges the classifier, steering attention toward role-consistent and temporally calibrated signals that matter most. We evaluate SEER on PyDesignNet (1,832 files, 35,000 sequences, 23 GoF patterns) and observe consistent gains over our previous system: macro-F1 increases from 92.47% to 93.20% and accuracy from 92.52% to 93.98%, with macro-precision 93.98% and macro-recall 92.52%. Beyond aggregate metrics, SEER reduces false positives by nearly 20%, a decisive improvement that strengthens its robustness and practical reliability. Moreover, SEER yields interpretable, symbol-level attributions aligned with canonical roles, exhibits robustness under small graph perturbations, and shows stable calibration.

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