Leveraging LLMs for Automatic Feature Extraction in Embedded Systems to Support Software Reuse
Bibliographic record
Abstract
Embedded systems often accumulate complex, monolithic code, making identifying and reusing functionality across projects difficult. This paper presents an automated approach for extracting reusable software features from embedded C code using six pre-trained large language models (CodeBERT, GraphCodeBERT, CodeT5, CodeGPT-small, PLBART, and UniXcoder). The system embeds code-level features (e.g., functions and global variables) and textual artifacts (e.g. comments, control-flow conditions, and README entries) into a shared semantic vector space. Cosine similarity matches each feature with its most relevant textual artifact, and a coherence score is assigned to capture the strongest alignment across models. High-confidence matches are then clustered using unsupervised learning to reveal logical subsystems within the codebase. We evaluate the method on a heating system controller by manually curating 14 feature-requirement description pairs. Each feature is a functional unit selected from the code, and each description summarizes its intended behavior. These pairs serve as the benchmark for assessing extraction performance. Our method successfully traced features with 86% recall, using conservative thresholding that accepts only semantically strong matches. Clustering based on coherence scores produced interpretable groupings corresponding to logical subsystems such as heating control, safety monitoring, and user interface. Compared to single-model baselines (with recall as low as 22%), the multimodel ensemble method significantly improved coverage by leveraging the semantic diversity of different LLM models. These results demonstrate the feasibility of combining diverse pretrained embeddings with multiple textual sources to support feature extraction and modular reuse in embedded software.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".