arXiv subjects

Kenta Nanaumi

Publications and source records attributed to Kenta Nanaumi.

2 recordsLinked to original sources

arXiv2026

Revision-Aware Success Prediction from Multi-Attempt Programming Trajectories

Programming outcome prediction plays a central role in data-driven programming education, supporting learner modeling, timely intervention, and adaptive assistance. Yet predicting submission success is difficult due to heterogeneous error states, short-term revisions, and uneven future-horizon availability in programming trajectories. This study examines three prediction tasks under a unified formulation: whether the current attempt is accepted (Task~1), whether the next attempt is accepted (Task~2), and whether acceptance is reached within a three-attempt recovery window (Task~3). Each task is evaluated across current-only, pairwise, and multi-step input regimes using ML, DL, and transformer-based pretrained models (PTM), represented by LinearSVM, XGBoost, BiGRU, BiLSTM, GraphCodeBERT, and CodeT5+. Results show a consistent pattern: the current-only regime is the most reliable, while pairwise and multi-step history provide no consistent gain. ML models are the strongest and most stable overall, particularly in Tasks~1 and~3, and Task~2 is the hardest across all model families. DL and PTMs perform well on Task~3 but are more task-dependent. In the Task~3 current-only setting, XGBoost achieves AP/PR-AUC of 99.09% and MCC of 0.6325, while GraphCodeBERT and CodeT5+ reach F1 scores of 80.00% and 73.68%, respectively. A sensitivity analysis confirms that Task~3 conclusions hold most robustly for ML models under stricter future-horizon control. Across all settings, ML models remain highly effective for programming success prediction, while complex models offer value in specific settings. This work provides a systematic comparison across predictive formulations and offers robust modeling guidance for submission-aware analytics in programming education, where near-future success prediction can inform timely intervention in online judge platforms and adaptive programming support systems.

cs.CY↗

arXiv2026

LLM-as-a-Judge for Human-AI Co-Creation: A Reliability-Aware Evaluation Framework for Coding

LLMs are increasingly employed both as judges for evaluating open-ended outputs and as co-creation partners in AI-assisted programming; yet rigorous evaluation in human-AI co-creation settings remains underdeveloped as judgments must be reliable, comparable across models, and interpretable over multi-turn interaction. To address this gap, a rubric-driven LLM-as-a-Judge framework is presented for contest-style human-AI co-creation in coding and software engineering (SE). The framework is built around schema-constrained judge outputs, validation and repair mechanisms, grouped and split by user and problem to prevent trajectory leakage, and participant-level NONBLIND context. Multiple LLM judges are assessed through a multi-metric protocol covering discrimination (ROC-AUC, PR-AUC), thresholded decision quality (MCC), probabilistic reliability (LogLoss, Brier score, ECE), and inter-judge agreement (Cohen's and Fleiss' k). Human-AI co-creation is further examined through trajectory-level signals, including turn-wise confidence, Success-at-Turn, time-to-success, revision churn, and CodeBLEU. Co-creation success is found to concentrate early, with Success-at-Turn rising to 0.8533 at the first observed turn and stabilizing at 0.8641 by turn 6. Revision behavior, however, remains heterogeneous, suggesting that productive progress can emerge through either incremental refinement or broader restructuring. On the judging side, the best held-out scores reach 0.5937 for ROC-AUC, 0.6904 for PR-AUC, and 0.5000 for MCC test, while inter-judge consistency remains modest overall (mean pairwise Cohen's k = 0.1592, Fleiss' k = 0.0696). Taken together, this work offers an auditable and reproducible evaluation methodology that links reliability-aware LLM judging with trajectory-based analysis of human-AI co-creation, providing a practical evaluation template for future AI-assisted coding and SE.

cs.SE↗