Tootfinder

RepoSummary: Feature-Oriented Summarization and Documentation Generation for Code Repositories
Yifeng Zhu, Xianlin Zhao, Xutian Li, Yanzhen Zou, Haizhuo Yuan, Yue Wang, Bing Xie
https://arxiv.org/abs/2510.11039

RepoSummary: Feature-Oriented Summarization and Documentation Generation for Code Repositories
Repository summarization is a crucial research question in development and maintenance for software engineering. Existing repository summarization techniques primarily focus on summarizing code according to the directory tree, which is insufficient for tracing high-level features to the methods that collaboratively implement them. To address these limitations, we propose RepoSummary, a feature-oriented code repository summarization approach that simultaneously generates repository documentation…

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning
Moona Kanwal, Muhammad Sami Siddiqui, Syed Anael Ali
https://arxiv.org/abs/2510.10263

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning
Profiling gamers provides critical insights for adaptive game design, behavioral understanding, and digital well-being. This study proposes an integrated, data-driven framework that combines psychological measures, behavioral analytics, and machine learning to reveal underlying gamer personas. A structured survey of 250 participants, including 113 active gamers, captured multidimensional behavioral, motivational, and social data. The analysis pipeline integrated feature engineering, association…

Software Defect Prediction using Autoencoder Transformer Model
Seshu Barma, Mohanakrishnan Hariharan, Satish Arvapalli
https://arxiv.org/abs/2510.10840 https://

Software Defect Prediction using Autoencoder Transformer Model
An AI-ML-powered quality engineering approach uses AI-ML to enhance software quality assessments by predicting defects. Existing ML models struggle with noisy data types, imbalances, pattern recognition, feature extraction, and generalization. To address these challenges, we develop a new model, Adaptive Differential Evolution (ADE) based Quantum Variational Autoencoder-Transformer (QVAET) Model (ADE-QVAET). ADE combines with QVAET to obtain high-dimensional latent features and maintain sequent…

Intelligent Algorithm Selection for Recommender Systems: Meta-Learning via in-depth algorithm feature engineering
Jarne Mathi Decker
https://arxiv.org/abs/2509.20134 https://

Intelligent Algorithm Selection for Recommender Systems: Meta-Learning via in-depth algorithm feature engineering
The "No Free Lunch" theorem dictates that no single recommender algorithm is optimal for all users, creating a significant Algorithm Selection Problem. Standard meta-learning approaches aim to solve this by selecting an algorithm based on user features, but treat the fundamentally diverse algorithms themselves as equivalent, "black-box" choices. This thesis investigates the impact of overcoming this limitation by engineering a comprehensive feature set to explicitly characterize the algorithms …

Metaphor identification using large language models: A comparison of RAG, prompt engineering, and fine-tuning
Matteo Fuoli, Weihang Huang, Jeannette Littlemore, Sarah Turner, Ellen Wilding
https://arxiv.org/abs/2509.24866

Metaphor identification using large language models: A comparison of RAG, prompt engineering, and fine-tuning
Metaphor is a pervasive feature of discourse and a powerful lens for examining cognition, emotion, and ideology. Large-scale analysis, however, has been constrained by the need for manual annotation due to the context-sensitive nature of metaphor. This study investigates the potential of large language models (LLMs) to automate metaphor identification in full texts. We compare three methods: (i) retrieval-augmented generation (RAG), where the model is provided with a codebook and instructed to …

BrainIB : Leveraging Graph Neural Networks and Information Bottleneck for Functional Brain Biomarkers in Schizophrenia
Tianzheng Hu, Qiang Li, Shu Liu, Vince D. Calhoun, Guido van Wingen, Shujian Yu
https://arxiv.org/abs/2510.03004

BrainIB++: Leveraging Graph Neural Networks and Information Bottleneck for Functional Brain Biomarkers in Schizophrenia
The development of diagnostic models is gaining traction in the field of psychiatric disorders. Recently, machine learning classifiers based on resting-state functional magnetic resonance imaging (rs-fMRI) have been developed to identify brain biomarkers that differentiate psychiatric disorders from healthy controls. However, conventional machine learning-based diagnostic models often depend on extensive feature engineering, which introduces bias through manual intervention. While deep learning…

Fifty Years of SAR Automatic Target Recognition: The Road Forward
Jie Zhou, Yongxiang Liu, Li Liu, Weijie Li, Bowen Peng, Yafei Song, Gangyao Kuang, Xiang Li
https://arxiv.org/abs/2509.22159

Fifty Years of SAR Automatic Target Recognition: The Road Forward
This paper provides the first comprehensive review of fifty years of synthetic aperture radar automatic target recognition (SAR ATR) development, tracing its evolution from inception to the present day. Central to our analysis is the inheritance and refinement of traditional methods, such as statistical modeling, scattering center analysis, and feature engineering, within modern deep learning frameworks. The survey clearly distinguishes long-standing challenges that have been substantially miti…

Domain-Informed Genetic Superposition Programming: A Case Study on SFRC Beams
Mohammad Sadegh Khorshidi, Navid Yazdanjue, Hassan Gharoun, Mohammad Reza Nikoo, Fang Chen, Amir H. Gandomi
https://arxiv.org/abs/2509.21355

Domain-Informed Genetic Superposition Programming: A Case Study on SFRC Beams
This study presents domain-informed genetic superposition programming (DIGSP), a symbolic regression framework tailored for engineering systems governed by separable physical mechanisms. DIGSP partitions the input space into domain-specific feature subsets and evolves independent genetic programming (GP) populations to model material-specific effects. Early evolution occurs in isolation, while ensemble fitness promotes inter-population cooperation. To enable symbolic superposition, an adaptive …

Regression Language Models for Code
Yash Akhauri, Xingyou Song, Arissa Wongpanich, Bryan Lewandowski, Mohamed S. Abdelfattah
https://arxiv.org/abs/2509.26476 https://

Regression Language Models for Code
We study code-to-metric regression: predicting numeric outcomes of code executions, a challenging task due to the open-ended nature of programming languages. While prior methods have resorted to heavy and domain-specific feature engineering, we show that a single unified Regression Language Model (RLM) can simultaneously predict directly from text, (i) the memory footprint of code across multiple high-level languages such as Python and C++, (ii) the latency of Triton GPU kernels, and (iii) the …

AGNOMIN - Architecture Agnostic Multi-Label Function Name Prediction
Yonatan Gizachew Achamyeleh, Tongtao Zhang, Joshua Hyunki Kim, Gabriel Garcia, Shih-Yuan Yu, Anton Kocheturov, Mohammad Abdullah Al Faruque
https://arxiv.org/abs/2509.25514

AGNOMIN - Architecture Agnostic Multi-Label Function Name Prediction
Function name prediction is crucial for understanding stripped binaries in software reverse engineering, a key step for \textbf{enabling subsequent vulnerability analysis and patching}. However, existing approaches often struggle with architecture-specific limitations, data scarcity, and diverse naming conventions. We present AGNOMIN, a novel architecture-agnostic approach for multi-label function name prediction in stripped binaries. AGNOMIN builds Feature-Enriched Hierarchical Graphs (FEHGs),…

A Comprehensive Analysis of Churn Prediction in Telecommunications Using Machine Learning
Xuhang Chen, Bo Lv, Mengqian Wang, Xunwen Xiang, Shiting Wu, Shenghong Luo, Wenjun Zhang
https://arxiv.org/abs/2509.22654

A Comprehensive Analysis of Churn Prediction in Telecommunications Using Machine Learning
Customer churn prediction in the telecommunications sector represents a critical business intelligence task that has evolved from subjective human assessment to sophisticated algorithmic approaches. In this work, we present a comprehensive framework for telecommunications churn prediction leveraging deep neural networks. Through systematic problem formulation, rigorous dataset analysis, and careful feature engineering, we develop a model that captures complex patterns in customer behavior indic…