Predictive Prompt Analysis
Fuente:
arXiv
Saved in:
| Main Authors: | Lee, Jae Yong, Kang, Sungmin, Yoo, Shin |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
COSMosFL: Ensemble of Small Language Models for Fault Localisation
by: Cho, Hyunjoon, et al.
Published: (2025)
by: Cho, Hyunjoon, et al.
Published: (2025)
A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization
by: Kang, Sungmin, et al.
Published: (2023)
by: Kang, Sungmin, et al.
Published: (2023)
Lachesis: Predicting LLM Inference Accuracy using Structural Properties of Reasoning Paths
by: Kim, Naryeong, et al.
Published: (2024)
by: Kim, Naryeong, et al.
Published: (2024)
Identifying Inaccurate Descriptions in LLM-generated Code Comments via Test Execution
by: Kang, Sungmin, et al.
Published: (2024)
by: Kang, Sungmin, et al.
Published: (2024)
Beyond pip install: Evaluating LLM Agents for the Automated Installation of Python Projects
by: Milliken, Louis, et al.
Published: (2024)
by: Milliken, Louis, et al.
Published: (2024)
DANDI: Diffusion as Normative Distribution for Deep Neural Network Input
by: Kim, Somin, et al.
Published: (2025)
by: Kim, Somin, et al.
Published: (2025)
Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap
by: Kim, Naryeong, et al.
Published: (2026)
by: Kim, Naryeong, et al.
Published: (2026)
Clotho: Measuring Task-Specific Pre-Generation Test Adequacy for LLM Inputs
by: Yoon, Juyeon, et al.
Published: (2025)
by: Yoon, Juyeon, et al.
Published: (2025)
Capturing Semantic Flow of ML-based Systems
by: Yoo, Shin, et al.
Published: (2025)
by: Yoo, Shin, et al.
Published: (2025)
Finding the Needle in the Crash Stack: Industrial-Scale Crash Root Cause Localization with AutoCrashFL
by: Kang, Sungmin, et al.
Published: (2025)
by: Kang, Sungmin, et al.
Published: (2025)
Real Faults in Deep Learning Fault Benchmarks: How Real Are They?
by: Jahangirova, Gunel, et al.
Published: (2024)
by: Jahangirova, Gunel, et al.
Published: (2024)
An Empirical Study of Fault Localisation Techniques for Deep Learning
by: Humbatova, Nargiz, et al.
Published: (2024)
by: Humbatova, Nargiz, et al.
Published: (2024)
Prompt Design and Engineering: Introduction and Advanced Methods
by: Amatriain, Xavier
Published: (2024)
by: Amatriain, Xavier
Published: (2024)
PyTorch-based Geometric Learning with Non-CUDA Processing Units: Experiences from Intel Gaudi-v2 HPUs
by: Bu, Fanchen, et al.
Published: (2025)
by: Bu, Fanchen, et al.
Published: (2025)
Prompt Smells: An Omen for Undesirable Generative AI Outputs
by: Ronanki, Krishna, et al.
Published: (2024)
by: Ronanki, Krishna, et al.
Published: (2024)
Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)
by: Ahmed, Toufique, et al.
Published: (2023)
by: Ahmed, Toufique, et al.
Published: (2023)
Prompt-Driven Code Summarization: A Systematic Literature Review
by: Farjana, Afia, et al.
Published: (2026)
by: Farjana, Afia, et al.
Published: (2026)
Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study
by: Nadim, Md, et al.
Published: (2025)
by: Nadim, Md, et al.
Published: (2025)
Code Roulette: How Prompt Variability Affects LLM Code Generation
by: Paleyes, Andrei, et al.
Published: (2025)
by: Paleyes, Andrei, et al.
Published: (2025)
An Effective Software Risk Prediction Management Analysis of Data Using Machine Learning and Data Mining Method
by: Xu, Jinxin, et al.
Published: (2024)
by: Xu, Jinxin, et al.
Published: (2024)
Try with Simpler -- An Evaluation of Improved Principal Component Analysis in Log-based Anomaly Detection
by: Yang, Lin, et al.
Published: (2023)
by: Yang, Lin, et al.
Published: (2023)
PPO guided Agentic Pipeline for Adaptive Prompt Selection and Test Case Generation
by: Koushik, Gourisetty Venkata Sai, et al.
Published: (2026)
by: Koushik, Gourisetty Venkata Sai, et al.
Published: (2026)
What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering
by: Errica, Federico, et al.
Published: (2024)
by: Errica, Federico, et al.
Published: (2024)
Should Code Models Learn Pedagogically? A Preliminary Evaluation of Curriculum Learning for Real-World Software Engineering Tasks
by: Khant, Kyi Shin, et al.
Published: (2025)
by: Khant, Kyi Shin, et al.
Published: (2025)
Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM
by: Ryan, Gabriel, et al.
Published: (2024)
by: Ryan, Gabriel, et al.
Published: (2024)
PromSec: Prompt Optimization for Secure Generation of Functional Source Code with Large Language Models (LLMs)
by: Nazzal, Mahmoud, et al.
Published: (2024)
by: Nazzal, Mahmoud, et al.
Published: (2024)
Next Edit Prediction: Learning to Predict Code Edits from Context and Interaction History
by: Lu, Ruofan, et al.
Published: (2025)
by: Lu, Ruofan, et al.
Published: (2025)
FGDM: Reasoning Aware Multi-Agentic Framework for Software Bug Detection using Chain of Thought and Tree of Thought Prompting
by: Padmanabhuni, Srita, et al.
Published: (2026)
by: Padmanabhuni, Srita, et al.
Published: (2026)
ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning
by: Jiang, Juyong, et al.
Published: (2026)
by: Jiang, Juyong, et al.
Published: (2026)
On the calibration of Just-in-time Defect Prediction
by: Shahini, Xhulja, et al.
Published: (2025)
by: Shahini, Xhulja, et al.
Published: (2025)
Defect Prediction Using Stylistic Metrics
by: Yasir, Rafed Muhammad, et al.
Published: (2022)
by: Yasir, Rafed Muhammad, et al.
Published: (2022)
DeepGo: Predictive Directed Greybox Fuzzing
by: Lin, Peihong, et al.
Published: (2025)
by: Lin, Peihong, et al.
Published: (2025)
ThrowBench: Benchmarking LLMs by Predicting Runtime Exceptions
by: Prenner, Julian Aron, et al.
Published: (2025)
by: Prenner, Julian Aron, et al.
Published: (2025)
Challenging Bug Prediction and Repair Models with Synthetic Bugs
by: Ibrahimzada, Ali Reza, et al.
Published: (2023)
by: Ibrahimzada, Ali Reza, et al.
Published: (2023)
A Feature-Driven Framework for Software Fault Prediction
by: Ghazi, Ahmad Nauman, et al.
Published: (2026)
by: Ghazi, Ahmad Nauman, et al.
Published: (2026)
SWE-Adept: An LLM-Based Agentic Framework for Deep Codebase Analysis and Structured Issue Resolution
by: He, Kang, et al.
Published: (2026)
by: He, Kang, et al.
Published: (2026)
Practitioners' Challenges and Perceptions of CI Build Failure Predictions at Atlassian
by: Hong, Yang, et al.
Published: (2024)
by: Hong, Yang, et al.
Published: (2024)
Investigating Reproducibility in Deep Learning-Based Software Fault Prediction
by: Mukhtar, Adil, et al.
Published: (2024)
by: Mukhtar, Adil, et al.
Published: (2024)
MooseAgent: A LLM Based Multi-agent Framework for Automating Moose Simulation
by: Zhang, Tao, et al.
Published: (2025)
by: Zhang, Tao, et al.
Published: (2025)
Better Knowledge Enhancement for Privacy-Preserving Cross-Project Defect Prediction
by: Wang, Yuying, et al.
Published: (2024)
by: Wang, Yuying, et al.
Published: (2024)
Similar Items
-
COSMosFL: Ensemble of Small Language Models for Fault Localisation
by: Cho, Hyunjoon, et al.
Published: (2025) -
A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization
by: Kang, Sungmin, et al.
Published: (2023) -
Lachesis: Predicting LLM Inference Accuracy using Structural Properties of Reasoning Paths
by: Kim, Naryeong, et al.
Published: (2024) -
Identifying Inaccurate Descriptions in LLM-generated Code Comments via Test Execution
by: Kang, Sungmin, et al.
Published: (2024) -
Beyond pip install: Evaluating LLM Agents for the Automated Installation of Python Projects
by: Milliken, Louis, et al.
Published: (2024)