Counterfactual Explanations for Models of Code

10 November 2021

Papers citing "Counterfactual Explanations for Models of Code"

22 / 22 papers shown

Title
On Explaining (Large) Language Models For Code Using Global Code-Based Explanations David Nader-Palacio Dipin Khati Daniel Rodríguez-Cárdenas Alejandro Velasco Denys Poshyvanyk LRM 47 0 0 21 Mar 2025
PromptExp: Multi-granularity Prompt Explanation of Large Language Models Ximing Dong Shaowei Wang Dayi Lin Gopi Krishnan Rajbahadur Boquan Zhou Shichao Liu Ahmed E. Hassan AAML LRM 27 1 0 16 Oct 2024
Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations David Nader-Palacio Daniel Rodríguez-Cárdenas Alejandro Velasco Dipin Khati Kevin Moran Denys Poshyvanyk 50 6 0 12 Jul 2024
A Critical Study of What Code-LLMs (Do Not) Learn Abhinav Anand Shweta Verma Krishna Narasimhan Mira Mezini 40 4 0 17 Jun 2024
Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation Zhaoyang Chu Yao Wan Qian Li Yang Wu Hongyu Zhang Yulei Sui Guandong Xu Hai Jin AAML 43 9 0 24 Apr 2024
NExT: Teaching Large Language Models to Reason about Code Execution Ansong Ni Miltiadis Allamanis Arman Cohan Yinlin Deng Kensen Shi Charles Sutton Pengcheng Yin ReLM LRM 36 34 0 23 Apr 2024
Do Large Code Models Understand Programming Concepts? Counterfactual Analysis for Code Predicates Ashish Hooda Mihai Christodorescu Miltos Allamanis Aaron Wilson Kassem Fawaz Somesh Jha ELM 32 7 0 08 Feb 2024
A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research Sicong Cao Xiaobing Sun Ratnadira Widyasari David Lo Xiaoxue Wu ... Jiale Zhang Bin Li Wei Liu Di Wu Yixin Chen 31 6 0 26 Jan 2024
On a Foundation Model for Operating Systems Divyanshu Saxena Nihal Sharma Donghyun Kim Rohit Dwivedula Jiayi Chen ... Alex Dimakis P. B. Godfrey Daehyeok Kim Chris Rossbach Gang Wang 47 2 0 13 Dec 2023
Towards Causal Deep Learning for Vulnerability Detection Md. Mahbubur Rahman Ira Ceka Chengzhi Mao Saikat Chakraborty Baishakhi Ray Wei Le 23 10 0 12 Oct 2023
T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems Yiming Li Daling Wang Wenfang Wu Shi Feng Yifei Zhang CML 40 1 0 28 Sep 2023
Redundancy and Concept Analysis for Code-trained Language Models Arushi Sharma Zefu Hu Christopher Quinn Ali Jannesari 73 1 0 01 May 2023
xASTNN: Improved Code Representations for Industrial Practice Zhiwei Xu Min Zhou Xibin Zhao Yang Chen Xi Cheng Hongyu Zhang AI4TS 29 5 0 13 Mar 2023
Study of Distractors in Neural Models of Code Md Rafiqul Islam Rabin Aftab Hussain Sahil Suneja Mohammad Amin Alipour AAML 42 6 0 03 Mar 2023
COMET: Neural Cost Model Explanation Framework Isha Chaudhary Alex Renda Charith Mendis Gagandeep Singh 21 2 0 14 Feb 2023
Toward a Theory of Causation for Interpreting Neural Code Models David Nader-Palacio Alejandro Velasco Nathan Cooper Á. Rodríguez Kevin Moran Denys Poshyvanyk 28 16 0 07 Feb 2023
ExplainableFold: Understanding AlphaFold Prediction with Explainable AI Juntao Tan Yongfeng Zhang 22 6 0 27 Jan 2023
Stealthy Backdoor Attack for Code Models Zhou Yang Bowen Xu Jie M. Zhang Hong Jin Kang Jieke Shi Junda He David Lo AAML 19 65 0 06 Jan 2023
Learning to Learn to Predict Performance Regressions in Production at Meta M. Beller Hongyu Li V. Nair V. Murali Imad Ahmad Jürgen Cito Drew Carlson Gareth Ari Aye Wes Dyer 31 5 0 08 Aug 2022
A Retrospective on ICSE 2022 Cailin Winston Caleb Winston Chloe N. Winston Claris Winston Cleah Winston 19 0 0 26 Jul 2022
Semantic Robustness of Models of Source Code Goutham Ramakrishnan Jordan Henkel Zi Wang Aws Albarghouthi S. Jha Thomas W. Reps SILM AAML 37 97 0 07 Feb 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,684 0 28 Feb 2017