Towards a Science of Causal Interpretability in Deep Learning for Software Engineering | Paper Citation Record

Observation 3e722ac8-7b3c-4666-a6ac-e529c44bb74e · outbound

This paper cites Asuncion, Arthur U.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Asuncion, Arthur U

Reference 1

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This paper cites Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng

Reference 2

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This paper cites Learning to represent programs with graphs.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Learning to represent programs with graphs

Reference 3

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This paper cites Information retrieval models for recovering traceability links between code and documentation.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Information retrieval models for recovering traceability links between code and documentation

Reference 4

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This paper cites Unified pre-training for program understanding and generation.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Unified pre-training for program understanding and generation

Reference 5

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This paper cites Albrecht, Filippos Christianos, and Lukas Sch\"afer.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Albrecht, Filippos Christianos, and Lukas Sch\"afer

Reference 6

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This paper cites A literature review of automatic traceability links recovery for software change impact analysis.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering A literature review of automatic traceability links recovery for software change impact analysis

Reference 7

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This paper cites The adverse effects of code duplication in machine learning models of code.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering The adverse effects of code duplication in machine learning models of code

Reference 8

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This paper cites Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin

Reference 9

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This paper cites The claude 3 model family: Opus, sonnet, haiku, 2024.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering The claude 3 model family: Opus, sonnet, haiku, 2024

Reference 10

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This paper cites Program synthesis with large language models, 2021.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Program synthesis with large language models, 2021

Reference 11

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This paper cites Bishop and Hugh Bishop.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Bishop and Hugh Bishop

Reference 12

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Observation 4d66507c-e0b6-4540-85b9-3a8b22b881ca · outbound

This paper cites Gpt-neox-20b: An open-source autoregressive language model, 2022.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Gpt-neox-20b: An open-source autoregressive language model, 2022

Reference 13

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This paper cites Maximum a posteriori estimators as a limit of Bayes estimators , 2018.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Maximum a posteriori estimators as a limit of Bayes estimators , 2018

Reference 14

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This paper cites A neural probabilistic language model.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering A neural probabilistic language model

Reference 15

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This paper cites Artificial Life.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Artificial Life

Reference 16

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Unresolved cited work

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This paper cites Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell

Reference 18

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Unresolved cited work

Reference 19

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This paper cites Natural Language Processing with Python.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Natural Language Processing with Python

Reference 20

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This paper cites NLTK : The natural language toolkit.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering NLTK : The natural language toolkit

Reference 21

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This paper cites API design for machine learning software: experiences from the scikit-learn project.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering API design for machine learning software: experiences from the scikit-learn project

Reference 22

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This paper cites A model-driven architecture approach to accelerate software code generation.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering A model-driven architecture approach to accelerate software code generation

Reference 23

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This paper cites On identifiability in transformers, 2020.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering On identifiability in transformers, 2020

Reference 24

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Unresolved cited work

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This paper cites Biggerstaff, Bharat G.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Biggerstaff, Bharat G

Reference 26

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This paper cites tree-sitter/tree-sitter: v0.25.3, March 2025.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering tree-sitter/tree-sitter: v0.25.3, March 2025

Reference 27

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This paper cites Sampling in Software Engineering Research: A Critical Review and Guidelines.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Sampling in Software Engineering Research: A Critical Review and Guidelines

Reference 28

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Unresolved cited work

Reference 29

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This paper cites Ullman, Fernando Martinez-Plumed, Joshua B.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Ullman, Fernando Martinez-Plumed, Joshua B

Reference 30

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This paper cites Causality: The Place of the Causal Principle in Modern Science.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Causality: The Place of the Causal Principle in Modern Science

Reference 31

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This paper cites Emergence and Convergence: Qualitative Novelty and the Unity of Knowledge.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Emergence and Convergence: Qualitative Novelty and the Unity of Knowledge

Reference 32

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Philosophy of Science: Volume 1 and 2

Reference 33

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering The care and feeding of wild-caught mutants

Reference 34

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering An empirical exploration of trust dynamics in llm supply chains, 2024

Reference 35

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Nature’s Capacities and Their Measurement

Reference 36

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering The Dappled World: A Study of the Boundaries of Science

Reference 37

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Hunting Causes and Using Them: Approaches in Philosophy and Economics

Reference 38

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering An empirical study on the usage of bert models for code completion

Reference 39

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This paper cites An Empirical Study on the Usage of BERT Models for Code Completion.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering An Empirical Study on the Usage of BERT Models for Code Completion

Reference 40

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering An empirical study on the usage of transformer models for code completion

Reference 41

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Unresolved cited work

Reference 42

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This paper cites Counterfactual explanations for models of code.

Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Counterfactual explanations for models of code

Reference 43

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation

Reference 44

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Constructing Grounded Theory: A Practical Guide through Qualitative Analysis

Reference 45

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Chang, and Mark Christensen

Reference 46

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering A machine learning approach for tracing regulatory codes to product specific requirements

Reference 47

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Connor, A

Reference 48

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Unresolved cited work

Reference 49

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Software and Systems Traceability

Reference 50

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Achieving lightweight trustworthy traceability

Reference 51

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Sequencer: Sequence-to-sequence learning for end-to-end program repair

Reference 52

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, and Alexey Svyatkovskiy

Reference 53

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Bigquery: Serverless, highly scalable, and cost-effective multi-cloud data warehouse, 2025

Reference 54

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Debugging tool for code generation neural language models, March 2024

Reference 55

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Cisco Systems

Reference 56

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Improving smart contract security with contrastive learning-based vulnerability detection

Reference 57

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering What Makes a Good Explanation?: A Harmonized View of Properties of Explanations

Reference 58

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Snopy: Bridging sample denoising with causal graph learning for effective vulnerability detection

Reference 59

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Unresolved cited work

Reference 60

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering A survey on open source software trustworthiness

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Knowledge Neurons in Pretrained Transformers

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Enhancing software traceability by automatically expanding corpora with relevant documentation

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Technique integration for requirements assessment

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Automatic traceability link recovery via active learning

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Incremental approach and user feedbacks: a silver bullet for traceability recovery

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Ir-based traceability recovery processes: An empirical comparison of one-shot and incremental processes

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Assessing ir-based traceability recovery tools through controlled experiments

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Supporting and accelerating reproducible research in software maintenance using tracelab component library

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Configuring topic models for software engineering tasks in tracelab

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Integrating information retrieval, execution and link analysis algorithms to improve feature location in software

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering What is artificial life today, and where should it go? Artificial Life , 30(1):1--15, 02 2024

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering The Benchmark Lottery

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Towards a rigorous science of interpretable machine learning, 2017

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Considerations for Evaluation and Generalization in Interpretable Machine Learning , pages 3--17

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Eick, T.L

Reference 78

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Reference 79

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Estimating the number of remaining links in traceability recovery

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Bayesian Data Analysis in Empirical Software Engineering Research

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Automated repair of programs from large language models

Reference 82

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering C ode BERT : A pre-trained model for programming and natural languages

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Vulexplainer: A transformer-based hierarchical distillation for explaining vulnerability types

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Comparing explanation methods for traditional machine learning models part 1: An overview of current methods and quantifying their disagreement

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Can cooperative multi-agent reinforcement learning boost automatic web testing? an exploratory study

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Trace++: A traceability approach to support transitioning to agile software engineering

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering The pile: An 800gb dataset of diverse text for language modeling, 2020

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Codeparrot, 2021

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Semantically enhanced software traceability using deep learning techniques

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Foundations for an expert system in domain-specific traceability

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Reconciling Manual and Automatic Refactoring

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Github, 2020

Reference 93

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Github copilot, 2025

Reference 94

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Gethers, R

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering On integrating orthogonal information retrieval methods to improve traceability recovery

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Cold-start Software Analytics

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Graphcode \ bert \ : Pre-training code representations with data flow

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Traceability recovery between bug reports and test cases-a mozilla firefox case study

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Towards a Science of Causal Interpretability in Deep Learning for Software Engineering Do automatic test generation tools generate flaky tests?, 2023

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