mlsanityData to 14 Oct 2026

Perfection Not Required? Human-AI Partnerships in Code Translation

arXiv 2104.03820 ↗First posted 8 Apr 2021cs.HC cs.SEcited by 42

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Abstract (arXiv)

Generative models have become adept at producing artifacts such as images, videos, and prose at human-like levels of proficiency. New generative techniques, such as unsupervised neural machine translation (NMT), have recently been applied to the task of generating source code, translating it from one programming language to another. The artifacts produced in this way may contain imperfections, such as compilation or logical errors. We examine the extent to which software engineers would tolerate such imperfections and explore ways to aid the detection and correction of those errors. Using a design scenario approach, we interviewed 11 software engineers to understand their reactions to the use of an NMT model in the context of application modernization, focusing on the task of translating source code from one language to another. Our three-stage scenario sparked discussions about the utility and desirability of working with an imperfect AI system, how acceptance of that system's outputs would be established, and future opportunities for generative AI in application modernization. Our study highlights how UI features such as confidence highlighting and alternate translations help software engineers work with and better understand generative NMT models.

Key facts. Cited by 42 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Apr 2021.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 6; 2023: 8; 2024: 12; 2025: 10; 2026: 5.

42 papers.

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  1. 2022Productivity Assessment of Neural Code Completioncited by 98
  2. 2022Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programmingcited by 91
  3. 2023CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translationcited by 57
  4. 2024Design Principles for Generative AI Applicationscited by 57
  5. 2022Investigating Explainability of Generative AI for Code through Scenario-based Designcited by 47
  6. 2022Code Translation with Compiler Representationscited by 42
  7. 2023DeceptPrompt: Exploiting LLM-driven Code Generation via Adversarial Natural Language Instructionscited by 33
  8. 2023When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programmingcited by 25
  9. 2023Toward General Design Principles for Generative AI Applicationscited by 21
  10. 2024Position: Towards Bidirectional Human-AI Alignmentcited by 18
  11. 2023"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Modelscited by 17
  12. 2022Mutual Theory of Mind for Human-AI Communicationcited by 15
30 more
  1. 2022Aligning Offline Metrics and Human Judgments of Value for Code Generation Modelscited by 14
  2. 2023Harnessing the Power of LLMs: Evaluating Human-AI Text Co-Creation through the Lens of News Headline Generationcited by 14
  3. 2025Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creationcited by 14
  4. 2024Rocks Coding, Not Development--A Human-Centric, Experimental Evaluation of LLM-Supported SE Taskscited by 13
  5. 2024Generative Echo Chamber? Effects of LLM-Powered Search Systems on Diverse Information Seekingcited by 13
  6. 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanationscited by 11
  7. 2024CREW: Facilitating Human-AI Teaming Researchcited by 11
  8. 2023R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intentscited by 10
  9. 2023Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Codecited by 9
  10. 2024Output Format Biases in the Evaluation of Large Language Models for Code Translationcited by 9
  11. 2024A Survey on Human-AI Collaboration with Large Foundation Modelscited by 7
  12. 2024How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directionscited by 5
  13. 2024Translating between SQL Dialects for Cloud Migrationcited by 4
  14. 2024A Joint Learning Model with Variational Interaction for Multilingual Program Translationcited by 4
  15. 2025Post-Incorporating Code Structural Knowledge into Pretrained Models via ICL for Code Translationcited by 3
  16. 2025Program Skeletons for Automated Program Translationcited by 3
  17. 2025Overhearing LLM Agents: A Survey, Taxonomy, and Roadmapcited by 3
  18. 2024Scalable Interactive Machine Learning for Future Command and Controlcited by 2
  19. 2025Personality Matters: User Traits Predict LLM Preferences in Multi-Turn Collaborative Taskscited by 2
  20. 2025Citation-Grounded Code Comprehension: Preventing LLM Hallucination Through Hybrid Retrieval and Graph-Augmented Contextcited by 2
  21. 2026XAI for Coding Agent Failures: Transforming Raw Execution Traces into Actionable Insightscited by 2
  22. 2021Using Document Similarity Methods to create Parallel Datasets for Code Translationcited by 1
  23. 2025Adaptive Human-Agent Teaming: A Review of Empirical Studies from the Process Dynamics Perspectivecited by 1
  24. 2026RISE: Rule-Driven SQL Dialect Translation via Query Reductioncited by 1
  25. 2026Artificial Intelligence in Open Source Software Engineering: A Foundation for Sustainabilitycited by 1
  26. 2025Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectivescited by 0
  27. 2025Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agentscited by 0
  28. 2025Highlight All the Phrases: Enhancing LLM Transparency through Visual Factuality Indicatorscited by 0
  29. 2026The Machine Learning Canvas: Empirical Findings on Why Strategy Matters More Than AI Code Generationcited by 0
  30. 2026Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraphcited by 0