# CCCI: Code Completion with Contextual Information for Complex Data Transfer Tasks Using Large Language Models

arXiv: https://arxiv.org/abs/2503.23231 · HTML: https://mlsanity.com/paper/2503.23231/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 49 references that are in the map.
- First posted on arXiv 29 Mar 2025.

## Builds on (most cited first, 29 of 29)

- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [DeepSeek LLM: Scaling Open-Source Language Models with Longtermism](https://mlsanity.com/paper/2401.02954/) (2024)
- [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020)
- [IntelliCode Compose: Code Generation Using Transformer](https://mlsanity.com/paper/2005.08025/) (2020)
- [Retrieval Augmented Code Generation and Summarization](https://mlsanity.com/paper/2108.11601/) (2021)
- [ReACC: A Retrieval-Augmented Code Completion Framework](https://mlsanity.com/paper/2203.07722/) (2022)
- [Repository-Level Prompt Generation for Large Language Models of Code](https://mlsanity.com/paper/2206.12839/) (2022)
- [A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges](https://mlsanity.com/paper/2303.17125/) (2023)
- [Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)](https://mlsanity.com/paper/2304.06815/) (2023)
- [Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM](https://mlsanity.com/paper/2402.00097/) (2024)
- [TreeGen: A Tree-Based Transformer Architecture for Code Generation](https://mlsanity.com/paper/1911.09983/) (2019)
- [Multi-task Learning based Pre-trained Language Model for Code Completion](https://mlsanity.com/paper/2012.14631/) (2020)
- [Long Code Arena: a Set of Benchmarks for Long-Context Code Models](https://mlsanity.com/paper/2406.11612/) (2024)
- [A Performance Study of LLM-Generated Code on Leetcode](https://mlsanity.com/paper/2407.21579/) (2024)
- [Retrieval-Based Neural Code Generation](https://mlsanity.com/paper/1808.10025/) (2018)
- [TreeBERT: A Tree-Based Pre-Trained Model for Programming Language](https://mlsanity.com/paper/2105.12485/) (2021)
- [Fill in the Blank: Context-aware Automated Text Input Generation for Mobile GUI Testing](https://mlsanity.com/paper/2212.04732/) (2022)
- [Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain](https://mlsanity.com/paper/2310.14053/) (2023)
- [Code Completion by Modeling Flattened Abstract Syntax Trees as Graphs](https://mlsanity.com/paper/2103.09499/) (2021)
- [SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning](https://mlsanity.com/paper/2406.01006/) (2024)
- [When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming](https://mlsanity.com/paper/2306.04930/) (2023)
- [On Mitigating Code LLM Hallucinations with API Documentation](https://mlsanity.com/paper/2407.09726/) (2024)
- [Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases](https://mlsanity.com/paper/2308.09313/) (2023)
- [A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning](https://mlsanity.com/paper/1909.06983/) (2019)
- [Compositional API Recommendation for Library-Oriented Code Generation](https://mlsanity.com/paper/2402.19431/) (2024)
- [Investigating the Performance of Language Models for Completing Code in Functional Programming Languages: a Haskell Case Study](https://mlsanity.com/paper/2403.15185/) (2024)
- [CodeFort: Robust Training for Code Generation Models](https://mlsanity.com/paper/2405.01567/) (2024)
- [Creativity, Generative AI, and Software Development: A Research Agenda](https://mlsanity.com/paper/2406.01966/) (2024)
- [Uncovering Weaknesses in Neural Code Generation](https://mlsanity.com/paper/2407.09793/) (2024)

## Built on by (most cited first, 2 of 2)

- [Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approaches](https://mlsanity.com/paper/2510.04905/) (2025)
- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)

## Abstract (arXiv)

Unlike code generation, which involves creating code from scratch, code completion focuses on integrating new lines or blocks of code into an existing codebase. This process requires a deep understanding of the surrounding context, such as variable scope, object models, API calls, and database relations, to produce accurate results. These complex contextual dependencies make code completion a particularly challenging problem. Current models and approaches often fail to effectively incorporate such context, leading to inaccurate completions with low acceptance rates (around 30%). For tasks like data transfer, which rely heavily on specific relationships and data structures, acceptance rates drop even further. This study introduces CCCI, a novel method for generating context-aware code completions specifically designed to address data transfer tasks. By integrating contextual information, such as database table relationships, object models, and library details into Large Language Models (LLMs), CCCI improves the accuracy of code completions. We evaluate CCCI using 289 Java snippets, extracted from over 819 operational scripts in an industrial setting. The results demonstrate that CCCI achieved a 49.1% Build Pass rate and a 41.0% CodeBLEU score, comparable to state-of-the-art methods that often struggle with complex task completion.