Do these tools actually improve code quality, or just make you faster?
The short answer is: it depends on the tool and how you use it. Some repository-level assistants are specifically designed to improve code quality by understanding the full context of your codebase. For example, a 2025 study introduced a knowledge-graph approach that represents your entire code repository as a graph of relationships between files, functions, and dependencies [1]. This method significantly outperformed baseline code generation on a benchmark called EvoCodeBench, meaning it produced code that was more consistent with the existing codebase and less likely to break things [1]. So when the tool is built to understand context, it can boost quality.
But there's a catch: even the best models still struggle. A 2026 benchmark called ReCUBE tested how well LLMs could reconstruct a hidden file using only the rest of the repository as context. The top model, GPT-5, only passed 37.57% of test cases in the full-context setting [2]. That means even state-of-the-art assistants get it wrong more than 60% of the time when they have to rely on repository-level context. So while quality can improve, it's far from perfect.
On the speed side, a 2024 paper on RepoGenix showed that you can actually have both: their tool cut inference time by 48.41% while also improving code completion accuracy compared to baselines [3]. They did this by smartly compressing the relevant context into a smaller prompt, so the model didn't have to process the entire codebase every time. So speed and quality aren't necessarily a trade-off—it's about how the context is managed.
Can these assistants actually make your code worse?
Yes, and this is the most concerning finding across these studies. A 2023 user study (the most cited paper here, with 137 citations) directly compared developers who had access to an AI code assistant versus those who didn't, on security-related tasks [5]. The result: participants with the AI assistant wrote significantly less secure code. Even worse, they were more likely to believe their code was secure—meaning the tool gave them a false sense of confidence [5]. This is a clear warning: if you rely on an assistant without carefully reviewing its suggestions, your code quality (especially security) can actually decline.
This finding is reinforced by a 2025 paper on CodeCloak, which highlights that LLM-based code assistants can inadvertently leak your proprietary code to the service provider [4]. While CodeCloak proposes a method to mitigate that leakage, the very need for such a tool shows that using these assistants introduces new risks to code quality and intellectual property. So the answer to the original question is nuanced: repository-level assistants can improve code quality in terms of consistency and completion accuracy, but they can also degrade security and create overconfidence. The net effect depends on the tool's design and the developer's vigilance.
When are repository-level assistants most useful for quality?
These tools shine when they have good context about your codebase. The ReCUBE study found that agents augmented with a Caller-Centric Exploration (CCE) toolkit—which uses dependency graphs to find the most relevant files—consistently outperformed all baselines, with improvements of up to 7.56% in strict pass rate [2]. That means if the assistant can intelligently navigate your repository and pull in the right context, it produces more correct code. The knowledge-graph approach in [1] similarly improved contextual relevance by capturing structural and relational information across files.
However, the same studies show that raw context—just dumping all files into the prompt—doesn't work well. GPT-5's 37.57% pass rate in the full-context setting [2] proves that more context isn't automatically better. The key is smart context selection, as demonstrated by RepoGenix [3], which used 'Context-Aware Selection' to compress relevant context into a limited-size prompt. So the practical takeaway: repository-level assistants improve code quality when they are designed to retrieve and prioritize the right context, not when they just dump everything into the model.
About These Sources
This answer is built on 5 peer-reviewed studies — published from 2023 to 2026, 4 from 2024 or later, collectively cited 144 times — selected as the most relevant from 5 studies that passed quality screening, drawn from 44 papers retrieved from a database of over 500 million.
Sources used in this answer
Knowledge Graph Based Repository-Level Code Generation
A knowledge-graph-based approach for repository-level code generation significantly outperformed baseline methods on the EvoCodeBench benchmark, showing improved contextual relevance and consistency with the existing codebase.
ReCUBE: Evaluating Repository-Level Context Utilization in Code Generation
The ReCUBE benchmark found that even GPT-5 achieved only a 37.57% strict pass rate in full-context repository-level code generation, but agents using the Caller-Centric Exploration toolkit improved pass rates by up to 7.56% across all tested models.
RepoGenix: Dual Context-Aided Repository-Level Code Completion with Language Models
RepoGenix reduced inference time by 48.41% while also improving code completion performance compared to baselines, using Context-Aware Selection to compress relevant context into limited-size prompts.
CodeCloak: A Method for Mitigating Code Leakage by LLM Code Assistants
CodeCloak proposes a deep reinforcement learning agent that manipulates prompts to minimize code leakage while preserving useful suggestions, validated across multiple code assistant models and repositories.
Do Users Write More Insecure Code with AI Assistants?
In a user study, participants with access to an AI code assistant wrote significantly less secure code than those without, and were more likely to believe their code was secure, indicating overconfidence.