CodePlan: Microsoft Research’s Planning-Driven, LLM-Powered Framework for Repository-Level Coding Tasks

Microsoft Research has introduced CodePlan, a versatile framework designed to tackle the intricate challenges of repository‑level coding tasks. Built to orchestrate extensive code changes across large, interconnected codebases, CodePlan leverages the planning capabilities of advanced language models to reason about dependencies, change propagation, and the validity of the resulting repository state. This article delves into the motivation behind CodePlan, its formal problem framing, the planning paradigm it adopts, the empirical validation that underpins its claims, and the broader implications for automated software development.

CodePlan represents a significant step beyond the current generation of use‑case driven coding assistants, which excel at completing individual functions or modest code edits but struggle with wide‑ranging, cross‑file transformations. In a landscape where modern software systems evolve through large migrations—such as migrating packages, introducing or refining type annotations, or adjusting API boundaries—the ability to reason about how a single edit reverberates across a repository is essential. CodePlan seeks to fill this gap by treating repository‑level coding as a planning problem and by offering a task‑agnostic framework capable of handling a variety of editing tasks through incremental, dependency‑aware reasoning. The approach stands on three pillars: formalizing the problem in a way that lends itself to automatic planning, developing a planning strategy that can adapt to changing information during execution, and validating the outcomes through a correctness oracle that drives iterative improvement.

The paper emphasizes that the core objective is to create a repository‑level coding system capable of autonomously generating derived specifications for edits, in order to arrive at a valid repository state. By “validity,” the authors refer to correctness criteria that can be instantiated in multiple ways depending on the project’s constraints. Examples include building without errors, passing static analysis checks, satisfying a type system, executing a battery of tests, or meeting specific verification tool criteria. CodePlan is designed to synthesize multi‑step plans that translate vague instructions or initial edits into a concrete sequence of code changes that collectively produce the desired, correct state. The authors illustrate how the system’s inputs—namely, a repository, a task described through natural language instructions or initial code edits, a correctness oracle, and an LLM—are orchestrated to generate actionable steps toward the target goal.

In this initial exploration, CodePlan uses a planning graph to represent the sequence of edits that must occur. Each node in the graph corresponds to a code edit obligation that the LLM must discharge. Edges encode dependencies: the target node’s completion depends on the completion of source nodes. Importantly, CodePlan does not rely on a single, static plan. It monitors the evolving repository as edits are made and adaptively extends the plan graph in response to observed outcomes and new information surfaced by the oracle. The arc of the process proceeds through cycles: generate plan suggestions with the LLM, implement the specified edits, reassess the repository with the oracle, and, if the oracle reports errors, feed those error reports back as seed specifications to generate a refined plan and repeat the cycle. This iterative loop is designed to converge toward a correct repository state by continuously re‑aligning the plan with the actual impact of edits.

A central contribution of the authors is the formalization of repository‑level coding as a planning problem. This formalization enables a consistent framework for analyzing how code changes propagate through a codebase, how dependencies influence the feasibility of edits, and how a sequence of edits can be orchestrated to maintain or improve repository integrity. The approach relies on an incremental dependency analysis that detects how a proposed change might affect downstream components, and on a change impact assessment mechanism that estimates the potential effects of edits across the repository. By combining these analyses with an adaptive planning algorithm, CodePlan can propose and adjust a coordinated set of edits that collectively fulfill the stated task while preserving overall correctness.

The empirical component of the study centers on two repository‑level coding tasks conducted with the gpt‑4‑32k model. The first task involves package migration within C# repositories, a scenario that typically requires changes across configuration files, project references, build settings, and potentially public APIs. The second task focuses on temporal code edits for Python repositories, a category that often entails adjustments to versioned dependencies, typing annotations, and runtime behavior that must be consistent across modules. In both cases, the goal is to demonstrate that a planning‑driven approach can outperform baseline strategies that rely on a simpler, iterative repair paradigm. The comparison underscores the value of planning as a means to coordinate a sequence of edits, reason about interdependencies, and guide the execution toward a coherent repository state.

The results of the experiments indicate a clear advantage for CodePlan over the baseline approach that uses a build system to identify breaking changes and then relies on an LLM to repair them. Specifically, CodePlan enabled five out of six repositories to pass the validity checks established by the correctness oracle, while the baseline approach failed to reach that level of success. This outcome highlights the effectiveness of the planning framework in aligning edits with the repository’s overall integrity requirements, rather than solely focusing on local fixes or ad hoc adjustments. The authors emphasize that the superiority of CodePlan stems from its planning‑driven structure, which ensures that edits are not only individually correct but also collectively coherent within the repository’s evolving state.

Beyond presenting empirical results, the study delves into the mechanics of how CodePlan operates in practice. The system consumes a repository, a task description, and initial specifications—provided in natural language or as early code edits—and a correctness oracle that ultimately validates the repository after proposed edits are applied. CodePlan then constructs a plan graph in which each node represents an obligation for the LLM to discharge. A node’s discharge is the completion of a particular edit, while an edge captures the dependency that a downstream edit can only be completed once an upstream edit has been satisfied. As edits are implemented and the repository changes, CodePlan extends and refines the plan graph in response to observed outcomes. When the oracle verifies that the repository meets the required correctness conditions, the task is considered complete. If errors are reported, those findings are transformed into seed specifications that guide an additional cycle of planning and execution, enabling the system to iterate toward a valid state.

In comparing CodePlan with the baseline, the authors emphasize that the baseline uses a traditional build system to detect breaking changes and then deploys an LLM to propose repairs. This contrast showcases the power of treating repository‑level coding as a coordinated planning problem rather than as a series of isolated fixes. The experimental narrative also provides qualitative insights into the kinds of failures that the baseline incurs—such as misjudging the broader impact of a change or failing to coordinate changes across disparate modules—while demonstrating how CodePlan mitigates those risks by maintaining a holistic view of the repository’s structure and dependencies throughout the planning and execution loop.

One of the key insights from the study is that planning enables a more scalable and robust approach to repository‑level edits. By explicitly modeling dependencies and propagation effects, CodePlan helps ensure that individual edits do not inadvertently compromise other parts of the system. The adaptive nature of the planning graph allows the system to accommodate unexpected outcomes, incorporate new constraints surfaced by the correctness oracle, and adjust its strategy accordingly. This dynamic adaptability is particularly important in real‑world codebases, where evolving requirements, varying coding styles, and legacy constraints introduce complexity that static, one‑shot editing strategies struggle to manage.

The research also clarifies the scope and limits of what CodePlan can achieve in its current form. While the results demonstrate encouraging progress, the framework relies on the strength of the underlying LLM and the precision of the correctness oracle. The quality of task descriptions, the granularity of the edit units, and the rigor of the oracle all influence the success rate. The authors acknowledge that CodePlan is positioned as a foundational framework rather than a finished product, inviting future work to enhance its robustness, expand its applicability to more programming languages, refine its heuristics for plan graph expansion, and integrate more sophisticated correctness checks that align with industry CI/CD pipelines.

In conclusion, CodePlan presents a promising avenue for automating intricate repository‑level coding tasks by combining language‑model reasoning with a planning‑driven execution strategy. The framework demonstrates tangible gains in both productivity and accuracy when addressing complex, cross‑module changes that define modern software ecosystems. The research illuminates a path toward more capable autonomous coding systems that can scale with the size and complexity of contemporary codebases, providing a foundation for further exploration and refinement in the realm of repository‑level automation.

Conclusion

CodePlan marks a meaningful advance in the pursuit of autonomous tooling for software engineering, addressing a critical gap between local code edits and large‑scale, repository‑level transformations. By formalizing repository editing as a planning problem, developing a dynamic plan graph that captures dependencies and propagation effects, and validating outcomes through a robust correctness oracle, CodePlan demonstrates that coordinated planning can outperform traditional, repair‑first approaches in many scenarios. The empirical results—showing success across multiple repository contexts and tasks while outperforming a baseline built on iterative repairs—underscore the potential for planning‑driven automation to improve both the speed and reliability of large‑scale code migrations and feature‑level evolutions.

The work also highlights important considerations for future development. The effectiveness of CodePlan hinges on the quality of the LLM’s reasoning, the design of the edit units, and the strength of the oracle used to assess repository validity. These components will benefit from ongoing refinement, broader language coverage, and tighter integration with real‑world CI systems, tests, and formal verification tools. As planning‑based approaches mature, they may become integral to how development teams manage complex, cross‑module changes in large software ecosystems, enabling more predictable outcomes, reduced manual intervention, and accelerated delivery of robust, correct software.