EN.601.727 Machine Programming

Johns Hopkins University — Fall 2025

Instructor: Ziyang Li   |   Email: ziyang@cs.jhu.edu

Final Project

The final project is a significant component of this course, worth 35% of your grade. It provides an opportunity to explore advanced topics in machine programming and apply the concepts learned throughout the semester.

Project Overview

Students will pick a research problem in program synthesis, analysis, or related areas. We provide a list of project ideas below, but you are encouraged to propose your own ideas based on your interests and background. The project can be theoretical, experimental, or a combination of both.

Working in a team

The project should be completed with at most 3 students. In the proposal, you should describe the project idea, the team members, and the division of labor.

Project Requirements

Project Timeline

Presentation Details

The final project presentations will take place on Friday, December 12 from 9:00 AM – 12:00 PM in Maryland Hall 217. Each group will have 10 minutes for presentation followed by 5 minutes for Q&A. Please make sure your slides are well-prepared and that you stay within time so everyone gets a fair slot.

Final Write-Up Requirements

The final project report/write-up is due on Tuesday, December 16 and should be submitted via Grade-scope.

Formatting Requirements

Please typeset your write-up professionally using LaTeX or Typst. For this class, we will use the NeurIPS template:

Note: If you do not know Typst yet, please look it up as it may be a good replacement of LaTeX.

Content Requirements

Your write-up must be at least 4 pages in length (not counting the references) and must include the following sections:

The sections in between may include things like "Methodology", "Evaluation", "Benchmark", "Framework", "Case Study", or anything that is key to your project. Please cite the related works professionally.

Project Ideas

Here are some example project areas to consider:

  1. Construct (either manually, by crawling, or by existing models) a mid-to-large-scale dataset studying a specific programming language or domain
  2. Pick a relatively low-resource language (Haskell, Prolog, Datalog, Scallop, CodeQL, LEAN, Coq, Z3, Racket, Clojure, LaTeX, TikZ, Processing, PDDL) and construct a small-scale dataset and evaluate LLMs' performance on synthesizing programs within the language
  3. Pick a high-resource language but a specific domain (Kernel driver within C, CUDA drivers, Unreal Engine Programming within C++, Neural Network Modules within Python/PyTorch/JAX) and evaluate LLMs' performance on the domain
  4. Design a small domain-specific language (for robot control, games, theorem proving, databases, maths, logic, visualization, animation, and etc.) and demonstrate LLMs' capability generating programs within the language
  5. Implementing a conversational coding agent that can help users make edits or feature requests to a codebase
  6. Implementing a downstream application that involves synthesizing of programs
  7. Using LLMs to synthesize formal specifications for existing programs, and properly evaluate the generated specifications
  8. Identifying security vulnerabilities within LLM generated programs
  9. Implementing the technique in existing papers (refer to Readings)

Note: These are suggestions only. You are encouraged to propose your own ideas based on your interests and background.