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    <title>Final Project - Machine Programming | Johns Hopkins University</title>

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    <header>
        <h1>EN.601.727 Machine Programming</h1>
        <p>Johns Hopkins University &mdash; Fall 2025</p>
        <p><strong>Instructor:</strong> Ziyang Li &nbsp; | &nbsp; <strong>Email:</strong> <a
                href="mailto:ziyang@cs.jhu.edu">ziyang@cs.jhu.edu</a></p>
    </header>
    <nav>
        <a href="index.html">Home</a>
        <a href="assignments.html">Assignments</a>
        <a href="readings.html">Readings</a>
        <a href="project.html" class="active">Final Project</a>
        <a href="presentation.html">Presentations</a>
        <a href="index.html#calendar">Calendar</a>
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        <section>
            <h2>Final Project</h2>
            <p>
                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.
            </p>
        </section>

        <section>
            <h3>Project Overview</h3>
            <p>
                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.
            </p>
        </section>

        <section>
            <h3>Working in a team</h3>
            <p>
                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.
            </p>
        </section>

        <section>
            <h3>Project Requirements</h3>
            <ul>
                <li><strong>Proposal (5%):</strong> A 2 page document outlining your project idea, methodology, and
                    expected outcomes</li>
                <li><strong>Implementation (20%):</strong> Working code and/or theoretical development</li>
                <li><strong>Report (5%):</strong> A comprehensive written report describing your work</li>
                <li><strong>Presentation (5%):</strong> A 10-minute presentation to the class</li>
            </ul>
        </section>

        <section>
            <h3>Project Timeline</h3>
            <ul>
                <li><strong>Week 13 (Nov 18):</strong> Project proposal due</li>
                <li><strong>Friday, December 12:</strong> Final project presentations (9:00 AM – 12:00 PM, Maryland Hall 217)</li>
                <li><strong>Tuesday, December 16:</strong> Final report due (via Grade-scope)</li>
            </ul>
        </section>

        <section>
            <h3>Presentation Details</h3>
            <p>
                The final project presentations will take place on <strong>Friday, December 12</strong> from <strong>9:00 AM – 12:00 PM</strong> in <strong>Maryland Hall 217</strong>.
                Each group will have <strong>10 minutes for presentation</strong> followed by <strong>5 minutes for Q&A</strong>.
                Please make sure your slides are well-prepared and that you stay within time so everyone gets a fair slot.
            </p>
        </section>

        <section>
            <h3>Final Write-Up Requirements</h3>
            <p>
                The final project report/write-up is due on <strong>Tuesday, December 16</strong> and should be submitted via <strong>Grade-scope</strong>.
            </p>
            <h4>Formatting Requirements</h4>
            <p>
                Please typeset your write-up professionally using <strong>LaTeX</strong> or <strong>Typst</strong>.
                For this class, we will use the <strong>NeurIPS template</strong>:
            </p>
            <ul>
                <li><a href="https://media.neurips.cc/Conferences/NeurIPS2025/Styles.zip" target="_blank">LaTeX package</a></li>
                <li><a href="https://typst.app/universe/package/bloated-neurips/0.7.0/" target="_blank">Typst Universe Template</a></li>
                <li><a href="https://www.overleaf.com/latex/templates/neurips-2024/tpsbbrdqcmsh" target="_blank">Overleaf NeurIPS 2024 template</a> (if using Overleaf)</li>
            </ul>
            <p>
                <em>Note: If you do not know <a href="https://typst.app" target="_blank">Typst</a> yet, please look it up as it may be a good replacement of LaTeX.</em>
            </p>
            <h4>Content Requirements</h4>
            <p>
                Your write-up must be at least <strong>4 pages in length</strong> (not counting the references) and must include the following sections:
            </p>
            <ul>
                <li><strong>Abstract</strong></li>
                <li><strong>Introduction</strong></li>
                <li><strong>Related Works</strong></li>
                <li><strong>Conclusion & Future Works</strong></li>
            </ul>
            <p>
                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.
            </p>
        </section>

        <section>
            <h3>Project Ideas</h3>
            <p>Here are some example project areas to consider:</p>
            <ol>
                <li>
                    Construct (either manually, by crawling, or by existing models) a mid-to-large-scale dataset
                    studying a specific programming language or domain
                </li>
                <li>
                    Pick a relatively low-resource language
                    (<a href="https://en.wikipedia.org/wiki/Haskell_(programming_language)" target="_blank">Haskell</a>,
                    <a href="https://en.wikipedia.org/wiki/Prolog" target="_blank">Prolog</a>,
                    <a href="https://en.wikipedia.org/wiki/Datalog" target="_blank">Datalog</a>,
                    <a href="https://scallop.github.io/" target="_blank">Scallop</a>,
                    <a href="https://en.wikipedia.org/wiki/CodeQL" target="_blank">CodeQL</a>,
                    <a href="https://en.wikipedia.org/wiki/LEAN_(proof_assistant)" target="_blank">LEAN</a>,
                    <a href="https://en.wikipedia.org/wiki/Coq" target="_blank">Coq</a>,
                    <a href="https://en.wikipedia.org/wiki/Z3_(satisfiability_modulo_theories_solver)"
                        target="_blank">Z3</a>,
                    <a href="https://en.wikipedia.org/wiki/Racket_(programming_language)" target="_blank">Racket</a>,
                    <a href="https://en.wikipedia.org/wiki/Clojure" target="_blank">Clojure</a>,
                    <a href="https://en.wikipedia.org/wiki/LaTeX" target="_blank">LaTeX</a>,
                    <a href="https://en.wikipedia.org/wiki/TikZ" target="_blank">TikZ</a>,
                    <a href="https://en.wikipedia.org/wiki/Processing_(programming_language)"
                        target="_blank">Processing</a>,
                    <a href="https://en.wikipedia.org/wiki/PDDL" target="_blank">PDDL</a>)
                    and construct a small-scale dataset and evaluate LLMs' performance on synthesizing programs within
                    the language
                </li>
                <li>
                    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
                </li>
                <li>
                    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
                </li>
                <li>
                    Implementing a conversational coding agent that can help users make edits or feature requests to a
                    codebase
                </li>
                <li>
                    Implementing a downstream application that involves synthesizing of programs
                </li>
                <li>
                    Using LLMs to synthesize formal specifications for existing programs, and properly evaluate the
                    generated specifications
                </li>
                <li>
                    Identifying security vulnerabilities within LLM generated programs
                </li>
                <li>
                    Implementing the technique in existing papers (refer to <a href="readings.html"
                        target="_blank">Readings</a>)
                </li>
            </ol>
            <p><em>Note: These are suggestions only. You are encouraged to propose your own ideas based on your
                    interests and background.</em></p>
        </section>
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