One Step at a Time: Combining LLMs and Static Analysis to Generate Next-Step Hints for Programming Tasks (2410.09268v1)

Abstract: Students often struggle with solving programming problems when learning to code, especially when they have to do it online, with one of the most common disadvantages of working online being the lack of personalized help. This help can be provided as next-step hint generation, i.e., showing a student what specific small step they need to do next to get to the correct solution. There are many ways to generate such hints, with LLMs being among the most actively studied right now. While LLMs constitute a promising technology for providing personalized help, combining them with other techniques, such as static analysis, can significantly improve the output quality. In this work, we utilize this idea and propose a novel system to provide both textual and code hints for programming tasks. The pipeline of the proposed approach uses a chain-of-thought prompting technique and consists of three distinct steps: (1) generating subgoals - a list of actions to proceed with the task from the current student's solution, (2) generating the code to achieve the next subgoal, and (3) generating the text to describe this needed action. During the second step, we apply static analysis to the generated code to control its size and quality. The tool is implemented as a modification to the open-source JetBrains Academy plugin, supporting students in their in-IDE courses. To evaluate our approach, we propose a list of criteria for all steps in our pipeline and conduct two rounds of expert validation. Finally, we evaluate the next-step hints in a classroom with 14 students from two universities. Our results show that both forms of the hints - textual and code - were helpful for the students, and the proposed system helped them to proceed with the coding tasks.

• The paper introduces a novel framework combining LLMs with static analysis to generate next-step programming hints for educational tasks.
• The methodology uses a chain-of-thought approach by decomposing tasks into subgoal, code, and textual hint generation stages.
• Validation via expert reviews and student feedback confirms its ability to enhance personalized learning in IDE-based programming education.

Integrating LLMs and Static Analysis to Improve Programming Education

The paper "One Step at a Time: Combining LLMs and Static Analysis to Generate Next-Step Hints for Programming Tasks" presents a novel approach for generating next-step hints in the context of programming education. Through the confluence of LLMs and static analysis, the authors introduce a system designed to provide students with both textual and code-based hints. Implemented as a modification to the JetBrains Academy plugin, this system seeks to offer personalized assistance in integrated development environment (IDE) courses. The proposed framework aims to enhance the learning experience for students, particularly in massive open online courses (MOOCs) where individualized feedback is largely absent.

System Workflow and Methodology

The system operates through a chain-of-thought prompting technique, which is consistent with modern AI practices for improving task performance by decomposing tasks into smaller subtasks. The implementation unfolds across three stages: subgoal generation, code hint generation, and textual hint generation.

1. Subgoal Generation: At the outset, the system generates a list of subgoals representing incremental steps towards the solution. This stage leverages LLMs with a prompt constructed from task-specific attributes — such as function signatures, existing student code, and task descriptions — ensuring that the generated subgoals align with the task at hand and the student's progress.
2. Code Hint Generation: The system synthesizes a piece of code that achieves the next subgoal. During this process, static analysis is employed to enhance the generated code's quality. Key features of this stage include the application of heuristics to manage code size and static inspections aimed at maintaining high standards of code quality.
3. Textual Hint Generation: Finally, the system provides a concise textual description that guides the student towards achieving the next subgoal, derived from the improved version of the student's code.

Evaluation and Validation

The researchers conducted extensive validation — involving expert sessions and student feedback — to refine and evaluate the hints' efficacy. Subgoals and hints were tested rigorously against criteria such as specificity, independence, language idiomaticity, and alignment with programming practice. This validation process was critical, leading to several improvements, including better prompt engineering and effective static analysis integration for the code.

Implications and Future Directions

The implications of this paper stretch across both theoretical and practical dimensions of educational technology in computer science. The multi-step framework could serve as a model for integrating AI with traditional static methods, bridging the gap between algorithmic learning and human-centric education methodologies. Practically, embedding such a system within an IDE offers an enriched learning environment, aligning closely with contemporary software engineering practices.

For future directions, the paper indicates potential expansion into other programming languages and platforms, necessitating adaptations of static analysis tools to suit various environments. Additionally, there is scope for more nuanced evaluations of how these next-step hints impact long-term learning and problem-solving skills in programming education.

Conclusion

The interdisciplinary approach of combining LLMs with static analysis renders this paper a significant contribution to technology-enhanced learning paradigms. By methodically presenting how a structured AI approach can supplement human teaching efforts, this work lays the groundwork for scalable, high-quality educational tools that could be transformative for remote and online learning landscapes.