MACEDON : Supporting Programmers with Real-Time Multi-Dimensional Code Evaluation and Optimization
Bibliographic record
Abstract
Recent advancements in Large Language Models (LLMs) have led programmers to increasingly turn to them for code optimization and evaluation.However, programmers need to frequently switch between code evaluation and prompt authoring because there is a lack of understanding of the underlying code.Yet, current LLMdriven code assistants do not provide sufficient transparency to help programmers track their code based on the intended evaluation metrics, a crucial step before aligning these evaluations with their optimization goals.To address this gap, we adopted an iterative, user-centered design process by first conducting a formative study and a large-scale code analysis.Based on the findings, we then developed MACEDON, a system that supports multi-dimensional code evaluation in real time, direct code segment optimization, as well as shareable report displays.We evaluated MACEDON through a controlled lab study with 24 novice programmers and two realworld case studies.The results show that MACEDON significantly improved users' ability to identify code issues, apply effective optimizations, and understand their code's evolving state.Our findings suggest that multi-dimensional evaluation, combined with interactive, segment-specific guidance, empowers users to perform more structured and confident code optimization.The code for this paper can be found in https://github.com/xuyeliu/MACEDON.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.017 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".