Studying and Improving Code Understandability Through Atoms of Confusion
Bibliographic record
Abstract
Developers spend most of their time reading code. Previous studies have shown that less understandable code hinders developers' productivity, making reading and debugging code harder and raising maintenance costs. In this research, we investigate how Atoms of Confusion (AoCs)-a set of low-level programming idioms for C-like languages proposed as a potential source of code confusion-can affect program comprehension and code quality. Specifically, we investigate (1) the impact of AoCs in the Open-Source Software (OSS) development community with Mining Software Repository (MSR) techniques, (2) how developers perceive how AoCs can affect comprehension, and (3) how Large Language Models (LLMs) can be used to refactor AoCs when appropriate. To this end, our preliminary study explores the defect-proneness of AoCs in open-source Java projects. We discovered that AoCs do not significantly affect defect-proneness in open-source Java projects, but future works are needed to investigate how developers interact with AoCs under various circumstances, such as different projects and programming languages. As our next steps, we aim to gather developers' perceptions of AoCs by conducting developer surveys. Next, based on these empirical findings, we will study how to LLMs can help refactor AoCs according to specific contexts such as developer experience and project convention. We expect this work will offer insights for researchers seeking to understand cognitive challenges in coding and practitioners looking to implement more effective strategies for enhancing code understandability and maintainability.
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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 | 0.012 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".