# Technical Data Extraction: HumanEvalFix Turn Frequency Histograms
This document provides a detailed extraction of the data presented in three side-by-side histograms illustrating the distribution of "Turns" across different programming language benchmarks: JavaScript (js), Java, and Python.
## 1. General Layout and Metadata
The image consists of three separate histogram plots arranged horizontally.
- **Language:** English.
- **Common Y-Axis:** Frequency (Scale: 0 to 100, increments of 20).
- **Common X-Axis:** Turn (Scale: 0 to 40, markers at 0, 10, 20, 30).
- **Component Isolation:**
- **Left Plot:** HumanEvalFix-js (Color: Dark Red)
- **Center Plot:** HumanEvalFix-java (Color: Orange)
- **Right Plot:** HumanEvalFix-python (Color: Green)
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## 2. Detailed Data Extraction by Benchmark
### A. HumanEvalFix-js (Left Plot)
* **Color:** Dark Red (#99001A)
* **Trend Analysis:** The distribution is highly leptokurtic (peaked) and right-skewed. The vast majority of data points are concentrated between 5 and 10 turns, with a very thin tail extending toward 35.
* **Estimated Data Points (Frequency per Bin):**
* **Bin ~5:** ~35
* **Bin ~7.5 (Peak):** ~90
* **Bin ~10:** ~13
* **Bin ~12.5:** ~6
* **Bin ~15:** ~2
* **Bin ~17.5:** ~1
* **Bin ~32.5:** ~1 (Outlier)
### B. HumanEvalFix-java (Center Plot)
* **Color:** Orange (#FF7F24)
* **Trend Analysis:** Similar to the JS plot, this shows a strong peak around 7-8 turns. However, the "tail" of the distribution is more populated than the JS version, indicating more instances requiring 10-20 turns.
* **Estimated Data Points (Frequency per Bin):**
* **Bin ~5:** ~29
* **Bin ~7.5 (Peak):** ~81
* **Bin ~10:** ~12
* **Bin ~12.5:** ~7
* **Bin ~15:** ~4
* **Bin ~17.5:** ~5
* **Bin ~20:** ~2
* **Bin ~25:** ~1
* **Bin ~27.5:** ~1
### C. HumanEvalFix-python (Right Plot)
* **Color:** Green (#458B00)
* **Trend Analysis:** This plot shows the highest peak of the three. The distribution is extremely tight, with the overwhelming majority of tasks completed in under 10 turns. The tail is the shortest and least populated of the three languages.
* **Estimated Data Points (Frequency per Bin):**
* **Bin ~5 (Peak):** ~96
* **Bin ~7.5:** ~26
* **Bin ~10:** ~12
* **Bin ~12.5:** ~2
* **Bin ~15:** ~3
* **Bin ~17.5:** ~1
* **Bin ~20:** ~1
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## 3. Comparative Summary Table
| Metric | HumanEvalFix-js | HumanEvalFix-java | HumanEvalFix-python |
| :--- | :--- | :--- | :--- |
| **Peak Frequency** | ~90 | ~81 | ~96 |
| **Peak Turn Bin** | ~7.5 | ~7.5 | ~5.0 |
| **Distribution Shape** | Peaked, right-skewed | Peaked, moderate tail | Highly peaked, short tail |
| **Max Turn Observed** | ~33 | ~28 | ~22 |
## 4. Conclusion
The data indicates that for all three languages, the "HumanEvalFix" process typically concludes within 5 to 10 turns. **Python** shows the highest efficiency (highest frequency at the lowest turn count), while **Java** shows a slightly higher tendency for tasks to require a moderate number of extra turns (10-20 range) compared to the others.
