## Bar Chart: Average Performance Comparison ### Overview The image is a bar chart comparing the average performance (in percentage) of different methods, labeled as "CoC (Interweave)", "CoC (Python)", "CoC (LM)", "CoC (LM state)", "CoC (try Python except LM)", and "CoC (try Python except LM state)", across various tasks or scenarios: "All", "NLP", "Alg", "Python only (repeated code)", "Python only (new code)", "Python + LM (repeated code)", and "Python + LM (new code)". ### Components/Axes * **Y-axis:** "Average performance (%)", with scale markers at 0, 25, 50, 75, and 100. * **X-axis:** Categorical labels representing different tasks or scenarios: "All", "NLP", "Alg", "Python only (repeated code)", "Python only (new code)", "Python + LM (repeated code)", and "Python + LM (new code)". * **Legend:** Located on the right side of the chart, mapping colors to methods: * Purple: "CoC (Interweave)" * Red: "CoC (Python)" * Blue: "CoC (LM)" * Light Blue: "CoC (LM state)" * Green: "CoC (try Python except LM)" * Light Green: "CoC (try Python except LM state)" ### Detailed Analysis or ### Content Details **1. All** * CoC (Interweave) (Purple): ~82% * CoC (Python) (Red): ~47% * CoC (LM) (Blue): ~54% * CoC (LM state) (Light Blue): ~66% * CoC (try Python except LM) (Green): ~78% * CoC (try Python except LM state) (Light Green): ~82% **2. NLP** * CoC (Interweave) (Purple): ~72% * CoC (Python) (Red): ~15% * CoC (LM) (Blue): ~60% * CoC (LM state) (Light Blue): ~65% * CoC (try Python except LM) (Green): ~68% * CoC (try Python except LM state) (Light Green): ~70% **3. Alg** * CoC (Interweave) (Purple): ~95% * CoC (Python) (Red): ~78% * CoC (LM) (Blue): ~48% * CoC (LM state) (Light Blue): ~55% * CoC (try Python except LM) (Green): ~97% * CoC (try Python except LM state) (Light Green): ~98% **4. Python only (repeated code)** * CoC (Interweave) (Purple): ~99% * CoC (Python) (Red): ~99% * CoC (LM) (Blue): ~35% * CoC (LM state) (Light Blue): ~30% * CoC (try Python except LM) (Green): ~99% * CoC (try Python except LM state) (Light Green): ~99% **5. Python only (new code)** * CoC (Interweave) (Purple): ~89% * CoC (Python) (Red): ~88% * CoC (LM) (Blue): ~88% * CoC (LM state) (Light Blue): ~90% * CoC (try Python except LM) (Green): ~89% * CoC (try Python except LM state) (Light Green): ~90% **6. Python + LM (repeated code)** * CoC (Interweave) (Purple): ~62% * CoC (Python) (Red): ~10% * CoC (LM) (Blue): ~62% * CoC (LM state) (Light Blue): ~72% * CoC (try Python except LM) (Green): ~62% * CoC (try Python except LM state) (Light Green): ~72% **7. Python + LM (new code)** * CoC (Interweave) (Purple): ~68% * CoC (Python) (Red): ~12% * CoC (LM) (Blue): ~68% * CoC (LM state) (Light Blue): ~70% * CoC (try Python except LM) (Green): ~68% * CoC (try Python except LM state) (Light Green): ~70% ### Key Observations * "CoC (try Python except LM state)" and "CoC (try Python except LM)" generally perform well across all tasks. * "CoC (Python)" shows lower performance in "NLP", "Python + LM (repeated code)", and "Python + LM (new code)" scenarios. * "CoC (LM)" has significantly lower performance in "Python only (repeated code)" compared to other scenarios. * For "Python only (repeated code)", all methods except "CoC (LM)" and "CoC (LM state)" achieve near-perfect performance. ### Interpretation The bar chart provides a comparative analysis of different methods ("CoC" variants) across various tasks. The performance varies significantly depending on the task and the method used. The "CoC (try Python except LM)" and "CoC (try Python except LM state)" methods generally exhibit high performance, suggesting that excluding the Language Model (LM) in certain Python-based approaches can be beneficial. The lower performance of "CoC (Python)" in specific scenarios indicates potential limitations or inefficiencies in those contexts. The chart highlights the importance of selecting the appropriate method based on the specific task requirements to optimize performance. The near-perfect performance of most methods in "Python only (repeated code)" suggests that repeated code execution benefits from these approaches, while the lower performance of "CoC (LM)" in the same scenario indicates a potential incompatibility or inefficiency when using a Language Model with repeated code.

\n ## Bar Chart: Average Performance Comparison of Code Generation Techniques ### Overview This bar chart compares the average performance (in percentage) of several code generation techniques across different task categories. The techniques are variations of "CoC" (likely Code Completion) utilizing different approaches like Interweave, Python, and Language Models (LM), with and without state management and exception handling. The task categories are "All", "NLP", "Alg", "Python only (repeated code)", "Python only (new code)", "Python + LM (repeated code)", and "Python + LM (new code)". ### Components/Axes * **X-axis:** Task Category. Categories are: "All", "NLP", "Alg", "Python only (repeated code)", "Python only (new code)", "Python + LM (repeated code)", "Python + LM (new code)". * **Y-axis:** Average performance (%). Scale ranges from 0 to 100. * **Legend:** Located in the top-right corner, identifies the different code generation techniques using color-coded bars. * CoC (Interweave) - Purple * CoC (Python) - Red * CoC (LM) - Blue * CoC (LM state) - Light Blue * CoC (try Python except LM) - Green * CoC (try Python except LM state) - Teal ### Detailed Analysis The chart consists of seven groups of bars, one for each task category. Each group contains six bars, representing the performance of each of the six code generation techniques. **All:** * CoC (Interweave): Approximately 82% * CoC (Python): Approximately 78% * CoC (LM): Approximately 73% * CoC (LM state): Approximately 76% * CoC (try Python except LM): Approximately 88% * CoC (try Python except LM state): Approximately 85% **NLP:** * CoC (Interweave): Approximately 72% * CoC (Python): Approximately 68% * CoC (LM): Approximately 63% * CoC (LM state): Approximately 66% * CoC (try Python except LM): Approximately 78% * CoC (try Python except LM state): Approximately 75% **Alg:** * CoC (Interweave): Approximately 78% * CoC (Python): Approximately 74% * CoC (LM): Approximately 92% * CoC (LM state): Approximately 88% * CoC (try Python except LM): Approximately 94% * CoC (try Python except LM state): Approximately 91% **Python only (repeated code):** * CoC (Interweave): Approximately 95% * CoC (Python): Approximately 92% * CoC (LM): Approximately 98% * CoC (LM state): Approximately 96% * CoC (try Python except LM): Approximately 99% * CoC (try Python except LM state): Approximately 98% **Python only (new code):** * CoC (Interweave): Approximately 85% * CoC (Python): Approximately 82% * CoC (LM): Approximately 88% * CoC (LM state): Approximately 86% * CoC (try Python except LM): Approximately 91% * CoC (try Python except LM state): Approximately 89% **Python + LM (repeated code):** * CoC (Interweave): Approximately 65% * CoC (Python): Approximately 62% * CoC (LM): Approximately 68% * CoC (LM state): Approximately 66% * CoC (try Python except LM): Approximately 72% * CoC (try Python except LM state): Approximately 70% **Python + LM (new code):** * CoC (Interweave): Approximately 63% * CoC (Python): Approximately 60% * CoC (LM): Approximately 66% * CoC (LM state): Approximately 64% * CoC (try Python except LM): Approximately 69% * CoC (try Python except LM state): Approximately 67% ### Key Observations * The "try Python except LM" technique consistently achieves the highest performance across all task categories. * Performance is generally highest for tasks involving "repeated code" and lowest for tasks involving "new code" when combining Python and LM. * The "LM" based approaches (CoC (LM) and CoC (LM state)) perform better on the "Alg" task compared to "All", "NLP", "Python only", and "Python + LM" tasks. * The "Interweave" and "Python" techniques show relatively stable performance across different task categories. ### Interpretation The data suggests that a hybrid approach of "try Python except LM" is the most effective code generation technique across a variety of tasks. This likely indicates that leveraging Python for its strengths and falling back on a Language Model when Python fails is a robust strategy. The superior performance on tasks with "repeated code" suggests that these techniques excel at recognizing and replicating existing patterns. The performance dip when generating "new code" implies a challenge in creative problem-solving or understanding complex requirements. The strong performance of LM-based approaches on the "Alg" task could be due to the LM's ability to learn and generalize algorithmic patterns. The chart highlights the importance of combining different code generation strategies to maximize overall performance and adaptability. The consistent performance of the "Interweave" and "Python" techniques suggests they provide a solid baseline, while the "try...except" approach adds a layer of resilience and adaptability.

## Grouped Bar Chart: Average Performance (%) by Method and Task Category ### Overview The image displays a grouped bar chart comparing the average performance (in percentage) of six different "CoC" (Chain-of-Code) methods across seven distinct task categories. The chart is divided into two main panels by a vertical line, separating broader task categories (left) from more specific Python-related task categories (right). ### Components/Axes * **Y-Axis:** Labeled "Average performance (%)". The scale runs from 0 to 100, with major tick marks at 0, 25, 50, 75, and 100. * **X-Axis:** Contains seven categorical groups. From left to right: 1. All 2. NLP 3. Alg 4. Python only (repeated code) 5. Python only (new code) 6. Python + LM (repeated code) 7. Python + LM (new code) * **Legend:** Positioned on the right side of the chart. It defines six data series by color: * Purple square: CoC (Interweave) * Red square: CoC (Python) * Blue square: CoC (LM) * Light Blue square: CoC (LM state) * Green square: CoC (try Python except LM) * Light Green square: CoC (try Python except LM state) ### Detailed Analysis Performance values are approximate, estimated from bar heights relative to the y-axis. **Left Panel (General Categories):** * **All:** * CoC (Interweave) [Purple]: ~85% * CoC (Python) [Red]: ~48% * CoC (LM) [Blue]: ~55% * CoC (LM state) [Light Blue]: ~63% * CoC (try Python except LM) [Green]: ~79% * CoC (try Python except LM state) [Light Green]: ~82% * **NLP:** * CoC (Interweave) [Purple]: ~74% * CoC (Python) [Red]: ~18% (notably low) * CoC (LM) [Blue]: ~63% * CoC (LM state) [Light Blue]: ~68% * CoC (try Python except LM) [Green]: ~69% * CoC (try Python except LM state) [Light Green]: ~71% * **Alg:** * CoC (Interweave) [Purple]: ~95% * CoC (Python) [Red]: ~80% * CoC (LM) [Blue]: ~49% * CoC (LM state) [Light Blue]: ~58% * CoC (try Python except LM) [Green]: ~92% * CoC (try Python except LM state) [Light Green]: ~96% **Right Panel (Python-Specific Categories):** * **Python only (repeated code):** * CoC (Interweave) [Purple]: ~100% * CoC (Python) [Red]: ~100% * CoC (LM) [Blue]: ~39% * CoC (LM state) [Light Blue]: ~32% * CoC (try Python except LM) [Green]: ~100% * CoC (try Python except LM state) [Light Green]: ~100% * **Python only (new code):** * CoC (Interweave) [Purple]: ~89% * CoC (Python) [Red]: ~83% * CoC (LM) [Blue]: ~51% * CoC (LM state) [Light Blue]: ~71% * CoC (try Python except LM) [Green]: ~89% * CoC (try Python except LM state) [Light Green]: ~88% * **Python + LM (repeated code):** * CoC (Interweave) [Purple]: ~75% * CoC (Python) [Red]: Bar is absent or at 0%. * CoC (LM) [Blue]: ~64% * CoC (LM state) [Light Blue]: ~72% * CoC (try Python except LM) [Green]: ~65% * CoC (try Python except LM state) [Light Green]: ~72% * **Python + LM (new code):** * CoC (Interweave) [Purple]: ~73% * CoC (Python) [Red]: ~19% * CoC (LM) [Blue]: ~68% * CoC (LM state) [Light Blue]: ~74% * CoC (try Python except LM) [Green]: ~68% * CoC (try Python except LM state) [Light Green]: ~74% ### Key Observations 1. **Method Dominance:** CoC (Interweave) [Purple] is consistently a top performer across all categories, never dropping below ~73%. 2. **Task-Specific Performance:** CoC (Python) [Red] shows extreme variance. It achieves near-perfect scores (~100%) in "Python only (repeated code)" but performs very poorly in NLP (~18%) and "Python + LM (new code)" (~19%). 3. **Hybrid Method Strength:** The "try Python except LM" methods (Green and Light Green) are strong and consistent performers, often matching or nearly matching the top-performing Interweave method, especially in the "Alg" and "Python only" categories. 4. **LM-Only Weakness:** The pure language model methods, CoC (LM) [Blue] and CoC (LM state) [Light Blue], generally underperform compared to the hybrid and Interweave methods, particularly in the "Python only (repeated code)" category where they score below 40%. 5. **Impact of Code Novelty:** For most methods, performance on "new code" tasks is slightly lower than on "repeated code" tasks within the same category (e.g., Python only). The exception is the pure LM methods, which sometimes show improvement on new code. 6. **Missing Data:** The CoC (Python) [Red] bar is absent for "Python + LM (repeated code)", indicating a score of 0% or that the method was not applicable. ### Interpretation This chart evaluates different strategies for a "Chain-of-Code" (CoC) prompting or reasoning framework. The data suggests that: * **Interweaving is Robust:** The "Interweave" strategy appears to be the most robust and generalizable approach, maintaining high performance regardless of the task domain (NLP, Algorithms, Python). * **Specialization vs. Generalization:** The "CoC (Python)" method is highly specialized. It excels when the task is purely Python code repetition but fails catastrophically when the task involves natural language or requires combining Python with a language model. This indicates it lacks generalization capability. * **Hybrid Approaches are Effective:** The "try Python except LM" strategies demonstrate the value of a hybrid, fallback approach. They leverage Python execution when possible but can fall back to the language model, achieving performance that rivals the top method in many cases. This suggests a practical and reliable implementation strategy. * **Pure LM Limitations:** Relying solely on a language model (LM or LM state) yields suboptimal performance compared to methods that incorporate code execution or sophisticated interleaving. This underscores the benefit of augmenting LMs with external tools or structured reasoning steps for these task types. * **Task Nature Matters:** The dramatic performance swings for some methods highlight that the underlying nature of the task (e.g., algorithmic vs. linguistic, code repetition vs. generation) is a critical factor in selecting the appropriate CoC strategy. There is no single best method for all scenarios, though Interweave comes closest.

## Bar Chart: Average Performance Comparison Across Configurations ### Overview The chart compares average performance (%) across six configurations (CoC variants) in different categories: "All," "NLP," "Alg," and Python/LM-specific scenarios ("Python only (repeated code)," "Python only (new code)," "Python + LM (repeated code)," "Python + LM (new code)"). Performance is measured on a 0-100% scale. ### Components/Axes - **X-axis**: Categories (All, NLP, Alg, Python only (repeated code), Python only (new code), Python + LM (repeated code), Python + LM (new code)) - **Y-axis**: Average performance (%) from 0 to 100 - **Legend**: - Purple: CoC (Interweave) - Red: CoC (Python) - Blue: CoC (LM) - Light blue: CoC (LM state) - Green: CoC (try Python except LM) - Light green: CoC (try Python except LM state) ### Detailed Analysis 1. **All Category**: - CoC (Interweave): ~82% - CoC (Python): ~48% - CoC (LM): ~55% - CoC (LM state): ~62% - CoC (try Python except LM): ~78% - CoC (try Python except LM state): ~80% 2. **NLP Category**: - CoC (Interweave): ~74% - CoC (Python): ~15% - CoC (LM): ~62% - CoC (LM state): ~68% - CoC (try Python except LM): ~68% - CoC (try Python except LM state): ~70% 3. **Alg Category**: - CoC (Interweave): ~92% - CoC (Python): ~78% - CoC (LM): ~50% - CoC (LM state): ~58% - CoC (try Python except LM): ~88% - CoC (try Python except LM state): ~90% 4. **Python only (repeated code)**: - CoC (Interweave): ~100% - CoC (Python): ~100% - CoC (LM): ~40% - CoC (LM state): ~30% - CoC (try Python except LM): ~100% - CoC (try Python except LM state): ~100% 5. **Python only (new code)**: - CoC (Interweave): ~85% - CoC (Python): ~82% - CoC (LM): ~51% - CoC (LM state): ~70% - CoC (try Python except LM): ~88% - CoC (try Python except LM state): ~85% 6. **Python + LM (repeated code)**: - CoC (Interweave): ~75% - CoC (Python): ~0% (no bar) - CoC (LM): ~65% - CoC (LM state): ~70% - CoC (try Python except LM): ~65% - CoC (try Python except LM state): ~70% 7. **Python + LM (new code)**: - CoC (Interweave): ~74% - CoC (Python): ~18% - CoC (LM): ~68% - CoC (LM state): ~72% - CoC (try Python except LM): ~67% - CoC (try Python except LM state): ~72% ### Key Observations - **Highest Performance**: "Python only (new code)" and "Python only (repeated code)" categories show near-perfect scores (100%) for CoC (Interweave), CoC (Python), and CoC (try Python except LM). - **Lowest Performance**: CoC (Python) underperforms in "NLP" (~15%) and "Python + LM (new code)" (~18%). - **Consistency**: CoC (Interweave) and CoC (try Python except LM) configurations generally outperform others across most categories. - **LM State Impact**: CoC (LM state) and CoC (try Python except LM state) show moderate performance, often trailing their non-state counterparts. ### Interpretation The data suggests that configurations leveraging Python (CoC (Interweave) and CoC (Python)) achieve the highest performance, particularly in Python-specific scenarios. The LM state variants (CoC (LM state) and CoC (try Python except LM state)) show mixed results, with some cases performing comparably to their non-state counterparts. The stark drop in CoC (Python) performance in NLP and Python+LM (new code) categories implies potential limitations in handling non-Python or novel code contexts. The near-perfect scores in Python-only scenarios highlight the effectiveness of Python-centric configurations when code is well-defined or repeated.