## Bar Chart: Number of Real-World Verifiable SWE Instances ### Overview The image is a bar chart comparing the number of real-world verifiable SWE (Software Weakness Enumeration) instances across different benchmarks and models. The chart compares "Python-only" instances against "Multilingual" instances. The x-axis represents the different SWE benchmarks/models, and the y-axis represents the number of instances. ### Components/Axes * **Title:** Number of Real-World Verifiable SWE Instances * **X-axis:** Categorical labels for different SWE benchmarks/models: SWE-Bench, SWE-Gym, Multi-SWE-RL, SWE-rebench, DeepSeek-V3.2, CWM, MiMo-V2-Flash, SWE-Universe (Ours) * **Y-axis:** (Implicit) Number of instances. The values are displayed above each bar. * **Legend:** Located in the top-left corner. * Blue: Python-only * Orange: Multilingual ### Detailed Analysis The chart presents the number of verifiable SWE instances for each benchmark/model, separated by Python-only and Multilingual. * **SWE-Bench:** Python-only: 2,294 * **SWE-Gym:** Python-only: 2,438 * **Multi-SWE-RL:** Multilingual: 4,723 * **SWE-rebench:** Python-only: 21,000 * **DeepSeek-V3.2:** Python-only: 24,667 * **CWM:** Python-only: 35,000 * **MiMo-V2-Flash:** Multilingual: 90,000 * **SWE-Universe (Ours):** Multilingual: 807,693 ### Key Observations * SWE-Universe (Ours) has a significantly higher number of multilingual instances (807,693) compared to all other benchmarks/models. * The number of Python-only instances varies across different benchmarks, ranging from 2,294 (SWE-Bench) to 35,000 (CWM). * Multi-SWE-RL and MiMo-V2-Flash only have Multilingual instances reported. ### Interpretation The chart highlights the performance of different benchmarks/models in terms of the number of verifiable SWE instances. The SWE-Universe (Ours) model demonstrates a substantially higher number of multilingual instances, suggesting it is more effective at identifying weaknesses in multilingual code compared to the other benchmarks/models. The data also shows the distribution of Python-only instances across different benchmarks, providing insights into their performance on Python-specific code. The large difference in the number of instances between SWE-Universe and other models suggests a significant improvement or difference in methodology.

## Bar Chart: Number of Real-World Verifiable SWE Instances ### Overview This bar chart visualizes the number of real-world verifiable Software Engineering (SWE) instances for different datasets, categorized by programming language support: Python-only and Multilingual. The chart uses a bar graph to compare the instance counts across several datasets. ### Components/Axes * **Title:** "Number of Real-World Verifiable SWE Instances" (centered at the top) * **X-axis:** Dataset names: "SWE-Bench", "SWE-Gym", "Multi-SWE-RL", "SWE-rebench", "DeepSeek-V3.2", "CWM", "MIMO-V2-Flash", "SWE-Universe (Ours)". * **Y-axis:** Number of Instances (scale not explicitly labeled, but implied to be linear). * **Legend:** Located in the top-left corner. * "Python-only" (represented by light blue) * "Multilingual" (represented by orange) ### Detailed Analysis The chart consists of eight datasets along the x-axis. For each dataset, there are two bars representing the number of Python-only and Multilingual instances. * **SWE-Bench:** * Python-only: Approximately 2,294 instances (light blue bar). * Multilingual: Approximately 2,438 instances (orange bar). * **SWE-Gym:** * Python-only: Approximately 2,438 instances (light blue bar). * Multilingual: Approximately 4,723 instances (orange bar). * **Multi-SWE-RL:** * Python-only: Approximately 4,723 instances (light blue bar). * Multilingual: Approximately 21,000 instances (orange bar). * **SWE-rebench:** * Python-only: Approximately 21,000 instances (light blue bar). * Multilingual: Approximately 24,667 instances (orange bar). * **DeepSeek-V3.2:** * Python-only: Approximately 24,667 instances (light blue bar). * Multilingual: Approximately 35,000 instances (orange bar). * **CWM:** * Python-only: Approximately 35,000 instances (light blue bar). * Multilingual: Approximately 90,000 instances (orange bar). * **MIMO-V2-Flash:** * Python-only: Approximately 90,000 instances (light blue bar). * Multilingual: No bar is visible for Python-only. * **SWE-Universe (Ours):** * Python-only: No bar is visible for Python-only. * Multilingual: Approximately 807,693 instances (orange bar). The orange bars (Multilingual) generally increase in height from left to right, with a particularly large jump for "SWE-Universe (Ours)". The light blue bars (Python-only) also generally increase, but at a much slower rate. ### Key Observations * The "SWE-Universe (Ours)" dataset has a significantly higher number of Multilingual instances (807,693) compared to all other datasets. * For most datasets, the number of Multilingual instances is greater than or equal to the number of Python-only instances. * The difference between Python-only and Multilingual instances is relatively small for the first few datasets (SWE-Bench, SWE-Gym, Multi-SWE-RL), but grows substantially for later datasets. * For MIMO-V2-Flash and SWE-Universe (Ours), there are no Python-only instances. ### Interpretation The data suggests that the "SWE-Universe (Ours)" dataset is substantially larger and more diverse than the other datasets, particularly in terms of multilingual support. The increasing trend in Multilingual instances across the datasets indicates a growing focus on supporting multiple programming languages in software engineering research and development. The lack of Python-only instances in the last two datasets suggests that these datasets are specifically designed for multilingual scenarios. The chart highlights the importance of multilingual support in modern software engineering and the potential benefits of using larger, more diverse datasets for training and evaluating SWE models. The large difference in scale between the datasets suggests that the "SWE-Universe (Ours)" dataset may represent a significant advancement in the availability of real-world verifiable SWE instances.

## Bar Chart: Number of Real-World Verifiable SWE Instances ### Overview This is a vertical bar chart comparing the number of verifiable Software Engineering (SWE) instances across different benchmarks or datasets. The chart highlights two categories of instances: "Python-only" and "Multilingual." The primary takeaway is the significantly larger scale of the "SWE-Universal (Ours)" dataset compared to all others listed. ### Components/Axes * **Chart Title:** "Number of Real-World Verifiable SWE Instances" * **Legend:** Located in the top-left corner. * **Blue Square:** "Python-only" * **Orange Square:** "Multilingual" * **X-Axis (Categories):** Lists eight different benchmarks/datasets. From left to right: 1. SWE-Bench 2. SWE-Gym 3. Multi-SWE-RL 4. SWE-rebench 5. DeepSeek-V3.2 6. QWM 7. MIMO-V2-Flash 8. SWE-Universal (Ours) * **Y-Axis:** Represents the count of instances. The axis line is present, but no numerical labels or title are visible. Values are provided directly above each bar. * **Data Labels:** Exact numerical values are printed above each bar. ### Detailed Analysis The chart presents the following data points for each category: 1. **SWE-Bench:** * **Bar:** Single blue bar (Python-only). * **Value:** 2,294. * **Trend:** Baseline value, the smallest on the chart. 2. **SWE-Gym:** * **Bar:** Single blue bar (Python-only). * **Value:** 2,438. * **Trend:** Slightly higher than SWE-Bench. 3. **Multi-SWE-RL:** * **Bar:** Single orange bar (Multilingual). * **Value:** 4,723. * **Trend:** First multilingual entry, roughly double the preceding Python-only values. 4. **SWE-rebench:** * **Bar:** Single blue bar (Python-only). * **Value:** 21,000. * **Trend:** Significant jump in scale compared to previous entries. 5. **DeepSeek-V3.2:** * **Bar:** Single orange bar (Multilingual). * **Value:** 24,667. * **Trend:** Comparable in scale to SWE-rebench, but multilingual. 6. **QWM:** * **Bar:** Single blue bar (Python-only). * **Value:** 35,000. * **Trend:** The largest Python-only dataset shown. 7. **MIMO-V2-Flash:** * **Bar:** Single orange bar (Multilingual). * **Value:** 90,000. * **Trend:** A major increase, more than double the previous highest value (QWM). 8. **SWE-Universal (Ours):** * **Bar:** Single orange bar (Multilingual). * **Value:** 807,693. * **Trend:** An order-of-magnitude increase over all other datasets. This bar dominates the chart visually. ### Key Observations * **Scale Disparity:** The "SWE-Universal (Ours)" dataset contains approximately **9 times** more instances than the next largest dataset (MIMO-V2-Flash) and over **350 times** more than the smallest (SWE-Bench). * **Category Distribution:** Of the eight datasets listed, five are categorized as "Python-only" (blue) and three as "Multilingual" (orange). The two largest datasets by a wide margin are both multilingual. * **Visual Trend:** There is a general, non-linear upward trend in dataset size from left to right, culminating in the massive final bar. The growth is not monotonic, as the fourth bar (SWE-rebench) is smaller than the fifth (DeepSeek-V3.2). ### Interpretation This chart is likely from a research paper or technical report introducing the "SWE-Universal" dataset. Its primary purpose is to **demonstrate the unprecedented scale** of this new resource compared to existing benchmarks in the software engineering domain. * **What the data suggests:** The field has previously relied on relatively small, often language-specific (Python) datasets for training and evaluating AI models on real-world software tasks. "SWE-Universal" represents a massive leap in available verifiable data, specifically emphasizing multilingual support. * **How elements relate:** The x-axis orders the datasets, likely in a combination of chronological release and increasing scale, to build a narrative of progression that peaks with the authors' contribution. The color coding (blue vs. orange) immediately draws a distinction between language-specific and broader multilingual resources. * **Notable implications:** The sheer size of "SWE-Universal" implies it could enable the training of more robust and generalizable AI software engineering agents. The emphasis on "verifiable" instances suggests a focus on high-quality, ground-truth data where the correctness of solutions can be automatically checked, which is crucial for reliable benchmarking. The chart makes a compelling visual argument for the significance of the authors' work.

## Bar Chart: Number of Real-World Verifiable SWE Instances ### Overview The chart compares the number of real-world verifiable software engineering (SWE) instances across different datasets/tools, categorized by Python-only and Multilingual implementations. The data is presented as grouped bars, with Python-only in blue and Multilingual in orange. The y-axis represents instance counts, while the x-axis lists specific SWE frameworks/datasets. ### Components/Axes - **Title**: "Number of Real-World Verifiable SWE Instances" - **X-Axis Labels**: - SWE-Bench - SWE-Gym - Multi-SWE-RL - SWE-rebench - DeepSeek-V3.2 - CWM - MiMo-V2-Flash - SWE-Universe (Ours) - **Y-Axis**: Instance counts (logarithmic scale implied by spacing) - **Legend**: - Blue = Python-only - Orange = Multilingual - **Bar Colors**: - Python-only: Blue - Multilingual: Orange ### Detailed Analysis | Dataset/Tool | Python-only | Multilingual | |-----------------------|-------------|--------------| | SWE-Bench | 2,294 | 2,438 | | SWE-Gym | 2,438 | 4,723 | | Multi-SWE-RL | 4,723 | 21,000 | | SWE-rebench | 21,000 | 24,667 | | DeepSeek-V3.2 | 24,667 | 35,000 | | CWM | 35,000 | 90,000 | | MiMo-V2-Flash | 90,000 | 807,693 | | SWE-Universe (Ours) | N/A | 807,693 | ### Key Observations - **Multilingual dominance**: Multilingual instances consistently outnumber Python-only across all categories, with ratios increasing exponentially (e.g., 1.06x for SWE-Bench to 22.5x for SWE-Universe). - **Exponential growth**: The largest gap appears in SWE-Universe (Ours), where Multilingual instances reach 807,693, dwarfing all prior categories. - **Outlier**: SWE-Universe (Ours) is an extreme outlier, with Multilingual instances exceeding the previous highest (MiMo-V2-Flash) by 8.97x. ### Interpretation The data suggests that Multilingual SWE implementations are significantly more prevalent or effective in real-world scenarios compared to Python-only approaches. The SWE-Universe (Ours) category demonstrates a breakthrough in scalability, achieving 807,693 Multilingual instances—an order of magnitude higher than prior tools. This implies that Multilingual frameworks may better address diverse linguistic requirements in software engineering tasks. The absence of Python-only data for SWE-Universe could indicate either a lack of Python support or a strategic focus on Multilingual capabilities in this dataset.