Update README.md

README.md

@@ -8,7 +8,7 @@ size_categories:
 ---
 # Dataset Summary
 GitGoodBench Lite is a subset of 17469 samples for collecting trajectories of AI agents resolving git tasks (see Supported Scenarios) for model training purposes. 
-We support the programming languages Python, Java and Kotlin and the sample types merge conflict resolution and file-commit gram.
+We support the programming languages Python, Java and Kotlin and the sample types merge conflict resolution and file-commit chain.
 
 All data in this dataset are collected from 816 unique, open-source GitHub repositories with permissive licenses 
 that have >= 1000 stars, >= 5 branches, >= 10 contributors and are not a fork or archived. We collected the initial list of repositories using [SEART.](https://seart-ghs.si.usi.ch/)
@@ -73,7 +73,6 @@ The following table provides per-field details. Columns marked “Yes” under *
 | **scenario**             | string     |  A JSON string describing specific scenario data (e.g., merge-conflict details, parent commits).                                                                     | No               |
 | **sample_type**          | string     |  The type of sample. Possible values: "merge" or "file_commit_chain."                                                                                                | No               |
 | **project_size**         | string     |  Estimated size based on lines of code. Possible values: "tiny," "small," "medium," "large," or "huge."                                                              | Yes              |
-| **project_activity**     | string     |  How recently the project was active. Possible values: "day," "week," or "month."                                                                                    | Yes              |
 | **difficulty**           | string     |  The complexity level of the scenario. Possible values: "easy," "medium," or "hard."                                                                                 | Yes              |
 
 **Note**:  
@@ -81,37 +80,36 @@ The following table provides per-field details. Columns marked “Yes” under *
 - Fields marked **No** represent the primary data for the scenario. Use them to inform or categorize the scenario type and project details.  
 
 # Dataset statistics
-We provide some statistics on the distribution of “difficulty” within the overall dataset and across different scenario types and on the distribution of samples across programming languages and
-the skew of our dataset.
+We provide some statistics on the diversity of our dataset with respect to repositories, programming languages and merge conflict resolution samples.
 
 ## Dataset Skew
 
 The below statistics show that our dataset does not exhibit an extreme skew towards some repositories and is relatively well balanced with respect to source repositories.
 
 ### Distribution Statistics
-- Total number of repositories analyzed: 829
-- Average (mean) samples per repository: 21.10
-- Standard deviation (std): 48.46
+- Total number of repositories analyzed: 816
+- Average (mean) samples per repository: 21.4
+- Standard deviation (std): 48.8
 - Minimum (min): 1
 - 25th percentile (25%): 2
 - Median (50%): 6
 - 75th percentile (75%): 18
-- Maximum (max): 645
+- Maximum (max): 644
 
 ### Top-10 Repositories by Sample Count
 
-| Repository                     | Percentage of Total Samples |
-|--------------------------------|----------------------------:|
-| zulip/zulip                    | 3.69%                       |
-| trinodb/trino                  | 2.46%                       |
-| wandb/wandb                    | 2.45%                       |
-| facebook/litho                 | 2.15%                       |
-| oss-review-toolkit/ort         | 2.03%                       |
-| apache/tomcat                  | 1.94%                       |
-| nvidia/nemo                    | 1.76%                       |
-| h2oai/h2ogpt                   | 1.30%                       |
-| conan-io/conan                 | 1.30%                       |
-| huggingface/transformers       | 1.05%                       |
+| Repository                               | Percentage of Total Samples |
+|------------------------------------------|----------------------------:|
+| zulip/zulip                              | 3.69%                       |
+| trinodb/trino                            | 2.47%                       |
+| wandb/wandb                              | 2.46%                       |
+| facebook/litho                           | 2.16%                       |
+| oss-review-toolkit/ort                   | 1.96%                       |
+| apache/tomcat                            | 1.94%                       |
+| nvidia/nemo                              | 1.76%                       |
+| h2oai/h2ogpt                             | 1.32%                       |
+| conan-io/conan                           | 1.30%                       |
+| huggingface/transformers                 | 1.05%                       |
 
 
 ### Distribution of Programming Languages
@@ -120,19 +118,11 @@ We do however note a severe skew towards Python and Java with only 3.8% of sampl
 
 | Programming Language |  Count  | Percentage |
 |----------------------|--------:|-----------:|
-| python               |  10956  | 62.63%     |
-| java                 |   5871  | 33.56%     |
-| kotlin               |    665  | 3.80%      |
+| python               |  10985  | 62.82%     |
+| java                 |   5881  | 33.67%     |
+| kotlin               |    603  | 3.45%      |
 
 
-## Overall Difficulty Distribution
-
-| Difficulty | Proportion |
-|------------|-----------:|
-| easy       | 0.409845   |
-| hard       | 0.337240   |
-| medium     | 0.252916   |
-
 ## Difficulty Distribution for "merge" Scenarios
 
 | Difficulty | Proportion |
@@ -141,13 +131,4 @@ We do however note a severe skew towards Python and Java with only 3.8% of sampl
 | hard       | 0.299672   |
 | medium     | 0.183861   |
 
-## Difficulty Distribution for "file_commit_chain" Scenarios
-
-| Difficulty | Proportion |
-|------------|-----------:|
-| hard       | 0.358954   |
-| easy       | 0.348218   |
-| medium     | 0.292828   |
-
-
 **Languages** We note that the text data in this dataset consists mostly of: commit messages, comments and is primarily in English. We do however not filter for any human languages explcitly.