Update README.md

README.md

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-license: apache-2.0
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+# CORE: Benchmarking LLMs’ Code Reasoning Capabilities through Static Analysis Tasks
+
+
+This repository hosts the **CoRe** benchmark, designed to evaluate the reasoning capabilities of large language models on **program analysis tasks** including data dependency, control dependency, and information flow. Each task instance is represented as a structured JSON object with detailed metadata for evaluation and reproduction.
+
+
+Each example is a JSON object with the following fields:
+
+```json
+{
+  "label_file": "codenet_p00496_s700056700_main_12_40.yaml",
+  "code_file": "codenet_p00496_s700056700_main_12_40.c",
+  "pid": "p00496",
+  "sid": "s700056700",
+  "funname": "main",
+  "start": 12,
+  "end": 40,
+  "dataset": "codenet",
+  "language": "C",
+  "src": 30,
+  "dst": 33,
+  "groundtruth": true,
+  "groundtruth_trace": [[30, 31, 33]],
+  "task_id": "control_codenet_p00496_s700056700_main_12_40_k_33_1",
+  "prompt": "..."
+  "category": answer/trace/all_source
+}
+```
+
+### 🏷 Category Field
+
+The `category` field specifies the type of prompt associated with each task instance:
+
+* **answer**: The prompt asks the model to provide a binary response (`yes`/`no`) indicating whether the specified dependency exists.
+* **trace**: The prompt asks the model to produce a dependency trace if the answer is `yes` (e.g., the control or data dependency exists).
+* **all\_source**: The prompt asks the model to enumerate all source elements involved in the dependency.
+
+
+## 🧩 Field Descriptions
+
+| Field             | Description |
+|------------------|-------------|
+| `label_file`      | Path to the YAML file containing ground truth annotations for the current task instance. |
+| `code_file`       | Path to the corresponding C/Java/Python source code file. |
+| `pid`             | Problem ID from the original source dataset (e.g., CodeNet or GCJ). |
+| `sid`             | Solution ID identifying the specific program implementation. |
+| `funname`         | Name of the target function in which the analysis is conducted. |
+| `start`, `end`    | Line numbers defining the start and end of the target function. |
+| `dataset`         | Original dataset source (`codenet` or `gcj`). |
+| `language`        | Programming language of the source file (`C`, `Java`, `Python`). |
+| `src`, `dst`      | Defines the two program elements queried in this task. In control dependency, these are line numbers. In data dependency and information flow, they are structured as `["varname", line_no]`, representing variable instances. |
+| `groundtruth`     | Boolean indicating whether the specified dependency relationship holds (i.e., true if `src` has the given dependency on `dst`). |
+| `groundtruth_trace` | The ground truth trace (as a list of line numbers) that represents a valid transitive chain of direct dependencies. This is only used for **control dependency** tasks. For **data dependency** and **information flow**, this is set to `null` since there can be theoretically infinite number of valid traces. Evaluation is instead conducted by validating the predicted trace against the code’s static dependency structure.|
+| `task_id`         | A unique ID for the task instance. The prefix (`control_`, `data_`, `infoflow_`) identifies the task type. |
+| `prompt`          | The prompt string used in the experiment for this task instance. It includes the instruction, examples, query, and code context provided to the LLM. Content-specific fields (e.g., source/target names, line numbers) are filled into a standardized prompt template. |
+
+## 📚 Task Types
+
+The benchmark contains three types of program reasoning tasks:
+
+- `control`: Control dependency between lines.
+- `data`: Data dependency between variables.
+- `infoflow`: Information flow (explicit or implicit) between variables.
+
+Each instance is designed to assess whether an LLM can understand and reason over static semantics in real-world source code.
+
+## 🛠 Scripts and Usage
+
+For scripts, evaluation tools, and detailed instructions on running inference over CoRe, please check out our companion GitHub repository:
+
+🔗 [https://github.com/researchartifact1234/CoRe](https://github.com/researchartifact1234/CoRe)
+
+This includes:
+
+- Raw annotation data that could be used to generate various static analysis tasks
+- Predefined prompts for each task and language
+- Scripts for invoking models and parsing responses
+- Evaluation scripts for dependency classification, trace generation, and dependency source enumeration
+
+
+### 📄 License
+
+Apache License 2.0