Update README.md with more information (#2)

- Update README.md with more information (5aadfca25c34301fe20c2da5b1ca5980a3dc7146)


Co-authored-by: Kevin X <[email protected]>

README.md

@@ -50,6 +50,35 @@ The core set of information in this dataset lies in two files, `data/project_inf
 We also provide other essential information such as CVE advisory, and build information for the projects.
 We now go into the project information and fix information CSVs.
 
+### Advisory 
+
+vuln_id | schema_version | published_date | modified_date | aliases | summary | details | cvss_version | cvss_vector | cvss_score | severity_rating | cwe_ids | ecosystem | package_name | introduced_version | fixed_version | references | github_reviewed | github_reviewed_at | nvd_published_at
+--------|----------------|----------------|---------------|---------|---------|---------|---------------|--------------|-------------|------------------|---------|-----------|----------------|--------------------|---------------|------------|------------------|---------------------|------------------
+CVE-2022-12345 | 1.4.0 | 2022-01-01 | 2022-01-10 | GHSA-xxxx-yyyy-zzzz | Example XSS vuln | Reflected XSS in parameter `q` | CVSS:3.1 | AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N | 6.1 | MODERATE | CWE-79 | Maven | com.example:library | 1.0.0 | 1.0.1 | https://example.com/advisory | true | 2022-01-05T12:00:00Z | 2022-01-01T00:00:00Z
+
+This data is extracted from the CWE-Mitre net database and converted to JSON format.
+We now get into each field and explain what they are.
+- `vuln_id`: a string like `CVE-2021-44667` or `GHSA-xxxx-yyyy-zzzz` representing the unique ID of the vulnerability.
+- `schema_version`: a string indicating the schema used to encode this data (e.g., `"1.4.0"`).
+- `published_date`: a date (in ISO 8601 format, e.g. `2022-03-12`) representing when the vulnerability was first disclosed.
+- `modified_date`: a date representing when the record was last updated.
+- `aliases`: a list of strings (e.g., `[ "CVE-2021-44667" ]`) capturing alternate identifiers.
+- `summary`: a short string summarizing the vulnerability in one sentence.
+- `details`: a longer string giving a full description of how the vulnerability occurs and its impact.
+- `cvss_version`: a string like `CVSS:3.1` indicating the version of the CVSS specification used.
+- `cvss_vector`: a CVSS vector string (e.g., `AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N`) describing the severity dimensions.
+- `cvss_score`: a float between 0.0 and 10.0 quantifying the vulnerability severity.
+- `severity_rating`: a string with one of `LOW`, `MODERATE`, `HIGH`, or `CRITICAL` as a qualitative severity label.
+- `cwe_ids`: a list of strings like `["CWE-79"]` referring to Common Weakness Enumeration identifiers.
+- `ecosystem`: a string such as `"Maven"` or `"npm"` indicating the software package manager ecosystem affected.
+- `package_name`: a string like `com.alibaba.nacos:nacos-common` identifying the specific package.
+- `introduced_version`: a string denoting the first version where the vulnerability was introduced (e.g., `"0"`).
+- `fixed_version`: a string indicating the version where the issue was patched (e.g., `"2.0.4"`).
+- `references`: a list of URLs pointing to advisory pages, commits, issue trackers, etc.
+- `github_reviewed`: a boolean (`true` or `false`) showing whether GitHub has reviewed this vulnerability.
+- `github_reviewed_at`: a timestamp (e.g., `2022-03-14T23:25:35Z`) of when GitHub reviewed the advisory.
+- `nvd_published_at`: a timestamp of when the NVD officially published the vulnerability.
+
 ### Project Info
 
 | id | project_slug | cve_id | cwe_id | cwe_name | github_username | github_repository_name | github_tag | github_url | advisory_id | buggy_commit_id | fix_commit_ids |
@@ -120,48 +149,62 @@ An extension of this dataset can be found on the Github repository, which provid
 
 <!-- Address questions around how the dataset is intended to be used. -->
 
-### Direct Use
+- Study patterns from previous vulnerability fixes to address current vulnerabilities effectively.
+- Evaluate and compare performance of static analysis tools (e.g., CodeQL, Semgrep) on real-world vulnerabilities.
+- Assess and measure accuracy, recall, precision, and false-positive rates of various security tools across different vulnerability types.
+- Use detailed fix information to improve automatic patch generation and vulnerability remediation systems.
+- Provide examples of code vulnerabilities and their respective fixes to educate developers, cybersecurity professionals, and students.
+
+### Direct Use Examples
 
 <!-- This section describes suitable use cases for the dataset. -->
 
-[More Information Needed]
+- Analyzing past Java-based CVE fixes on CWE-022 classifications to develop guidelines for addressing file-access vulnerabilities
+- Conducting controlled experiments to systematically quantify false positives and true positive detections of security tools for injection
+- Developing interactive security training modules that showcase vulnerabilities alongside detailed explanations of the actual patches
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
+- Because the dataset covers only specific vulnerability types and limited CVEs, it should not be treated as a complete security benchmark for evaluating the entire security posture of software projects.
+- Sole reliance on the provided CVE data without additional context or tooling may lead to misinterpretations on what the vulnerability actually is.
+- Tools unrelated to static analysis or vulnerability patching (e.g., antivirus software) would likely see limited benefit from this dataset.
 
-## Dataset Structure
+### Misuse and Malicious Use
 
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+- Attackers could analyze vulnerable code examples to understand how to exploit similar software weaknesses
+- Detailed information about vulnerabilities might aid in crafting targeted attacks against unpatched software versions.
 
 [More Information Needed]
 
-## Dataset Creation
-
 ### Curation Rationale
 
-<!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
+The dataset was created to provide a high-quality benchmark for evaluating the ability of IRIS to detect and fix real-world vulnerabilities in Java code. 
+Existing benchmarks often lack direct links to real CVEs and actionable fixes. 
+This dataset bridges that gap with reproducible, well-labeled examples tied to CVEs and CWEs.
 
 ### Source Data
 
 <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+- GitHub Security Advisories (https://github.com/advisories): Used to extract structured CVE metadata, severity ratings, affected packages, ecosystem information.
+- Github commits (https://docs.github.com/en/pull-requests/committing-changes-to-your-project/creating-and-editing-commits/about-commits): Commit logs and diffs were used to identify the buggy and fixed versions of code and determine class/method-level changes needed to fix the vulnerability.
+- MITRE CWE Database (https://cwe.mitre.org): Provided the classification, naming, description, and available links related to each vulnerability type.
 
 #### Data Collection and Processing
 
 <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
 
+- Projects were selected based on the availability of Java source code and documentation on how to fix the error 
+- Buggy and fixed code versions using git diff and commit history
+- Associated advisory information from GitHub Security Advisories and NVD
+- Class and method boundaries using AST analysis 
+- Patch validation was performed via manual review and automated testing where possible.
+
 [More Information Needed]
 
 #### Who are the source data producers?
 
 <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
 
-[More Information Needed]
-
 ### Annotations [optional]
 
 <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
@@ -188,7 +231,10 @@ An extension of this dataset can be found on the Github repository, which provid
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+- Biased toward Java: Only Java-based CVEs are included; generalization to other languages (e.g., C/C++, Python) should be done with caution.
+- Limited in CWE scope: Covers only 4 CWEs — CWE-022, CWE-078, CWE-079, CWE-094.
+- Biased toward open-source: Enterprise and closed-source vulnerabilities are excluded, limiting the scope of evaluation.
+- Manually vetted, which introduces potential human error or subjective judgment in what counts as the “core fix.”
 
 ### Recommendations
 
@@ -225,15 +271,24 @@ Consider citing our paper:
 
 <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
 
-[More Information Needed]
+- CVE (Common Vulnerabilities and Exposures): A unique identifier for a known security vulnerability.
+- CWE (Common Weakness Enumeration): A formalized taxonomy of software vulnerability types.
+- CVSS (Common Vulnerability Scoring System): A standard for assessing the severity of security vulnerabilities.
+- Static Analysis: Code analysis technique that examines source code without executing it.
+- Patch: A set of changes to a program designed to fix a known issue or vulnerability
 
 ## More Information [optional]
 
 [More Information Needed]
 
-## Dataset Card Authors [optional]
+## Dataset Card Authors 
 
-[More Information Needed]
+- Ziyang Li (University of Pennsylvania)
+- Saikat Dutta (University of Pennsylvania)
+- Mayur Naik (University of Pennsylvania)
+- Claire Wang (University of Pennsylvania)
+- Kevin Xue (University of Pennsylvania)
+- Amartya Das
 
 ## Dataset Card Contact