Datasets:

iris-sast
/

CWE-Bench-Java

@@ -16,9 +16,9 @@ tags:
 This repository contains the dataset CWE-Bench-Java presented in the paper [LLM-Assisted Static Analysis for Detecting Security Vulnerabilities](https://arxiv.org/abs/2405.17238). At a high level, this dataset contains 120 CVEs spanning 4 CWEs, namely path-traversal, OS-command injection, cross-site scripting, and code-injection. Each CVE includes the buggy and fixed source code of the project, along with the information of the fixed files and functions. We provide the seed information for each CVE in this repository, as well as advisories.
-CWE-Bench-Java Github Repository (https://github.com/iris-sast/cwe-bench-java) - The CWE-Bench-Java repository contains more details about the benchmark, and how to reproduce the benchmark for the paper. For any feedback, please open an issue.
-IRIS Paper Github Repository (https://github.com/iris-sast/iris) - The IRIS repository contains instructions on reproducing the paper.
 ## Dataset Details
@@ -133,21 +133,17 @@ Each row in `data/fix_info.csv` looks like the following.
 - `signature`: the signature of the method. Note that we might have multiple overloaded methods with the same name but with different signatures
-### Dataset Sources [optional]
 An extension of this dataset can be found on the Github repository, which provides utilities to fetch and build the relevant projects, and a simple website visualizer.
 - **Curated by:** Ziyang Li, Saikat Dutta
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
 - **License:** MIT
 <!-- Provide the basic links for the dataset. -->
 - **Repository:** [CWE-Bench-Java](https://github.com/iris-sast/cwe-bench-java/tree/master)
 - **Paper [optional]:** [LLM-Assisted Static Analysis for Detecting Security Vulnerabilities](https://arxiv.org/abs/2405.17238)
-- **Demo [optional]:** [More Information Needed]
 ## Uses
@@ -178,8 +174,6 @@ An extension of this dataset can be found on the Github repository, which provid
 - 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]
 ### Curation Rationale
 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.
@@ -203,34 +197,6 @@ This dataset bridges that gap with reproducible, well-labeled examples tied to C
 - 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. -->
-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-#### Annotation process
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-[More Information Needed]
-#### Who are the annotators?
-<!-- This section describes the people or systems who created the annotations. -->
-[More Information Needed]
-#### Personal and Sensitive Information
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-[More Information Needed]
 ## Bias, Risks, and Limitations
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
@@ -240,13 +206,7 @@ This dataset bridges that gap with reproducible, well-labeled examples tied to C
 - 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
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-## Citation [optional]
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 Consider citing our paper:
@@ -263,15 +223,7 @@ Consider citing our paper:
 }
 ```
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
 <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
@@ -281,10 +233,6 @@ Consider citing our paper:
 - 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
 - Ziyang Li (University of Pennsylvania)
@@ -296,4 +244,4 @@ Consider citing our paper:
 ## Dataset Card Contact
-[More Information Needed]

 This repository contains the dataset CWE-Bench-Java presented in the paper [LLM-Assisted Static Analysis for Detecting Security Vulnerabilities](https://arxiv.org/abs/2405.17238). At a high level, this dataset contains 120 CVEs spanning 4 CWEs, namely path-traversal, OS-command injection, cross-site scripting, and code-injection. Each CVE includes the buggy and fixed source code of the project, along with the information of the fixed files and functions. We provide the seed information for each CVE in this repository, as well as advisories.
+**CWE-Bench-Java Github Repository** (https://github.com/iris-sast/cwe-bench-java) - The CWE-Bench-Java repository contains more details about the benchmark, and how to reproduce the benchmark for the paper. For any feedback, please open an issue.
+**IRIS Paper Github Repository** (https://github.com/iris-sast/iris) - The IRIS repository contains instructions on reproducing the paper.
 ## Dataset Details
 - `signature`: the signature of the method. Note that we might have multiple overloaded methods with the same name but with different signatures
+### Dataset Sources
 An extension of this dataset can be found on the Github repository, which provides utilities to fetch and build the relevant projects, and a simple website visualizer.
 - **Curated by:** Ziyang Li, Saikat Dutta
 - **License:** MIT
 <!-- Provide the basic links for the dataset. -->
 - **Repository:** [CWE-Bench-Java](https://github.com/iris-sast/cwe-bench-java/tree/master)
 - **Paper [optional]:** [LLM-Assisted Static Analysis for Detecting Security Vulnerabilities](https://arxiv.org/abs/2405.17238)
 ## Uses
 - 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.
 ### Curation Rationale
 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.
 - Class and method boundaries using AST analysis
 - Patch validation was performed via manual review and automated testing where possible.
 ## Bias, Risks, and Limitations
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 - 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.”
+## Citation
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 Consider citing our paper:
 }
 ```
+## Glossary
 <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
 - 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
 ## Dataset Card Authors
 - Ziyang Li (University of Pennsylvania)
 ## Dataset Card Contact
+For any feedback, questions, concerns - please [open an issue](https://github.com/iris-sast/cwe-bench-java/issues) on the CWE-Bench-Java Github repository.