X-COBOL: A Dataset of COBOL Repositories
June 08, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mir Sameed Ali, Nikhil Manjunath, Sridhar Chimalakonda
arXiv ID
2306.04892
Category
cs.SE: Software Engineering
Cross-listed
cs.PL
Citations
9
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Despite being proposed as early as 1959, COBOL (Common Business-Oriented Language) still predominantly acts as an integral part of the majority of operations of several financial, banking, and governmental organizations. To support the inevitable modernization and maintenance of legacy systems written in COBOL, it is essential for organizations, researchers, and developers to understand the nature and source code of COBOL programs. However, to the best of our knowledge, we are unaware of any dataset that provides data on COBOL software projects, motivating the need for the dataset. Thus, to aid empirical research on comprehending COBOL in open-source repositories, we constructed a dataset of 84 COBOL repositories mined from GitHub, containing rich metadata on the development cycle of the projects. We envision that researchers can utilize our dataset to study COBOL projects' evolution, code properties and develop tools to support their development. Our dataset also provides 1255 COBOL files present inside the mined repositories. The dataset and artifacts are available at https://doi.org/10.5281/zenodo.7968845.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted