COB2PY - A Non-AI, Rule-Based COBOL to Python Translator
Legacy modernization is a crucial task in the software industry to maintain the relevance of old and critical software systems. COBOL, a programming language developed in the 1950s, has been tightly coupled to modern-day transactions and has been prevalent in the business sector for the past several decades. Over the years, the decline in the software community around COBOL and the lack of documentation have made the maintenance and evolution of these software systems a challenging task. For these reasons, many organizations have attempted to migrate from COBOL to several languages. While there is extensive work on translating COBOL to Java, there are only a limited works that focus on translating COBOL to other languages, such as Python. Python has been widely adopted over the last decade due to its readability, dynamic nature and vast library ecosystem. Python also supports integration with modern technologies and languages, making migration of COBOL into Python a viable option. To aid this migration, we introduce COB2PY, a rule-based tool for automatically converting COBOL source code to Python. It generates the AST of the COBOL source code and translates it to Python code, preserving the original COBOL code logic and functionality. The tool is evaluated using the Computational Accuracy (CA) on the programs from the CodeNet dataset. The tool and demo video can be found at https://rishalab.github.io/COB2PY/.
Wed 10 SepDisplayed time zone: Auckland, Wellington change
13:30 - 15:00 | Session 3 - Debugging and RefactoringResearch Papers Track / Industry Track / Tool Demonstration Track / NIER Track at Case Room 3 260-055 Chair(s): Ashkan Sami Edinburgh Napier University | ||
13:30 15m | Boosting Redundancy-based Automated Program Repair by Fine-grained Pattern Mining Research Papers Track Jiajun Jiang Tianjin University, Fengjie Li Tianjin University, Zijie Zhao Tianjin University, Zhirui Ye Tianjin University, Mengjiao Liu Tianjin University, Bo Wang Beijing Jiaotong University, Hongyu Zhang Chongqing University, Junjie Chen Tianjin University | ||
13:45 10m | LadyBug: A GitHub Bot for UI-Enhanced Bug Localization in Mobile Apps Tool Demonstration Track Junayed Mahmud University of Central Florida, James Chen University of Toronto, Terry Achille University of Central Florida, Camilo Alvarez-Velez University of Central Florida, Darren Dean Bansil University of Central Florida, Patrick Ijieh University of Central Florida, Samar Karanch University of Central Florida, Nadeeshan De Silva William & Mary, Oscar Chaparro William & Mary, Andrian Marcus George Mason University, Kevin Moran University of Central Florida | ||
13:55 15m | Together We Are Better: LLM, IDE and Semantic Embedding to Assist Move Method Refactoring Research Papers Track Abhiram Bellur University of Colorado Boulder, Fraol Batole Tulane University, Malinda Dilhara Amazon Web Services, USA, Mohammed Raihan Ullah University of Colorado Boulder, Yaroslav Zharov JetBrains Research, Timofey Bryksin JetBrains Research, Kai Ishikawa NEC Corporation, Haifeng Chen NEC Laboratories America, Masaharu Morimoto NEC Corporation, Shota Motoura NEC Corporation, Takeo Hosomi NEC Corporation, Tien N. Nguyen University of Texas at Dallas, Hridesh Rajan Tulane University, Nikolaos Tsantalis Concordia University, Danny Dig University of Colorado Boulder, JetBrains Research | ||
14:10 10m | COB2PY - A Non-AI, Rule-Based COBOL to Python Translator Tool Demonstration Track Kowshik Reddy Challa Indian Institute of Technology, Tirupati, Sonith M V Indian Institute of Technology, Tirupati, Chiranjeevi B S Indian Institute of Technology Tirupati, Sridhar Chimalakonda Indian Institute of Technology Tirupati | ||
14:20 10m | How Does Test Code Differ From Production Code in Terms of Refactoring? An Empirical Study NIER Track Kosei Horikawa Nara Institute of Science and Technology, Yutaro Kashiwa Nara Institute of Science and Technology, Bin Lin Hangzhou Dianzi University, Kenji Fujiwara Nara Women’s University, Hajimu Iida Nara Institute of Science and Technology Pre-print | ||
14:30 10m | How Much Can a Behavior-Preserving Changeset Be Decomposed into Refactoring Operations? NIER Track Kota Someya Institute of Science Tokyo, Lei Chen Institute of Science Tokyo, Michael J. Decker Bowling Green State University, Shinpei Hayashi Institute of Science Tokyo DOI Pre-print | ||
14:40 15m | Governance Matters: Lessons from Restructuring the data.table OSS Project Industry Track Pedro Arantes RESHAPE LAB, Northern Arizona University, USA, Doris Amoakohene Northern Arizona University, Toby Hocking Université de Sherbrooke, Marco Gerosa Northern Arizona University, Igor Steinmacher RESHAPE LAB, Northern Arizona University, USA | ||