Parsing Fortran-77 with proprietary extensions
September 05, 2023 Β· Declared Dead Β· π IEEE International Conference on Software Maintenance and Evolution
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Younoussa Sow, Larisa Safina, LΓ©andre Brault, Papa Ibou Diouf, StΓ©phane Ducasse, Nicolas Anquetil
arXiv ID
2309.02019
Category
cs.SE: Software Engineering
Citations
1
Venue
IEEE International Conference on Software Maintenance and Evolution
Last Checked
5 months ago
Abstract
Far from the latest innovations in software development, many organizations still rely on old code written in "obsolete" programming languages. Because this source code is old and proven it often contributes significantly to the continuing success of these organizations. Yet to keep the applications relevant and running in an evolving environment, they sometimes need to be updated or migrated to new languages or new platforms. One difficulty of working with these "veteran languages" is being able to parse the source code to build a representation of it. Parsing can also allow modern software development tools and IDEs to offer better support to these veteran languages. We initiated a project between our group and the Framatome company to help migrate old Fortran-77 with proprietary extensions (called Esope) into more modern Fortran. In this paper, we explain how we parsed the Esope language with a combination of island grammar and regular parser to build an abstract syntax tree of the code.
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