Gerald Mitchell

dblp:393/9704 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0003-1085-9687ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Program verification · 56% Program synthesis and code generation · 28% Software testing · 8%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Program synthesis and code generation
code translation
0.812024
Automated Validation of COBOL to Java Transformation · ASE 2024
Program verification
equivalence checking
0.812024
Automated Validation of COBOL to Java Transformation · ASE 2024
Program verification
semantic equivalence
0.812024
Automated Validation of COBOL to Java Transformation · ASE 2024
Program analysis
symbolic execution
0.212024
Automated Validation of COBOL to Java Transformation · ASE 2024
Software testing
test generation
0.212024
Automated Validation of COBOL to Java Transformation · ASE 2024

Methods — techniques the papers use, named apart from their topics

test generation · 0.8symbolic execution · 0.8large language model · 0.8
YearPublicationVenuePosition
2024 Automated Validation of COBOL to Java Transformation
abstract
Recent advances in Large Language Model (LLM) based Generative AI techniques have made it feasible to translate enterpriselevel code from legacy languages such as COBOL to modern languages such as Java or Python. While the results of LLM-based automatic transformation are encouraging, the resulting code cannot be trusted to correctly translate the original code. We propose a framework and a tool to help validate the equivalence of COBOL and translated Java. The results can also help repair the code if there are some issues and provide feedback to the AI model to improve. We have developed a symbolic-execution-based test generation to automatically generate unit tests for the source COBOL programs which also mocks the external resource calls. We generate equivalent JUnit test cases with equivalent mocking as COBOL and run them to check semantic equivalence between original and translated programs. Demo Video: https://youtu.be/aqF_agNP-lU
Atul Kumar 0002, Diptikalyan Saha, Toshiaki Yasue, Kohichi Ono, Saravanan Krishnan, Sandeep Hans, Fumiko Satoh, Gerald Mitchell, Sachin Kumar 0011
ASE8