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Takaaki Tateishi

Publications and source records attributed to Takaaki Tateishi.

2 recordsLinked to original sources

Translating PL/I Macro Procedures into Java Using Automatic Templatization and Large Language Models

Modernizing legacy enterprise systems often involves translating PL/I programs into modern languages such as Java. This task becomes significantly more complex when PL/I macro procedures are involved. The PL/I macro procedures are considered string-manipulating programs that generate PL/I code, and they make automated translation more complex. Recently, large language models (LLMs) have been explored for automated code translation. However, LLM-based code translation struggles to translate the PL/I macro procedures to Java programs that reproduce the behavior of the plain PL/I code generated by the original PL/I macro procedures. This paper proposes a novel method called templatization, which uses symbolic execution to generate code templates (code with named placeholders) as an intermediate representation. In this approach, symbolic values are treated as parts of macro-generated code. By symbolically executing macro procedures and generating code templates, our approach facilitates LLMs to generate readable and maintainable Java code. Our preliminary experiment on ten PL/I macro procedures shows that the LLM-based translation through templatization successfully generates Java programs that reproduce the behavior of the macro-generated PL/I programs.

cs.SE↗

Matching of Descriptive Labels to Glossary Descriptions

Semantic text similarity plays an important role in software engineering tasks in which engineers are requested to clarify the semantics of descriptive labels (e.g., business terms, table column names) that are often consists of too short or too generic words and appears in their IT systems. We formulate this type of problem as a task of matching descriptive labels to glossary descriptions. We then propose a framework to leverage an existing semantic text similarity measurement (STS) and augment it using semantic label enrichment and set-based collective contextualization where the former is a method to retrieve sentences relevant to a given label and the latter is a method to compute similarity between two contexts each of which is derived from a set of texts (e.g., column names in the same table). We performed an experiment on two datasets derived from publicly available data sources. The result indicated that the proposed methods helped the underlying STS correctly match more descriptive labels with the descriptions.

cs.CL↗