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Yasuomi Sato

dblp:129/6607 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none

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

Software engineering, systems software and programming languages · 2 · 1 first-author · 1 since 2021Artificial intelligence and machine learning · 1 · 1 first-author

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
Software testing · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing
integration testing
0.912025
Acceleration of Automotive Software Development by Retrieval Augmented Integration Test Script Generation · ASE 2025
Software testing › test generation › automated test generation
test script generation
0.912025
Acceleration of Automotive Software Development by Retrieval Augmented Integration Test Script Generation · ASE 2025
Embedded and real-time systems › automotive embedded systems
automotive software
0.312025
Acceleration of Automotive Software Development by Retrieval Augmented Integration Test Script Generation · ASE 2025

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

retrieval-augmented generation · 1.7large language model · 1.7
YearPublicationVenuePosition
2025 Acceleration of Automotive Software Development by Retrieval Augmented Integration Test Script Generation
abstract
Improving the efficiency of software integration testing is a critical challenge in the automotive industry, particularly as Electronic Control Unit (ECU) architectures become increasingly complex. This paper addresses the automation of integration test script generation by leveraging Large Language Models (LLMs) with Retrieval Augmented Generation (RAG). Specifically, we target the phase in which test engineers translate natural language test case specifications into executable scripts for integration test environments containing hardware debug interfaces. To bridge the knowledge gap between LLMs and domain-specific test tool APIs, we construct a task-oriented vector store that incorporates both API manuals and supplemental, workflow-centric information. By combining these with prompts containing code prefixes, our method enables LLMs to generate robust and correct integration test scripts. We evaluated our approach on typical test scenarios reflecting industry practices for multi-core ECUs. While the test cases used were not directly taken from a specific development project, they closely mirror those routinely employed across numerous automotive ECU development initiatives. The proposed method successfully generated executable scripts for all cases and reduced total test execution man-hours by 43% compared to a realistic baseline of manual execution. These results demonstrate the practical benefit of context-enriched LLMs in accelerating specialized software engineering tasks within the automotive domain, and it also identifies remaining challenges in extending automation to broader aspects such as test coverage, maintainability, and seamless process integration.
Masashi Mizoguchi, Kentaro Yoshimura, Keita Nakazawa, Yasuomi Sato, Takahiro Iida, Fumio Narisawa
ASE4
1992 Program Refinement Using Generic Object
abstract
This paper introduces a generic object which behaves flexibly according to its related objects and conditions for execution in an object-oriented programming system, called ISL-xscheme. A generic object is an instance of 'Generic' class and created by providing abstract classes as a domain. It behaves adequately in the domain depending on its constraints and the messages sent to it. Generic objects are useful for various purposes and applications because they are flexibly transformed via executions depending on what they are used for and how they are used. They are especially effective for program refinement.>
Yasuomi Sato, Yasuhiko Morimoto, Tadao Ichikawa
SEKE1