Demo Data from VLDB 2026 / reviewers in the wild / expert
Konrad Cinkusz
dblp:393/9571
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2025
0009-0001-5709-1172ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 first-author · 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 synthesis and code generation · 77% Empirical software engineering · 23% | |
| Artificial intelligence
1 paper |
Multi-agent systems · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Program synthesis and code generation › code generation with language models
software engineering agents |
0.8 | 1 | 2024 | Towards LLM-augmented multiagent systems for agile software engineering · ASE 2024 |
Empirical software engineering
agile software development |
0.2 | 1 | 2024 | Towards LLM-augmented multiagent systems for agile software engineering · ASE 2024 |
Methods — techniques the papers use, named apart from their topics
theory of mind · 1.5large language model · 1.5dynamic context · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Agile Software Management with Cognitive Multi-Agent Systems
Konrad Cinkusz, Jaroslaw A. Chudziak |
ICAART (1) | 1 |
| 2024 | Towards LLM-augmented multiagent systems for agile software engineeringabstractA cognitive multi-agent ecosystem designed for efficient software engineering using Agile methodologies can significantly improve software development processes. Key components include the integration of Multi-Agent Systems (MAS) and Large Language Models (LLMs), utilizing Dynamic Context techniques for agent profiling, and Theory of Mind to enhance collaboration. The CogniSim Ecosystem analyzes problems, proposes solutions, constructs and validates plans, and coordinates specialized agents playing roles such as developers, executors, quality checkers, and methodology reviewers. These agents produce documentation, models, and diagrams (e.g., UML) while adhering to predefined quality and performance measures. The ecosystem also simulates the impact of various team configurations on problem-solving effectiveness, helping organizations identify optimal team structures. Case studies and simulations demonstrate its practical applications. Konrad Cinkusz, Jaroslaw A. Chudziak |
ASE | 1 |