Demo Data from SIGIR 2026 / reviewers in the wild / expert
Ketai Qiu
dblp:375/9671
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
0009-0002-9750-2762ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software 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 |
Empirical software engineering · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Human-AI interaction · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering › developer studies › developer behavior
developer productivity |
0.9 | 1 | 2025 | From Today's Code to Tomorrow's Symphony: The AI Transformation of Developer's Routine by 2030 · ACM Trans. Softw. Eng. Methodol. 2025 |
Human-AI interaction › human-AI collaboration
AI-assisted software development |
0.3 | 1 | 2025 | From Today's Code to Tomorrow's Symphony: The AI Transformation of Developer's Routine by 2030 · ACM Trans. Softw. Eng. Methodol. 2025 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | From Today's Code to Tomorrow's Symphony: The AI Transformation of Developer's Routine by 2030abstractIn the rapidly evolving landscape of software engineering, the integration of AI into the Software Development Lifecycle (SDLC) heralds a transformative era for developers. Recently, we have assisted to a pivotal shift toward AI-assisted programming, exemplified by tools like GitHub Copilot and OpenAI’s ChatGPT, which have become a crucial element for coding, debugging, and software design. In this article, we provide a comparative analysis between the current state of AI-assisted programming in 2024 and our projections for 2030, by exploring how AI advancements are set to enhance the implementation phase, fundamentally altering developers’ roles from manual coders to orchestrators of AI-driven development ecosystems. We envision HyperAssistant , an augmented AI tool that offers comprehensive support to 2030 developers, addressing current limitations in mental health support, fault detection, code optimization, team interaction, and skill development. We emphasize AI as a complementary force, augmenting developers’ capabilities rather than replacing them, leading to the creation of sophisticated, reliable, and secure software solutions. Our vision seeks to anticipate the evolution of programming practices, challenges, and future directions, shaping a new paradigm where developers and AI collaborate more closely, promising a significant leap in SE efficiency, security, and creativity. Ketai Qiu, Niccolò Puccinelli, Matteo Ciniselli, Luca Di Grazia |
ACM Trans. Softw. Eng. Methodol. | 1 |