Skills for the future software profession: beyond agentic AI!
- URL: http://arxiv.org/abs/2606.21894v2
- Date: Tue, 23 Jun 2026 05:52:26 GMT
- Title: Skills for the future software profession: beyond agentic AI!
- Abstract summary: We summarize the results of two round-tables with researchers and industrial practitioners, held in 2026 in New York and Singapore.<n>One key finding is that verification and validation is increasing in importance as agents handle implementation.<n>We identify the skills developers need in the agentic era of development, with implications for training and educating future software engineers.
- Score: 20.895857090846885
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As coding agents are rapidly changing software engineering, a natural question is: what are the core skills needed by future software engineers? To identify where software engineering is headed and thus what skills will be needed, we summarize the results of two round-tables with researchers and industrial practitioners, held in 2026 in New York and Singapore. One key finding is that verification and validation is increasing in importance as agents handle implementation, as highlighted by anecdotes from the events. From our observations, we identify the skills developers need in the agentic era of development, with implications for training and educating future software engineers in coming years.
Related papers
- From Determinism to Delegation: AI-Native Software Engineering and the Evolution of the Agentic Engineer [0.0]
Software engineering is experiencing its most significant transformation since the emergence of high-level programming languages.<n>This paper argues that AI-Native Software Engineering is a paradigm shift rather than a mere tooling advance.<n>We define core mechanisms of autonomous agents, including reasoning-acting loops, context engineering, tool use, memory, behavioral drift, and compositional error.
arXiv Detail & Related papers (2026-06-27T07:43:43Z) - Reshaping Undergraduate Computer Science Education in the Generative AI Era [85.78707817763434]
Generative AI represents a turning point for Computer Science (CS) education.<n>To keep pace with advances in AI technology, CS curricula should consider a shift toward understanding and verifying AI-generated artifacts.<n>We believe that to effectively prepare future computer science graduates, capable of creating, solving problems, and managing, as well as co-creating, artifacts with AI.
arXiv Detail & Related papers (2026-05-02T05:26:24Z) - Agentic AI for Software: thoughts from Software Engineering community [9.966138715949205]
At the code level, common software tasks include code generation, testing, and program repair.<n>Key to successfully developing agentic AI-based software will be to resolve the core difficulty in software engineering - the deciphering and clarification of developer intent.<n>A successful deployment of agentic technology into software engineering would involve making conceptual progress in such intent inference via agents.
arXiv Detail & Related papers (2025-08-24T12:57:21Z) - Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows [60.04362496037186]
We present the first controlled study of developer interactions with coding agents.<n>We evaluate two leading copilot and agentic coding assistants.<n>Our results show agents can assist developers in ways that surpass copilots.
arXiv Detail & Related papers (2025-07-10T20:12:54Z) - Unified Software Engineering agent as AI Software Engineer [14.733475669942276]
Large Language Model (LLM) technology has raised expectations for automated coding.<n>In this paper, we seek to understand this question by developing a Unified Software Engineering agent or USEagent.<n>We envision USEagent as the first draft of a future AI Software Engineer which can be a team member in future software development teams involving both AI and humans.
arXiv Detail & Related papers (2025-06-17T16:19:13Z) - What do professional software developers need to know to succeed in an age of Artificial Intelligence? [1.5774644021279332]
Generative AI is showing early evidence of productivity gains for software developers, but concerns persist regarding workforce disruption and deskilling.<n>We describe our research with 21 developers at the cutting edge of using AI, summarizing 12 of their work goals we uncovered, together with 75 associated tasks and the skills & knowledge for each.<n>We found that the skills & knowledge to be a successful AI-enhanced developer are organized into four domains.
arXiv Detail & Related papers (2025-05-30T20:14:03Z) - Challenges and Paths Towards AI for Software Engineering [55.95365538122656]
We discuss progress in AI for software engineering in threefold manner.<n>First, we provide a structured taxonomy of concrete tasks in AI for software engineering.<n>Second, we outline several key bottlenecks that limit current approaches.
arXiv Detail & Related papers (2025-03-28T17:17:57Z) - OpenHands: An Open Platform for AI Software Developers as Generalist Agents [109.8507367518992]
We introduce OpenHands, a platform for the development of AI agents that interact with the world in similar ways to a human developer.<n>We describe how the platform allows for the implementation of new agents, safe interaction with sandboxed environments for code execution, and incorporation of evaluation benchmarks.
arXiv Detail & Related papers (2024-07-23T17:50:43Z) - Artificial Intelligence Impact On The Labour Force -- Searching For The
Analytical Skills Of The Future Software Engineers [0.0]
This systematic literature review aims to investigate the impact of artificial intelligence on the labour force in software engineering.
It focuses on the skills needed for future software engineers, the impact of AI on the demand for software engineering skills, and the future of work for software engineers.
arXiv Detail & Related papers (2023-02-26T03:49:53Z) - Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions [54.55334589363247]
We study whether conveying information about uncertainty enables programmers to more quickly and accurately produce code.
We find that highlighting tokens with the highest predicted likelihood of being edited leads to faster task completion and more targeted edits.
arXiv Detail & Related papers (2023-02-14T18:43:34Z) - Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
This chapter focuses on differentiable intelligence and on-board machine learning.
We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
arXiv Detail & Related papers (2022-12-10T07:49:50Z) - Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring [81.06807079998117]
We propose a novel approach to enable Automated Machine Learning (AutoML) for Non-Intrusive Appliance Load Monitoring (NIALM)<n>NIALM offers a cost-effective alternative to smart meters for measuring the energy consumption of electric devices and appliances.
arXiv Detail & Related papers (2022-03-06T10:12:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.