Knowledge catalogue
arXiv:2604.20436v1 Announce Type: cross Abstract: Generative AI (GenAI) is reshaping software engineering by shifting development from manual coding toward agent-driven implementation. While vibe codi
arXiv:2604.19827v1 Announce Type: cross Abstract: Software engineering faces a fundamental challenge: multi-agent AI systems fail in ways that defy explanation by traditional theories. While individua
arXiv:2511.03690v2 Announce Type: replace-cross Abstract: Agents are now used widely in the process of software development, but building production-ready software engineering agents is a complex task
arXiv:2505.07849v2 Announce Type: replace-cross Abstract: Software issue localization, the task of identifying the precise code locations (files, classes, or functions) relevant to a natural language
arXiv:2609.11321v1 Announce Type: new Abstract: Artificial intelligence is changing both software production and the economics of software-based business models. Classical technology due diligence mai
Anthropic’s CEO says software engineering is dying. Anthropic’s job listing has 70 open positions in software engineering. 🙄 @GaryMarcus https://www.anthropic.com/careers/jobs 70 opened position in SE
Software engineering legend @Grady_Booch re Dario: I think that @DarioAmodei does not understand software engineering and that he is working feverishly to pump up the valuation of his company in antic
arXiv:2606.05608v1 Announce Type: cross Abstract: For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into s
Financial Times: Sources: software-specialist PE firm Hg agrees to acquire Rightsline, whose software helps Disney, the BBC, WBD, and others manage IP licensing, for 500M — Acquisition of rights and r
Palantir CEO Alex Karp goes after AI slop. The fight over AI “slop” is really a fight over whether software is performing or merely pretending. 'The appearance of software working is not software work
When a software company actually uses its own software, all of their users benefit. @runwayml is exactly this type of company. They obviously take great care in the entire workflow that users desire.
In this interview at @aiDotEngineer World's Fair, @vercel chief of software @andrewqu explains why agents represent a new form of software, what Vercel learned from building its own, and why Vercel it
Julie Bort / TechCrunch: Insurance tech startup Corgi denies accusations that it used Papermark's open source software code to develop its software and present it as its own — Y Combinator-backed insu
We raised $8m to build self-healing software. In 2026, software moves fast. But monitoring and observability are still manual and slow. @sazabi is a next-generation observability platform for fast-mov
This is exactly what we're seeing. The 'AI for everything' era is really the 'software for everyone' era. Every SMB becomes a software company. You don’t need to build the next J.A.R.V.I.S. You need t
Open source software will be many times more secure than closed source software in the new Mythos era they: OpenClaw is so insecure look at all these GHSAs! reality: we are just an indicator of the co
As AI agents accelerate coding, what is the future of software engineering? Some trends are clear, such as the Product Management Bottleneck, referring to the idea that we are more constrained by deci
'Claude Code isn't magic. The harness layer is just software, and software is something any dev can shape to fit how they want to work.' Check out @Hacubu’s practical guide to building a custom agent
Agentic coding lets developers—even those who previously focused on front‑end or mobile work—delegate code generation to AI agents and assume broader full‑stack roles. However, understanding core soft
arXiv:2512.22256v2 Announce Type: replace-cross Abstract: Software issue resolution aims to address real-world issues in software repositories based on natural language descriptions provided by users,
arXiv:2608.21107v1 Announce Type: new Abstract: Large Language Models (LLMs) are moving from code completion toward repository-scale agents that retrieve context, edit files, execute tools, and partic
arXiv:2608.10025v1 Announce Type: new Abstract: For safety-critical software, data from the software's operational past (e.g. a sequence of success and failure events experienced by the software) can
arXiv:2608.10450v1 Announce Type: cross Abstract: Complex software systems develop over timescales that exceed the lifespan of any individual coding agent. Most agentic software systems preserve conti
arXiv:2608.20201v1 Announce Type: new Abstract: Software form has undergone two paradigm shifts since its inception: Software 1.0, in which instructions determine behavior, and Software 2.0, in which
arXiv:2512.14012v2 Announce Type: replace-cross Abstract: The rise of AI agents is transforming how software can be built. The promise of agents is that developers might write code quicker, delegate m
arXiv:2505.13766v5 Announce Type: replace-cross Abstract: Software Quality Assurance (SQA) is critical for delivering reliable, secure, and efficient software products. The Software Quality Assurance
arXiv:2604.24822v1 Announce Type: cross Abstract: Context: Software vulnerabilities pose significant security threats to software systems, especially as software is increasingly used across many areas
arXiv:2507.18625v3 Announce Type: replace-cross Abstract: Graphical user interface (UI) software has undergone a fundamental transformation from traditional two-dimensional (2D) desktop/web/mobile int
Qodo 3.0 makes a statement: Engineering teams want to build their own software factory, not buy a black box. @QodoAI provides the quality, governance, plus a map of your software and your team’s triba
arXiv:2609.37849v1 Announce Type: cross Abstract: Scientific software is increasingly required to process larger datasets while maintaining acceptable execution times. Software optimization traditiona
arXiv:2609.37294v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for software engineering tasks, yet their stochastic behavior challenges the validity, reproducibil
arXiv:2609.36161v1 Announce Type: cross Abstract: Can coding agents restore software that no longer runs while preserving its underlying methods, and reconstruct industrial software engines from open
arXiv:2607.18970v2 Announce Type: replace-cross Abstract: Agent Skills have become persistent behavioral artifacts across independent AI agent systems. They combine natural-language task specification
arXiv:2301.03709v3 Announce Type: replace-cross Abstract: Consistent and holistic expression of software requirements is important for the success of software projects. In this study, we aim to enhanc
arXiv:2605.01392v1 Announce Type: cross Abstract: Recent advancements in Large Language Models (LLMs) have demonstrated significant potential across a wide range of software engineering tasks, includi
AI-native software engineering teams operate very differently than traditional teams. The obvious difference is that AI-native teams use coding agents to build products much faster, but this leads to
arXiv:2604.13826v1 Announce Type: cross Abstract: Sentiment analysis in software engineering focuses on understanding emotions expressed in software artifacts. Previous research highlighted the limita
arXiv:2607.16660v2 Announce Type: replace-cross Abstract: The increasing adoption of Large Language Models (LLMs) as AI components in modern software systems introduces distinct security risks to the
arXiv:2604.04226v2 Announce Type: replace-cross Abstract: The Agentic Web is emerging as a paradigm in which autonomous software agents interact with online resources and with each other to accomplish
arXiv:2602.02262v3 Announce Type: replace-cross Abstract: LLM-powered coding agents are redefining how real-world software is developed. To drive the research towards better coding agents, we require
arXiv:2605.17611v1 Announce Type: cross Abstract: Software fault prediction (SFP) is a critical task in software engineering, enabling early identification of faults in modules to improve software qua
arXiv:2610.13022v1 Announce Type: cross Abstract: As LLMs become increasingly capable of implementing software, a critical bottleneck emerges: whether they truly understand what users want. Users ofte
Momentic Inc., the artificial intelligence-powered software testing and quality assurance platform, today announced the launch of Mo, an AI agent that lets developers test software without maintaining
arXiv:2609.24348v1 Announce Type: cross Abstract: AI coding agents increasingly support software development beyond code completion, including planning, implementation, testing, and repository-level t
arXiv:2609.08149v1 Announce Type: new Abstract: SWE-Bench Pro has emerged as a standard benchmark for evaluating software engineering agents on challenging repository-level tasks. However, our analysi
arXiv:2606.27406v1 Announce Type: cross Abstract: Software engineering, whether performed by humans or by AI agents, requires reasoning about how software behaves. We call the internal model that supp
arXiv:2604.23257v1 Announce Type: cross Abstract: Software engineering (SE) organizations operate in a knowledge-intensive domain where critical assets -- architectural expertise, design rationale, an
arXiv:2604.15468v1 Announce Type: cross Abstract: AI-based systems, currently driven largely by LLMs and tool-using agentic harnesses, are increasingly discussed as a possible threat to software engin
arXiv:2610.08651v1 Announce Type: cross Abstract: Software-engineering agents can enable people without formal software training to build systems they could not otherwise implement and simultaneously
arXiv:2610.07832v1 Announce Type: cross Abstract: Large language models (LLMs) equipped with terminal access have demonstrated strong capabilities in automating software engineering tasks. However, ex
arXiv:2609.21667v1 Announce Type: cross Abstract: Software is a first-class scientific object, yet validated links between source code and the scholarly record remain largely absent from the Linked Op
arXiv:2609.16302v1 Announce Type: cross Abstract: When a coding agent returns to existing software, it inherits evidence from earlier engineering work: tests, type checks, proofs, static analyses, and
arXiv:2609.10248v1 Announce Type: cross Abstract: Traditional software delivery assumes a static paradigm: code is constructed prior to execution and deployed as a fixed artifact. We present Agentic J
arXiv:2609.04711v1 Announce Type: cross Abstract: Generative AI and coding agents can accelerate research software development, but they also increase the need for efficient software discovery and mai
arXiv:2608.07317v1 Announce Type: cross Abstract: The AI-Native Manifesto envisions large-scale agile software development in which humans increasingly govern intent, risk, and exceptions while agents
arXiv:2607.27146v1 Announce Type: cross Abstract: Coding agents have made substantial progress on software engineering tasks that modify existing codebases, including bug fixing and feature implementa
arXiv:2607.21832v1 Announce Type: cross Abstract: Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agen
arXiv:2606.29520v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used as assistants across the software development lifecycle, yet their ability to reason about software
arXiv:2606.21894v2 Announce Type: replace-cross Abstract: As coding agents are rapidly changing software engineering, a natural question is: what are the core skills needed by future software engineer
A fundamental problem with extending Codex/Cowork/Code to all knowledge work is that they remain very 'software-brained' where the end result (the software) is what is important & that code serves as