SEARCH · Search arXiv

Results for “cs.SE”

Search indexed arXiv papers on artificial intelligence, large language models, computer vision and robotics. Read source abstracts and follow links to arXiv.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

7,800 recordsLinked to original sources

arXiv2026

The Impact of GenAI on the Future of Requirements Engineering

Recent advances in artificial intelligence (AI), particularly large language models (LLMs), are transforming how we design and build systems by increasing access to domain knowledge and by providing automation support to software engineering (SE). As implementation becomes less expensive through generalist SE agents, engineering effort shifts away from writing correct code and toward expressing, curating, verifying, and evaluating requirements. In this paper, we survey the state of the art in AI for requirements engineering (RE) research leading up to the transformation, before reviewing advances in LLMs. We survey two subsequent research areas: prompt programming, which treats LLM instructions as a program in SE vernacular, and generalist SE agents, which combine multiple LLM advances to yield semi-autonomous processes that complete SE tasks. Finally, we explore the future of requirements engineering along two axes: matters changing how we interact with requirements through the SE process, and matters changing how requirements are experienced by software developers and stakeholders more broadly, including end-users. This article aims to inform how RE researchers can navigate this transformation in the selection of future research priorities.

cs.SE↗

arXiv2026

From Prompting to Engineering: A Research Agenda for Prompt Engineering in Software Engineering

Prompt engineering is increasingly used across Software Engineering (SE) activities, including requirements analysis, coding, testing, documentation, repository analysis, and planning. Yet prompts and related instruction artifacts are often created and evolved through task-specific and informal practices, with limited support for their systematic evaluation, management, traceability, and governance. To examine how SE can contribute to the maturation of these practices, we organized a structured community discussion at the First International Workshop on Empirical Prompt Engineering for Software Engineering (PROMPT-SE), co-located with EASE 2026. Participants discussed current prompting practices, challenges to their adoption and evaluation, and future directions for integrating prompt engineering into software development. We synthesized these discussions into five areas: prompt artifacts and standardization; evaluation and benchmarking; lifecycle integration; human-AI collaboration and skills; and governance, privacy, and technical debt. Based on these areas, we outline a research agenda to move prompt engineering from predominantly ad hoc interactions toward more systematic, maintainable, evaluable, traceable, and governable SE practices.

cs.SE↗

arXiv2026

Antipatterns in AI-assisted Qualitative Data Analysis: A Catalog of Temptations and Pitfalls for Software Engineering Researchers

AI-assisted qualitative data analysis (QDA) offers unprecedented opportunities to streamline software engineering (SE) research, yet uncritical use risks compromising analytical rigor and flooding the field with accelerated production of low-quality research. While tactical best practices will naturally evolve over time, SE researchers currently lack strategic guidance to identify and mitigate methodological risks when attempting AI-assisted QDA. Based on our decades of qualitative SE research expertise and experience combined with an understanding of the emerging landscape of AI-assisted QDA, this paper presents a catalog of antipatterns in AI-assisted QDA - a set of assumptions and practices that initially appear advantageous but ultimately undermine analytical rigor and validity. The antipatterns are grouped into three categories reflecting escalating impact: Dangerous Drivers, Operational Missteps, and Analytical Failures. As more SE researchers attempt AI-assisted QDA, these antipatterns will help them identify and avoid common temptations and pitfalls, while reviewers can be equipped with the vocabulary and criteria to call out problematic and failed practice. Ultimately, this catalog of antipatterns can serve as a stepping stone in our responsible methodological evolution toward principled and meaningful human-AI collaboration in qualitative research.

cs.SE↗

arXiv2026

What Does an Evaluation License? A Commit-Bound Census of Claim Replay in Inspect Evals

Benchmarks can run without determining what their results license. We freeze a large evaluation collection and attempt to replay its historical claims. Most units stop because the evidence required for replay is not bound. Where replay is possible, different claims remain stable at different resolutions. We make this otherwise implicit inference step explicit and executable.

cs.SE↗

arXiv2026

Essence Coach: A Bot for Software Practice Adoption

Although Essence has been proposed as a unifying framework for understanding and evaluating software engineering practices, its adoption remains challenging, calling for tools that can act as coaches for learners and practitioners. We present Essence Coach, a chatbot that integrates LLMs with retrieval-augmented generation (RAG) from a curated Essence knowledge base. Experiments with multiple LLMs show that RAG consistently improves response quality for Essence-related queries. Some preliminary tests suggest that such systems can help both technical and non-technical students understand and apply Essence concepts. While validation through broader user studies is needed, this work-in-progress highlights how LLMs can bridge abstract SE frameworks and practice adoption.

cs.SE↗

arXiv2026

Towards a Reliable and Practical Eval Pipeline

LLM-based software systems increasingly require effective "evals" as quality gates in the development lifecycle. However, existing work typically addresses individual aspects of eval reliability rather than the full set of practical requirements. We present an end-to-end eval pipeline that combines eval checklist creation, with learned aggregation for checklist responses, to improve agreement across LLM judges and accuracy against human judgments. The framework additionally pro- vides self-consistency, explanations, and prediction uncertainty, and we empirically demonstrate its effectiveness.

cs.AI↗

arXiv2026

TSExplorer: An interactive data annotation and exploration tool for time-series data

We present TSExplorer, a cross-platform tool for interactive annotation and exploration of time-series data. The tool enables users to inspect high-dimensional datasets through multiple complementary 2D visualizations derived from high-dimensional feature representations. TSExplorer is designed as a general-purpose research tool supporting a wide range of workflows, including exploratory data analysis, annotation of unlabeled or partially-labeled datasets, comparison of feature representations, and post-hoc inspection and refinement of existing labels with interactive visual feedback.

cs.HC↗

arXiv2026

Masked Autoregressive Speech Enhancement with Continuous Neural Audio Codec Representations

Most previous work on speech enhancement (SE) based on masked generative modeling relied on discrete token representations of audio signals, obtained using neural audio codecs (NACs). However, a recent study has shown that continuous latent representations of NACs can be advantageous for SE in terms of speech quality and intelligibility. In this work, we propose masked autoregressive SE (MARSE), a method for SE based on iterative decoding of masked clean speech frames using continuous NAC representations of speech. In particular, we investigate a set of different decoding policies, ceteris paribus, that is, using the same DNN (a Conformer model), the same NAC (the DAC codec) and the same training setup. The results show that MARSE enables a flexible trade-off between SE performance and computational cost. Audio examples and code are available online.

cs.SD↗

arXiv2026

Architecting Conversational Data Systems for Stateless LLM APIs: The Hydration Proxy Pattern

As enterprise platforms transition to conversational reasoning interfaces, the stateless nature of LLM APIs creates an architectural gap. While statelessness enables horizontal scalability for AI providers, it forces client applications to manage the entire burden of conversational state and semantic memory. The work identifies the Hydration Proxy Pattern, an architecture that decouples session persistence from the reasoning engine. The framework ensures platform sovereignty over conversational data while enabling secure, multi-stage semantic grounding. We further propose the Context Stabilization Mandate to resolve the tradeoff between sovereign state management and KV caching.

cs.AI↗

arXiv2026

The Psychological Costs of Artificial Intelligence Adoption in Software Engineering

Artificial intelligence (AI) is increasingly used to augment software engineering (SE) workflows. While code generation remains the main use case, organizations are actively seeking AI integration in other practices such as test cases generation and code reviews. Organizational AI adoption strategies seem to focus on tangible outcomes such as productivity. However, AI is a disruptive force, introduced into settings where role identity, team norms, and the sources of job satisfaction were well established before the recent advances in generative AI. Historically, technological disruptions have caused psychological and social strains in workplaces, ranging from anxiety and eroded meaning to deskilling and disrupted professional identities. The assumption that AI for SE is cost-free may not be accurate. Therefore, in this study we sought to understand the psychological costs software professionals experience during organizational AI adoption. We carried out a case study in a large software development services company, one year after the company launched its AI adoption. We collected qualitative data through meetings and semi-structured interviews (N = 21). We found that software professionals experience accountability anxiety, craft identity disruption, meaning and satisfaction erosion, cognitive and workload intensification, and uncertainty distress. Practitioners manage these costs through practices that restore control, mitigate them through protective and identity-preserving adaptations, or absorb them, carrying what neither can resolve. We contribute to AI-human collaboration in SE by repositioning AI adoption as a human transition, not only a technological and organizational one.

cs.SE↗

arXiv2026

Augmenting software engineering with AI - The ai4se taxonomy and its use

Although model-driven software engineering (MDSE) has proven effective in managing complex systems, its industrial adoption remains limited by the substantial maintenance overhead required for models and the specialised skills demanded of developers. Meanwhile, advances in artificial intelligence (AI), particularly generative and agentic AI, have shown great promise in automating code-related tasks such as comprehension, generation, and defect detection. These capabilities are largely powered by 'big code': vast repositories of open-source software that now form the basis of data-driven, empirical SE and automated quality assurance. This paper aims to synthesise these two domains by exploring the integration of AI into model-driven practices. It provides a comprehensive overview of the current state of AI-augmented software engineering and introduces a novel taxonomy 'ai4se' to classify and connect diverse AI applications within the field. On this basis, the paper proposes a vision for 'big models' in software engineering (SE), an approach designed to leverage the structural advantages of MDSE alongside the scalability of AI. Finally, the paper discusses the pair modelling paradigm as a collaborative framework for the MDSE industry, designed to enhance software quality through human-AI partnership.

cs.SE↗

arXiv2026

SH-PDOPS: AI-Driven Cloud Native Enterprise Reliability Framework for Predictive Analytics and Intelligent DevOps Automation

This paper presents an AI-driven cloud-native enterprise reliability framework designed to improve predictive analytics, intelligent DevOps automation, and system resilience in modern distributed infrastructures. The proposed framework integrates machine learning models, Kubernetes orchestration, observability pipelines, and automated incident response mechanisms to enhance reliability engineering practices. The study explores predictive failure detection, anomaly monitoring, self-healing infrastructure, and CI/CD optimization using cloud-native technologies. Experimental evaluation demonstrates improved operational efficiency, reduced downtime, and enhanced scalability for enterprise environments. The framework provides a practical approach for combining artificial intelligence with DevOps methodologies to achieve adaptive and autonomous infrastructure management.

cs.SE↗

arXiv2026

Beyond Vector Search: Comparing Classical RAG with Hybrid GraphRAG for Climate Science Q\&A

Traditional Retrieval-Augmented Generation (RAG) systems treat documents in isolation, failing to capture hierarchical relationships between concepts in complex scientific corpora. This limitation compromises answer quality in specialized domains such as climatology, where conceptual dependencies frequently traverse multiple articles. We propose a hybrid architecture that integrates vector search with GraphRAG, Leiden community detection, and cross-encoder re-ranking, achieving gains of 160\% in contextual relevance and 177\% in contextual recall compared to classical RAG. These results demonstrate that unifying local and global retrieval significantly outperforms text-span isolation, paving the way for more effective question-answering systems over dispersed scientific literature.

cs.SE↗

arXiv2026

Skills for the future software profession: beyond agentic AI!

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.

cs.SE↗

arXiv2026

Beyond Locks and Thread IDs: Static Data Race Detection Off The Beaten Path (Extended Version)

Maintaining an abstraction of the execution history of threads can improve the precision of data race detection in static analysis. Here, we extend the digest framework to handle concurrency constructs and synchronization mechanisms that have been ignored in static race detection. We introduce mechanisms for the commonly used thread barriers, as well as pthread_once, which allows to ensure that an action is executed only once. We also instantiate the framework with an abstraction of locksets held by ancestor threads. We propose a suite of litmus tests to evaluate analyses for these features and compare our implementation to state-of-the-art tools, finding that they lack support.

cs.PL↗

arXiv2026

Exploring Quantum Software Testing Across Research and Practice: Emerging Results from a Multivocal Literature Review

This paper presents preliminary findings from a multivocal literature review investigating how quantum software testing is characterized across academic and practitioner-oriented sources. Our study integrated peer-reviewed studies with gray literature, including blogs, tutorials, forums, technical reports, documentation pages, and company webpages. Our results indicate a rapidly evolving but fragmented ecosystem involving classical adapted testing approaches, quantum-specific techniques, statistical validation methods, simulators, debugging environments, and verification frameworks. The reviewed material also revealed recurring challenges related to scalability limitations, hardware noise, probabilistic execution, limited observability, and immature tooling ecosystems. These findings provide an initial characterization of how research and practice currently discuss quantum software testing challenges, techniques, and tooling.

cs.SE↗

arXiv2026

ParaStudent: Closing the Sim2Real Gap in User Simulators for AI Tutor Evaluation

Evaluating Artificial Intelligence (AI) tutor feedback before deployment requires anticipating student engagement, typically assessed through real interaction data. We introduce ParaStudent, a fine-tuning framework for simulating novice programming revisions to support AI tutor evaluation. Compared with prompted baselines, ParaStudent's revisions more closely match real student code distributions across functional, stylistic, and semantic metrics. Our best variant achieves AUCs of 0.80 for both feedback relevance and successful uptake when distinguishing streams with real engagement above versus at or below the median, while prompted baselines remain near chance on successful uptake. These findings demonstrate the promise of simulated engagement for pre-deployment feedback triage.

cs.CY↗

arXiv2026

Quantisation of Abstract Data Types

In this paper, we introduce a notion of abstract quantum data type within the framework of universal algebra. This notion provides an algebraic foundation for describing data abstraction in quantum programming. We formally define a quantisation of classical data types and show that their equational specifications can be soundly lifted to the quantum setting. Two standard quantisation methods for classical functions, namely the bit oracle and the phase oracle, arise as special cases of this general construction. We illustrate the framework with applications to quantum arrays and quantum error-correcting codes, showing how they can be understood through the lens of data-type quantisation. We further establish conditions under which quantisation preserves structural relationships and constructions of classical data types, including embeddings, isomorphisms, and products.

quant-ph↗