Reshaping the SDLC for Data- and AI-Centric Systems
本文探讨了通过整合数据工程和软件工程实践（如DataOps、MLOps等）来重塑面向数据和AI系统的软件开发生命周期，以应对传统SDLC在处理这些系统时遇到的挑战。
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Representative Papers
Mawqif-v2: An Arabic Benchmark Dataset for Cross-Target Stance Detection
This study addresses the scarcity of publicly available datasets for evaluating cross-target generalization in Arabic stance detection. To bridge this gap, the authors introduce Mawqif-v2, an expanded dataset comprising 996 manually annotated Arabic tweets spanning three distinct topics: women driving, electric vehicles, and the trimester academic system. This work presents the first benchmark specifically designed for cross-target stance detection in Arabic, with each tweet labeled for stance, sentiment, and sarcasm. The dataset enables systematic evaluation of both zero-shot large language models and Arabic/multilingual Transformer-based approaches. By establishing reproducible baseline performance metrics, the study provides a standardized evaluation framework to advance research on cross-target generalization in Arabic stance detection.
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Educating the Agentic Engineer: Curricula, Collaboration, and Continuous Learning in the AI Era
This work addresses the inadequacy of traditional engineering education in meeting the urgent demand for a new generation of engineers equipped for the era of generative and autonomous intelligence. It proposes the ACCEL educational framework, which introduces a novel competency model for “autonomous intelligence engineers.” The framework systematically cultivates core capabilities—including intent articulation, multi-agent orchestration, output evaluation, and ethical judgment—through three integrated pathways: curriculum restructuring, collaborative mechanisms, and lifelong learning. Grounded in principal–agent theory, research on trust in automation, and international AI competency standards, ACCEL incorporates a commission–verification instructional cycle, governance-oriented ethics education, and an innovative assessment system. It identifies critical risks such as automation bias and skill atrophy, and advances engineering education beyond incremental reform by shifting its focus from artifact-centric outcomes to cultivating judgment over autonomous sociotechnical systems.
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Specification-Driven Development as the Foundation of AI-Native Enterprise Software Engineering
This study addresses the risks associated with ungoverned conversational AI deployments in enterprise settings, which often result in low reliability, architectural degradation, security vulnerabilities, and technical debt. To mitigate these challenges, the paper introduces a Specification-Driven Development (SDD) paradigm anchored by a novel Specification Governance Reference Model (SGRM). This model enforces probabilistic AI outputs through specification contracts, a three-tier rigor framework, and deterministic verification mechanisms, thereby transforming generative AI into auditable engineering practice. The approach integrates constitutional constraints, mappings to the ISO/IEC 25010 quality model, and an agent-driven delivery pipeline. Empirical evaluation demonstrates that the proposed framework simultaneously ensures regulatory compliance and safety while reducing security defects by 73% and accelerating time-to-market by 50%.
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From Determinism to Delegation: AI-Native Software Engineering and the Evolution of the Agentic Engineer
Traditional software engineering struggles to address the development of autonomous, probabilistic systems powered by large language models. This work proposes a new paradigm—AI-native software engineering—that shifts the focus from writing deterministic code to supervising agent workflows, redefining the engineer’s role as an “agent engineer” whose primary output is an intelligent agent system. The paradigm encompasses key techniques including reasoning-action loops, context engineering, tool invocation, memory mechanisms, and behavioral drift control. It emphasizes statistical evaluation to ensure reliable system behavior under uncertainty and establishes an outcome-oriented accountability framework. Drawing on empirical studies since 2022, the paper demonstrates that disciplined human-agent collaboration outperforms full automation and introduces a governance framework to mitigate emerging risks such as indirect prompt injection.
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Latest Papers
Reshaping the SDLC for Data- and AI-Centric Systems
本文探讨了通过整合数据工程和软件工程实践（如DataOps、MLOps等）来重塑面向数据和AI系统的软件开发生命周期，以应对传统SDLC在处理这些系统时遇到的挑战。
0 citationsRead paper
Mawqif-v2: An Arabic Benchmark Dataset for Cross-Target Stance Detection
This study addresses the scarcity of publicly available datasets for evaluating cross-target generalization in Arabic stance detection. To bridge this gap, the authors introduce Mawqif-v2, an expanded dataset comprising 996 manually annotated Arabic tweets spanning three distinct topics: women driving, electric vehicles, and the trimester academic system. This work presents the first benchmark specifically designed for cross-target stance detection in Arabic, with each tweet labeled for stance, sentiment, and sarcasm. The dataset enables systematic evaluation of both zero-shot large language models and Arabic/multilingual Transformer-based approaches. By establishing reproducible baseline performance metrics, the study provides a standardized evaluation framework to advance research on cross-target generalization in Arabic stance detection.
0 citationsRead paper
Educating the Agentic Engineer: Curricula, Collaboration, and Continuous Learning in the AI Era
This work addresses the inadequacy of traditional engineering education in meeting the urgent demand for a new generation of engineers equipped for the era of generative and autonomous intelligence. It proposes the ACCEL educational framework, which introduces a novel competency model for “autonomous intelligence engineers.” The framework systematically cultivates core capabilities—including intent articulation, multi-agent orchestration, output evaluation, and ethical judgment—through three integrated pathways: curriculum restructuring, collaborative mechanisms, and lifelong learning. Grounded in principal–agent theory, research on trust in automation, and international AI competency standards, ACCEL incorporates a commission–verification instructional cycle, governance-oriented ethics education, and an innovative assessment system. It identifies critical risks such as automation bias and skill atrophy, and advances engineering education beyond incremental reform by shifting its focus from artifact-centric outcomes to cultivating judgment over autonomous sociotechnical systems.
0 citationsRead paper
Specification-Driven Development as the Foundation of AI-Native Enterprise Software Engineering
This study addresses the risks associated with ungoverned conversational AI deployments in enterprise settings, which often result in low reliability, architectural degradation, security vulnerabilities, and technical debt. To mitigate these challenges, the paper introduces a Specification-Driven Development (SDD) paradigm anchored by a novel Specification Governance Reference Model (SGRM). This model enforces probabilistic AI outputs through specification contracts, a three-tier rigor framework, and deterministic verification mechanisms, thereby transforming generative AI into auditable engineering practice. The approach integrates constitutional constraints, mappings to the ISO/IEC 25010 quality model, and an agent-driven delivery pipeline. Empirical evaluation demonstrates that the proposed framework simultaneously ensures regulatory compliance and safety while reducing security defects by 73% and accelerating time-to-market by 50%.
0 citationsRead paper
From Determinism to Delegation: AI-Native Software Engineering and the Evolution of the Agentic Engineer
Traditional software engineering struggles to address the development of autonomous, probabilistic systems powered by large language models. This work proposes a new paradigm—AI-native software engineering—that shifts the focus from writing deterministic code to supervising agent workflows, redefining the engineer’s role as an “agent engineer” whose primary output is an intelligent agent system. The paradigm encompasses key techniques including reasoning-action loops, context engineering, tool invocation, memory mechanisms, and behavioral drift control. It emphasizes statistical evaluation to ensure reliable system behavior under uncertainty and establishes an outcome-oriented accountability framework. Drawing on empirical studies since 2022, the paper demonstrates that disciplined human-agent collaboration outperforms full automation and introduces a governance framework to mitigate emerging risks such as indirect prompt injection.
0 citationsRead paper