AI-assisted development tools enable software engineers to generate implementations at substantially higher speed and volume than in traditional workflows, yet relatively little is known about how existing guardrails evolve in response.
Abstract
AI-assisted development tools enable software engineers to generate implementations at substantially higher speed and volume than in traditional workflows. Software teams have long relied on guardrails -- standing control mechanisms such as code review, linting, testing, and CI/CD pipelines -- to maintain quality and coordination. High-throughput AI-assisted generation increases pressure on these guardrails -- straining their capacity to keep pace with the volume and rate of generated changes -- and reshapes how organizations supervise development workflows, yet relatively little is known about how existing guardrails evolve in response. We conducted a qualitative interview study with five software engineering practitioners, situated within a broader practitioner survey. Our findings indicate that organizations distribute the work of supervision across multiple guardrail layers: preventive guardrails (produced by externalizing architectural intent and conventions into machine-interpretable form), executable guardrails (linting, testing, and CI/CD repurposed as scalable supervision infrastructure), and human oversight (shifting from line-by-line inspection toward supervisory interpretation focused on architectural reasoning, explainability, and long-term maintainability). We characterize this as a transition from review- centric guardrails toward layered supervision, in which no single guardrail carries the supervision load alone.
: Generative AI has intensified interest in Automated Software Engineering, but most current evidence still concerns short, local coding tasks rather than the broader software engineering lifecycle. This paper presents a structured experience report on the development of a Python package for interpretable multiclass classification that hosts heterogeneous estimator families behind a shared interface. We analyze how AI assistance contributed to four recurring engineering activities—cross-language transfer, paper-to-code implementation, benchmark-driven algorithm refinement, and packaging/refactoring work—and how a small set of machine-checkable contracts acted as a cross-cutting enabler that made AI-assisted changes safer to accept. The contribution of the paper is threefold: a concrete research-software case from interpretable machine learning, a task-oriented account of where AI created practical leverage, and a set of lessons on planning, architecture, and validation for trustworthy use. The case suggests that generative AI is especially useful when requirements are only partially formalized, yet objective feedback from tests, benchmarks, and model quality metrics is available. At the same time, the study indicates that human oversight remains essential for semantic correctness, experimental validity, and maintainable software architecture. Overall, the results suggest that AI-augmented development is a relevant topic for scientific software engineering.
Robin Nunkesser· Proceedings of the 21st Inte...· 0 citations
AI-generated C++ code has a distinct quality profile, showing higher rates of interface and coupling burdens, copy and allocation overheads, and a reliance on explicit loops over optimized standard APIs, which translates into tangible downstream costs, including increased review effort and a 5-8% increase in compute resource consumption.
Michael Tran, Fred Lewis, Kun Yang et al.· 1 citation
Software testing is moving away from rigid, hand-written scripts toward AI systems that can adapt on their own. This review traces how quality engineering has changed, from rule-based automation to self-adjusting test frameworks, and looks at the technology behind Autonomous Quality Agents: Large Language Models (LLMs) that generate code from requirements, Computer Vision that handles visual regression, and Reinforcement Learning that drives exploratory testing. It also examines two ongoing problems: the difficulty of understanding how AI models make decisions, and the extra work needed to keep older, script-based automation running. The review closes with a proposed framework for where autonomous software assurance is headed next. This proposed framework, termed Autonomous Quality Assurance (AQA), is organised around three layers, perception (visual and DOM-based sensing), cognition (LLM-driven reasoning and test generation), and governance (interpretability and verification), intended to give practitioners and researchers a shared structure for locating where a given tool or technique sits today and what would need to mature before autonomous testing can be trusted at industrial scale.
Vanshita Agarwal· International journal for ad...· 0 citations
A three-level taxonomy inspired by autonomous driving that distinguishes degrees of autonomy along a roadmap from today’s AI-assisted development workflows to fully autonomous software development in which AI systems autonomously identify demands and design, implement, verify, and maintain software without human oversight is introduced.
A codepath-aware governance framework for AI-assisted engineering in regulated codebases, with emphasis on financial services, payments, healthcare, and other domains where software changes may affect legal, operational, privacy, and audit obligations is developed.
Ashutosh Pal· International journal of com...· 0 citations
Software effort estimation remains a cornerstone of project planning and control, yet existing estimation models are grounded in the assumption that software development effort is dominated by human reasoning and manual construction. The rapid integration of large language models (LLMs) into development workflows fundamentally challenges this assumption by automating substantial portions of code synthesis while shifting human effort toward supervision, validation, and integration. As a result, traditional effort estimation proxies such as Story Points and size-based metrics may no longer reliably characterize development effort. This paper presents an empirical study examining how effort manifests in LLM-assisted software development. Rather than using LLMs as predictive estimation tools, we investigate how their adoption reshapes the underlying cost structure of development work. We introduce the notion of Hybrid Intelligence Effort (HIE), conceptualizing effort as the combined burden of model-performed reasoning and human oversight activities. Using a controlled experiment involving 22 developers, 110 real-world tasks, and three LLMs, we compare the explanatory power of traditional estimation metrics against interaction- and oversight-based Hybrid Intelligence dimensions. Our results show that while Story Points retain partial explanatory validity, they fail to capture dominant sources of effort in LLM-assisted workflows. In controlled experiments, HIE dimensions increase explained variance in observed effort from approximately 72–80%, while substantially reducing systematic estimation error. Human validation and corrective intervention emerge as the primary drivers of effort, outweighing artifact-level characteristics. These findings suggest that effort estimation models must move beyond human-centric and size-based assumptions to remain effective in AI-augmented software engineering.
Feisal Alaswad, E. Poovammal, Kadiyala Ramana et al.· Discover Computing· 1 citation· ⚡1
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