Reducing Alert Fatigue via AI-Assisted Negotiation: A Case for Dependabot

• URL: http://arxiv.org/abs/2502.06175v1
• Date: Mon, 10 Feb 2025 05:58:21 GMT
• Title: Reducing Alert Fatigue via AI-Assisted Negotiation: A Case for Dependabot
• Abstract summary: This paper presents a position on using Artificial Intelligence (AI) agents as dependency negotiators.<n>We examine specific use cases where AI agents can facilitate dependency negotiations.<n>Our goal is to reduce alert fatigue to an extent that maintainers no longer feel overwhelmed and welcome pull requests.
• Score: 2.9497620776025046
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing complexity of software dependencies has led to the emergence of automated dependency management tools, such as Dependabot. However, these tools often overwhelm developers with a high volume of alerts and notifications, leading to alert fatigue. This paper presents a position on using Artificial Intelligence (AI) agents as dependency negotiators to reduce alert fatigue. We then examine specific use cases where AI agents can facilitate dependency negotiations, such as when working with external dependencies or managing complex, multi-component systems. Our findings highlight the need for more research on the design and evaluation of AI-driven dependency mediation mechanisms. With a focus on ensuring transparency, explainability, and human trustworthiness in these GitHub software projects, our goal is to reduce alert fatigue to an extent that maintainers no longer feel overwhelmed and welcome pull requests just like any other contribution into their projects.

Related papers

• Offloading Score: Measuring AI Reliance Through Counterfactual Workflows [70.84727355516559]
  We introduce offloading score, a measure of reliance that quantifies the fraction of cognitive effort offloaded to an AI tool.<n>We validate offloading score through intrinsic evaluations of metric validity, and a controlled user study.<n>We show that offloading score detects significantly higher reliance in time-constrained settings.
  arXiv  Detail & Related papers  (2026-05-28T05:44:31Z)
• Insights into Security-Related AI-Generated Pull Requests [0.0]
  We analyze more than 33,000 AI-generated pull requests (PRs) and identify 675 security-related submissions made by agentic AIs.<n>The results show that security-related AI PRs introduce a small set of recurring weaknesses.<n>The commit message quality of AI PRs has a limited effect on acceptance or latency, in contrast to human PRs reported in previous studies.
  arXiv  Detail & Related papers  (2026-04-21T20:20:20Z)
• When Should a Robot Think? Resource-Aware Reasoning via Reinforcement Learning for Embodied Robotic Decision-Making [68.12864562049957]
  Embodied robotic systems increasingly rely on large language model (LLM)-based agents to support high-level reasoning.<n>This raises a fundamental question for embodied agents: when should the agent reason, and when should it act?<n>We propose RARRL (Resource-Aware Reasoning via Reinforcement Learning), a hierarchical framework for resource-aware orchestration of embodied agents.
  arXiv  Detail & Related papers  (2026-03-17T15:38:50Z)
• AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development [12.50615284537175]
  Large language model (LLM) based coding agents increasingly act as autonomous contributors that generate and merge pull requests.<n>We present a longitudinal causal study of agent adoption in open-source repositories using staggered difference-in-differences with matched controls.
  arXiv  Detail & Related papers  (2026-01-20T04:51:56Z)
• Security in the Age of AI Teammates: An Empirical Study of Agentic Pull Requests on GitHub [4.409447722044799]
  This study aims to characterize how autonomous coding agents contribute to software security in practice.<n>We conduct a large-scale empirical analysis of agent-authored PRs using the AIDev dataset.<n>We then analyze prevalence, acceptance outcomes, and review latency across autonomous agents, programming ecosystems, and types of code changes.
  arXiv  Detail & Related papers  (2026-01-01T21:14:11Z)
• Hybrid Stackelberg Game and Diffusion-based Auction for Two-tier Agentic AI Task Offloading in Internet of Agents [70.6356929970175]
  The Internet of Agents (IoA) is a foundational architecture for interconnected intelligent systems.<n>IoA involves physical entities, i.e., Wireless Agents (WAs) with limited onboard resources, which need to offload their compute-intensive agentic AI services to nearby servers.<n>We propose a two-tier optimization approach to facilitate task offloading.
  arXiv  Detail & Related papers  (2025-11-27T03:55:18Z)
• Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning [84.70211451226835]
  Large Language Model (LLM) Agents are constrained by a dependency on human-curated data.<n>We introduce Agent0, a fully autonomous framework that evolves high-performing agents without external data.<n>Agent0 substantially boosts reasoning capabilities, improving the Qwen3-8B-Base model by 18% on mathematical reasoning and 24% on general reasoning benchmarks.
  arXiv  Detail & Related papers  (2025-11-20T05:01:57Z)
• Impatient Users Confuse AI Agents: High-fidelity Simulations of Human Traits for Testing Agents [58.00130492861884]
  TraitBasis is a lightweight, model-agnostic method for systematically stress testing AI agents.<n>TraitBasis learns directions in activation space corresponding to steerable user traits.<n>We observe on average a 2%-30% performance degradation on $tau$-Trait across frontier models.
  arXiv  Detail & Related papers  (2025-10-06T05:03:57Z)
• Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows [66.1850490474361]
  We conduct the first academic study to explore developer interactions with coding agents.<n>We evaluate two leading copilot and agentic coding assistants, GitHub Copilot and OpenHands.<n>Our results show agents have the potential to assist developers in ways that surpass copilots.
  arXiv  Detail & Related papers  (2025-07-10T20:12:54Z)
• Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities [117.49715661395294]
  Data structurization can play a promising role by transforming intricate and disorganized data into well-structured forms.<n>This survey presents a first systematic review of how graphs can empower AI agents.
  arXiv  Detail & Related papers  (2025-06-22T12:59:12Z)
• Co-Saving: Resource Aware Multi-Agent Collaboration for Software Development [65.94639060883475]
  We propose a resource-aware multi-agent system -- Co-Saving.<n>Our key innovation is the introduction of "shortcuts"<n>Compared to the state-of-the-art MAS ChatDev, our method achieves an average reduction of 50.85% in token usage.
  arXiv  Detail & Related papers  (2025-05-28T02:23:53Z)
• Explainability for Embedding AI: Aspirations and Actuality [1.8130068086063336]
  Explainable AI (XAI) may allow developers to understand better the systems they build. Existing XAI systems still fall short of this aspiration. We see an unmet need to provide developers with adequate support mechanisms to cope with this complexity.
  arXiv  Detail & Related papers  (2025-04-20T14:20:01Z)
• AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents [15.802600809497097]
  This paper introduces AI2Agent, an end-to-end framework that automates AI project deployment through guideline-driven execution. We conducted experiments on 30 AI deployment cases, covering TTS, text-to-image generation, image editing, and other AI applications. Results show that AI2Agent significantly reduces deployment time and improves success rates.
  arXiv  Detail & Related papers  (2025-03-31T10:58:34Z)
• Interacting with AI Reasoning Models: Harnessing "Thoughts" for AI-Driven Software Engineering [11.149764135999437]
  Recent advances in AI reasoning models provide unprecedented transparency into their decision-making processes. Software engineers rarely have the time or cognitive bandwidth to analyze, verify, and interpret every AI-generated thought in detail. We propose a vision for structuring the interaction between AI reasoning models and software engineers to maximize trust, efficiency, and decision-making power.
  arXiv  Detail & Related papers  (2025-03-01T13:19:15Z)
• Integration of Agentic AI with 6G Networks for Mission-Critical Applications: Use-case and Challenges [12.015880968827384]
  Agentic AI (AAI) has gained a lot of attention recently due to its ability to analyze textual data through a contextual lens. We propose a novel framework with a multi-layer architecture to realize the AAI. Preliminary analysis shows that the AAI reduces initial response time by 5.6 minutes on average.
  arXiv  Detail & Related papers  (2025-02-19T07:00:53Z)
• Self-Regulation and Requesting Interventions [63.5863047447313]
  We propose an offline framework that trains a "helper" policy to request interventions.<n>We score optimal intervention timing with PRMs and train the helper model on these labeled trajectories.<n>This offline approach significantly reduces costly intervention calls during training.
  arXiv  Detail & Related papers  (2025-02-07T00:06:17Z)
• Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization [56.674356045200696]
  We propose a novel method to train AI agents to incorporate knowledge and skills for multiple tasks without the need for cumbersome note systems or prior high-quality demonstration data.<n>Our approach employs an iterative process where the agent collects new experiences, receives corrective feedback from humans in the form of hints, and integrates this feedback into its weights.<n>We demonstrate the efficacy of our approach by implementing it in a Llama-3-based agent which, after only a few rounds of feedback, outperforms advanced models GPT-4o and DeepSeek-V3 in a taskset.
  arXiv  Detail & Related papers  (2025-02-03T17:45:46Z)
• AIOpsLab: A Holistic Framework to Evaluate AI Agents for Enabling Autonomous Clouds [12.464941027105306]
  AI for IT Operations (AIOps) aims to automate complex operational tasks, such as fault localization and root cause analysis, to reduce human workload and minimize customer impact.<n>Recent advances in Large Language Models (LLMs) and AI agents are revolutionizing AIOps by enabling end-to-end and multitask automation.<n>We present AIOPSLAB, a framework that deploys microservice cloud environments, injects faults, generates workloads, and exports telemetry data but also orchestrates these components and provides interfaces for interacting with and evaluating agents.
  arXiv  Detail & Related papers  (2025-01-12T04:17:39Z)
• Asynchronous Tool Usage for Real-Time Agents [61.3041983544042]
  We introduce asynchronous AI agents capable of parallel processing and real-time tool-use. Our key contribution is an event-driven finite-state machine architecture for agent execution and prompting. This work presents both a conceptual framework and practical tools for creating AI agents capable of fluid, multitasking interactions.
  arXiv  Detail & Related papers  (2024-10-28T23:57:19Z)
• Raising the Stakes: Performance Pressure Improves AI-Assisted Decision Making [57.53469908423318]
  We show the effects of performance pressure on AI advice reliance when laypeople complete a common AI-assisted task. We find that when the stakes are high, people use AI advice more appropriately than when stakes are lower, regardless of the presence of an AI explanation.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• Agent-as-a-Judge: Evaluate Agents with Agents [61.33974108405561]
  We introduce the Agent-as-a-Judge framework, wherein agentic systems are used to evaluate agentic systems. This is an organic extension of the LLM-as-a-Judge framework, incorporating agentic features that enable intermediate feedback for the entire task-solving process. We present DevAI, a new benchmark of 55 realistic automated AI development tasks.
  arXiv  Detail & Related papers  (2024-10-14T17:57:02Z)
• Building AI Agents for Autonomous Clouds: Challenges and Design Principles [17.03870042416836]
  AI for IT Operations (AIOps) aims to automate complex operational tasks, like fault localization and root cause analysis, thereby reducing human intervention and customer impact. This vision paper lays the groundwork for such a framework by first framing the requirements and then discussing design decisions. We propose AIOpsLab, a prototype implementation leveraging agent-cloud-interface that orchestrates an application, injects real-time faults using chaos engineering, and interfaces with an agent to localize and resolve the faults.
  arXiv  Detail & Related papers  (2024-07-16T20:40:43Z)
• A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges [23.467373994306524]
  In practice, developers do not accept AI programming assistants' initial suggestions at a high frequency. To understand developers' practices while using these tools, we administered a survey to a large population of developers. We found that developers are most motivated to use AI programming assistants because they help developers reduce key-strokes, finish programming tasks quickly, and recall syntax. We also found the most important reasons why developers do not use these tools are because these tools do not output code that addresses certain functional or non-functional requirements.
  arXiv  Detail & Related papers  (2023-03-30T03:21:53Z)
• AI Maintenance: A Robustness Perspective [91.28724422822003]
  We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance. We propose an AI model inspection framework to detect and mitigate robustness risks. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
  arXiv  Detail & Related papers  (2023-01-08T15:02:38Z)
• When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning [57.53138994155612]
  A long-term goal of reinforcement learning is to design agents that can autonomously interact and learn in the world. A critical challenge is the presence of irreversible states which require external assistance to recover from, such as when a robot arm has pushed an object off of a table. We propose an algorithm that efficiently learns to detect and avoid states that are irreversible, and proactively asks for help in case the agent does enter them.
  arXiv  Detail & Related papers  (2022-10-19T17:57:24Z)

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.