Tootfinder

Architectural Degradation: Definition, Motivations, Measurement and Remediation Approaches
Noman Ahmad, Ruoyu Su, Matteo Esposito, Andrea Janes, Valentina Lenarduzzi, Davide Taibi
https://arxiv.org/abs/2507.14547

Architectural Degradation: Definition, Motivations, Measurement and Remediation Approaches
Architectural degradation, also known as erosion, decay, or aging, impacts system quality, maintainability, and adaptability. Although widely acknowledged, current literature shows fragmented definitions, metrics, and remediation strategies. Our study aims to unify understanding of architectural degradation by identifying its definitions, causes, metrics, tools, and remediation approaches across academic and gray literature. We conducted a multivocal literature review of 108 studies extracting …

Tel Aviv-based Seemplicity, whose AI-based tech automates enterprise vulnerability and exposure management, raised a $50M Series B led by Sienna Venture Capital (Rick Whiting/CRN)
https://www.crn.com/news/software/2025/remediation-o…

MalCodeAI: Autonomous Vulnerability Detection and Remediation via Language Agnostic Code Reasoning
Jugal Gajjar, Kamalasankari Subramaniakuppusamy, Noha El Kachach
https://arxiv.org/abs/2507.10898

MalCodeAI: Autonomous Vulnerability Detection and Remediation via Language Agnostic Code Reasoning
The growing complexity of cyber threats and the limitations of traditional vulnerability detection tools necessitate novel approaches for securing software systems. We introduce MalCodeAI, a language-agnostic, multi-stage AI pipeline for autonomous code security analysis and remediation. MalCodeAI combines code decomposition and semantic reasoning using fine-tuned Qwen2.5-Coder-3B-Instruct models, optimized through Low-Rank Adaptation (LoRA) within the MLX framework, and delivers scalable, accu…

Good Morning #Canada
Collingwood Ontario today is known as a resort town, supporting skiers at Blue Mountains and swimmers at nearby Wasaga Beach. But the town was built on their reputation for ship building for over 100 years. The Collingwood Shipyard, established in 1882, was a major shipbuilding center, building over 200 ships including Great Lakes freighters, naval vessels, and the HMCS Hochelaga. During #WWII the shipyard launched ships for the Canadian Navy. The shipyard was famous for its unique side-launching technique, necessary due to harbour constraints, which became a significant local spectacle. It closed in 1986, and after significant remediation efforts, the site is now a waterfront park with mixed housing and a marina.
#CanadaIsAwesome #History
https://definingmomentscanada.ca/all-for-9/historical-articles/whats-a-side-launch/

GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?
Yifang Tian, Yaming Liu, Zichun Chong, Zihang Huang, Hans-Arno Jacobsen
https://arxiv.org/abs/2508.12472

GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?
Root cause analysis (RCA) in microservice systems is challenging, requiring on-call engineers to rapidly diagnose failures across heterogeneous telemetry such as metrics, logs, and traces. Traditional RCA methods often focus on single modalities or merely rank suspect services, falling short of providing actionable diagnostic insights with remediation guidance. This paper introduces GALA, a novel multi-modal framework that combines statistical causal inference with LLM-driven iterative reasonin…

Coupled Interfacial Phenomena Suppress Propulsion in Catalytic Janus Colloids
Muhammad Haroon, Christopher Wirth
https://arxiv.org/abs/2509.14471 https://a…

Coupled Interfacial Phenomena Suppress Propulsion in Catalytic Janus Colloids
Platinum-coated polystyrene Janus particles exhibit a combination of stochastic and deterministic motion in hydrogen peroxide solutions, making them promising candidates for applications in micro-scale cargo transport, drug delivery, and environmental remediation. The dynamics of Janus particles very near a boundary are dictated by conservative and non-conservative interactions that depend on particle, substrate, and solution properties. This study investigated the influence of orientational qu…

Conflicting Scores, Confusing Signals: An Empirical Study of Vulnerability Scoring Systems
Viktoria Koscinski, Mark Nelson, Ahmet Okutan, Robert Falso, Mehdi Mirakhorli
https://arxiv.org/abs/2508.13644

Conflicting Scores, Confusing Signals: An Empirical Study of Vulnerability Scoring Systems
Accurately assessing software vulnerabilities is essential for effective prioritization and remediation. While various scoring systems exist to support this task, their differing goals, methodologies and outputs often lead to inconsistent prioritization decisions. This work provides the first large-scale, outcome-linked empirical comparison of four publicly available vulnerability scoring systems: the Common Vulnerability Scoring System (CVSS), the Stakeholder-Specific Vulnerability Categorizat…

Findings of the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered Tutors
Ekaterina Kochmar, Kaushal Kumar Maurya, Kseniia Petukhova, KV Aditya Srivatsa, Ana\"is Tack, Justin Vasselli
https://arxiv.org/abs/2507.10579

Findings of the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered Tutors
This shared task has aimed to assess pedagogical abilities of AI tutors powered by large language models (LLMs), focusing on evaluating the quality of tutor responses aimed at student's mistake remediation within educational dialogues. The task consisted of five tracks designed to automatically evaluate the AI tutor's performance across key dimensions of mistake identification, precise location of the mistake, providing guidance, and feedback actionability, grounded in learning science principl…

Understanding Spatial Regression Models from a Weighting Perspective in an Observational Study of Superfund Remediation
Sophie M. Woodward, Francesca Dominici, Jose R. Zubizarreta
https://arxiv.org/abs/2508.19572

Understanding Spatial Regression Models from a Weighting Perspective in an Observational Study of Superfund Remediation
Superfund sites are locations in the United States with high levels of environmental toxicants, often resulting from industrial activity or improper waste management. Given mounting evidence linking prenatal environmental exposures to adverse birth outcomes, estimating the impact of Superfund remediation is of substantial policy relevance. A widespread approach is to fit a spatial regression, i.e., a linear regression of the outcome (e.g., birth weight) on binary treatment (e.g., indicator for …

Time is running out for wild salmon in the Columbia River Basin in the Pacific Northwest.
Their populations, as well as those of some other native fish, have been declining for decades.
Now, Donald Trump is attacking the progress that had been made to restore those once-abundant salmon runs.
In June 2025, Trump signed a memorandum signaling his administration’s unwillingness to abide by the Resilient Columbia Basin Agreement signed in December 2023 after two years of neg…

Tribes Condemn Trump for Backing Out of Columbia River Deal for Salmon Recovery
Tribal leaders and environmental advocates warn salmon populations will go extinct without remediation efforts.

ACE: Automated Technical Debt Remediation with Validated Large Language Model Refactorings
Adam Tornhill, Markus Borg, Nadim Hagatulah, Emma S\"oderberg
https://arxiv.org/abs/2507.03536

ACE: Automated Technical Debt Remediation with Validated Large Language Model Refactorings
The remarkable advances in AI and Large Language Models (LLMs) have enabled machines to write code, accelerating the growth of software systems. However, the bottleneck in software development is not writing code but understanding it; program understanding is the dominant activity, consuming approximately 70% of developers' time. This implies that improving existing code to make it easier to understand has a high payoff and - in the age of AI-assisted coding - is an essential activity to ensure…

VerilogLAVD: LLM-Aided Rule Generation for Vulnerability Detection in Verilog
Xiang Long, Yingjie Xia, Xiyuan Chen, Li Kuang
https://arxiv.org/abs/2508.13092 https://

VerilogLAVD: LLM-Aided Rule Generation for Vulnerability Detection in Verilog
Timely detection of hardware vulnerabilities during the early design stage is critical for reducing remediation costs. Existing early detection techniques often require specialized security expertise, limiting their usability. Recent efforts have explored the use of large language models (LLMs) for Verilog vulnerability detection. However, LLMs struggle to capture the structure in Verilog code, resulting in inconsistent detection results. To this end, we propose VerilogLAVD, the first LLM-aided…

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML
Hendrik Reiter, Ahmad Rzgar Hamid, Florian Schl\"osser, Mikkel Baun Kj{\ae}rgaard, Wilhelm Hasselbring
https://arxiv.org/abs/2507.22702

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML
Edge computing offers significant advantages for realtime data processing tasks, such as object recognition, by reducing network latency and bandwidth usage. However, edge environments are susceptible to various types of fault. A remediator is an automated software component designed to adjust the configuration parameters of a software service dynamically. Its primary function is to maintain the services operational state within predefined Service Level Objectives by applying corrective actions…

An End-to-End Assurance Framework for AI/ML Workloads in Datacenters
Jit Gupta, Tarun Banka, Rahul Gupta, Mithun Dharmaraj, Jasleen Kaur
https://arxiv.org/abs/2507.03158

An End-to-End Assurance Framework for AI/ML Workloads in Datacenters
Modern machine learning workloads such as large language model training, fine-tuning jobs are highly distributed and span across hundreds of systems with multiple GPUs. Job completion time for these workloads is the artifact of the application, compute, network and storage performance. In case of failure or degraded performance it is imperative to understand the root cause and possible remediation for the problem for end-to-end assurance. This demo showcases SaaSbased observability and automate…

BitsAI-Fix: LLM-Driven Approach for Automated Lint Error Resolution in Practice
Yuanpeng Li, Qi Long, Zhiyuan Yao, Jian Xu, Lintao Xie, Xu He, Lu Geng, Xin Han, Yueyan Chen, Wenbo Duan
https://arxiv.org/abs/2508.03487

BitsAI-Fix: LLM-Driven Approach for Automated Lint Error Resolution in Practice
As enterprise codebases continue to grow in scale and complexity, the volume of lint errors far exceeds engineers' manual remediation capacity, leading to continuous accumulation of technical debt and hindered development efficiency. This paper presents BitsAI-Fix, an automated lint error remediation workflow based on Large Language Models (LLMs), designed to address this critical challenge in industrial-scale environments. BitsAI-Fix employs tree-sitter for context expansion and generates sear…

Towards Underwater Detection of Diluted Bitumen with Interdigital Sensors
Graziella Bedenik, Melissa Greeff, Matthew Robertson
https://arxiv.org/abs/2507.03781

Towards Underwater Detection of Diluted Bitumen with Interdigital Sensors
Diluted bitumen spills in freshwater environments pose significant challenges for detection and remediation due to the complex behaviour of the material once released, rendering traditional oil monitoring techniques, such as aerial surveys and fluorometry, ineffective. This study investigates the feasibility of utilizing interdigital capacitive sensors to detect dilbit in water via frequency-dependent impedance measurements. Three sensor designs were fabricated and tested with a vector network …

When AIOps Become "AI Oops": Subverting LLM-driven IT Operations via Telemetry Manipulation
Dario Pasquini, Evgenios M. Kornaropoulos, Giuseppe Ateniese, Omer Akgul, Athanasios Theocharis, Petros Efstathopoulos
https://arxiv.org/abs/2508.06394

When AIOps Become "AI Oops": Subverting LLM-driven IT Operations via Telemetry Manipulation
AI for IT Operations (AIOps) is transforming how organizations manage complex software systems by automating anomaly detection, incident diagnosis, and remediation. Modern AIOps solutions increasingly rely on autonomous LLM-based agents to interpret telemetry data and take corrective actions with minimal human intervention, promising faster response times and operational cost savings. In this work, we perform the first security analysis of AIOps solutions, showing that, once again, AI-driven …

Measuring the Vulnerability Disclosure Policies of AI Vendors
Yangheran Piao (University of Edinburgh), Jingjie Li (University of Edinburgh), Daniel W. Woods (University of Edinburgh)
https://arxiv.org/abs/2509.06136

Measuring the Vulnerability Disclosure Policies of AI Vendors
As AI is increasingly integrated into products and critical systems, researchers are paying greater attention to identifying related vulnerabilities. Effective remediation depends on whether vendors are willing to accept and respond to AI vulnerability reports. In this paper, we examine the disclosure policies of 264 AI vendors. Using a mixed-methods approach, our quantitative analysis finds that 36% of vendors provide no disclosure channel, and only 18% explicitly mention AI-related risks. Vul…

Autonomic Microservice Management via Agentic AI and MAPE-K Integration
Matteo Esposito, Alexander Bakhtin, Noman Ahmad, Mikel Robredo, Ruoyu Su, Valentina Lenarduzzi, Davide Taibi
https://arxiv.org/abs/2506.22185

Autonomic Microservice Management via Agentic AI and MAPE-K Integration
While microservices are revolutionizing cloud computing by offering unparalleled scalability and independent deployment, their decentralized nature poses significant security and management challenges that can threaten system stability. We propose a framework based on MAPE-K, which leverages agentic AI, for autonomous anomaly detection and remediation to address the daunting task of highly distributed system management. Our framework offers practical, industry-ready solutions for maintaining ro…