arXiv subjects

Markus Wagner

Publications and source records attributed to Markus Wagner.

At least 19 recordsLinked to original sources

arXiv2026

Ensuring reliability in 100% renewable microgrids: a scenario-based joint planning and operational design framework

Off-grid microgrids powered entirely by renewable energy sources face substantial challenges in achieving utility-grade reliability standards. Existing microgrid planning frameworks often prioritize cost minimization while treating reliability as a secondary metric, thereby leading to suboptimal designs. This paper presents a comprehensive scenario-based optimization framework that simultaneously addresses long-term capacity planning and short-term operational dispatch in two stages for 100%-renewable microgrids. The developed two-stage stochastic programming model co-optimizes the investment and operation of photovoltaic generation and battery energy storage, while ensuring compliance with stringent reliability constraints following utility grid standards. Network modeling with operational constraints, such as line capacities and voltage limits, is incorporated to allow distributed resource placement leveraging power sharing between microgrid nodes. A novel scenario generation approach captures critical uncertainties, including seasonal demand fluctuations, solar output variations, and probabilistic equipment failures, through the statistical clustering of historical data. The optimization framework integrates utility-grade reliability constraints limiting the expected energy not served to below 0.002% of the annual demand while minimizing the total system costs. Numerical simulations demonstrate the effectiveness of the proposed framework, achieving 99.998% supply reliability using only photovoltaic power and battery energy storage. The optimized network-aware distributed resource allocation provides inherent resilience through power rerouting during component outages, maintaining load continuity even under simultaneous equipment failures. This study confirms the feasibility of 100%-renewable microgrids to support remote communities while meeting utility-grade reliability benchmarks.

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arXiv2026

Reliability as a Design Principle: A Systematic Review and Integrated Framework for Renewable-Based Microgrids

Reliable operation is a central motivation for deploying renewable-based microgrids. This paper presents a systematic rapid review that positions reliability as the central organizing principle for microgrid design. Specifically, this review systematically synthesizes recent literature to examine how planning assumptions, optimization formulations, operational flexibility mechanisms, and reliability assessment frameworks jointly shape reliability outcomes. The synthesis shows that reliability in renewable-based microgrids is governed primarily by chronological, time-coupled energy adequacy rather than installed capacity alone, with Dunkelflaute events emerging as a key determinant of adequacy failure. Reliability outcomes are shaped by the joint interaction of resource portfolios, storage operating policies, and state trajectories, network features, and protection feasibility under inverter-dominated operation. The review further demonstrates that reliability indices inherited from conventional power systems are poorly suited for renewable-based microgrids, as they compress performance into single dimensions and obscure temporal, spatial, and service-critical risk concentrations. Across optimization practice, reliability is increasingly embedded through multi-objective and constrained formulations; however, persistent gaps remain in representing correlated renewable scarcity, mission-profile-dependent component reliability, and interruption valuation (e.g., value of lost load and customer damage functions) in a consistent and decision-relevant manner. Overall, this review consolidates planning factors, optimization approaches, reliability evaluation methods, and metric suitability into an integrated roadmap for reliability-centered microgrid planning, and outlines future directions toward state-aware, service-oriented planning and assessment frameworks.

math.OC↗

arXiv2026

Dynamic Menu-Based Pricing for Electric Vehicle Charging with Vehicle-to-Grid Integration

The number of electric vehicles is rapidly increasing worldwide. This growth brings significant environmental benefits but also introduces new challenges: uncoordinated charging can place stress on the grid, particularly during peak hours. Beyond these challenges lies the opportunity for electric vehicles to feed energy back to the grid (V2G), which helps balance supply and demand and supports renewable energy. However, current pricing schemes such as time-of-use tariffs provide little incentive for discharging. To study incentive design in a realistic context, we focus on a parking lot operator who manages multiple EV chargers. We propose a menu-based pricing mechanism in which each EV declares its energy requirement and parking duration; given the retail real-time electricity prices, the operator offers a menu of options that trade off the allowed level of discharging and the associated price. We formulate this interaction as a bilevel optimization problem and reformulate it into a single-level model. Results show that, relative to a no-V2G baseline, the proposed mechanism increases operator profit by 30% and reduces EV payments by 17%. Compared to widely used tariff baselines, it improves operator profit by 22-29 percent, lowers EV payments by 9-18 percent, and increases V2G contribution by 87-235 percent. Overall, the results show that the proposed dynamic menu-based pricing framework provides a practical, computationally efficient, and economically advantageous approach for real-time EV charging and V2G integration.

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arXiv2026

ModARO: A Modular Approach to Architecture Reconstruction of Distributed Microservice Codebases

Microservice architectures promote small, independently developed services, but increase overall architectural complexity. It is crucial that developers understand the architecture and how changes to a service affect the overall system, but rapid and independent development of services increases the risk of architectural drift and discourages the creation and maintenance of documentation. Automatic architecture reconstruction can help avoid these issues, but it is difficult to reuse reconstruction code across multiple projects, as all use different combinations of technologies and project-specific conventions. Reconstruction of architecture-level details is further complicated by the tendency to split microservices into separate repositories, preventing a full view of the system from any one codebase. In this paper, we present and evaluate ModARO, an approach to microservice architecture reconstruction that allows writing modular reconstruction code ('extractors') for any technologies and reusing them across different projects, independent of the surrounding technology stack or whether or not the services are split into multiple codebases. We demonstrate the effectiveness of our approach by configuring ModARO to reconstruct 10 open source projects, and we validate the usefulness and usability of ModARO against a state-of-the-art baseline in a user study with 8 industry practitioners. Using this approach, developers can assemble or create extractors tailored to their technology stacks and distribute architecture reconstruction across repositories, enabling integration into repository CI/CD pipelines.

cs.SE↗

arXiv2026

Distributed Architecture Reconstruction of Polyglot and Multi-Repository Microservice Projects

Microservice architectures encourage the use of small, independently developed services; however, this can lead to increased architectural complexity. Accurate documentation is crucial, but is challenging to maintain due to the rapid, independent evolution of services. While static architecture reconstruction provides a way to maintain up-to-date documentation, existing approaches suffer from technology limitations, mono-repo constraints, or high implementation barriers. This paper presents a novel framework for static architecture reconstruction that supports technology-specific analysis modules, called \emph{extractors}, and supports \emph{distributed architecture reconstruction} in multi-repo environments. We describe the core design concepts and algorithms that govern how extractors are executed, how data is passed between them, and how their outputs are unified. Furthermore, the framework is interoperable with existing static analysis tools and algorithms, allowing them to be invoked from or embedded within extractors.

cs.SE↗

arXiv2026

Forecasting Developer Environments with GenAI: A Research Perspective

Generative Artificial Intelligence (GenAI) models are achieving remarkable performance in various tasks, including code generation, testing, code review, and program repair. The ability to increase the level of abstraction away from writing code has the potential to change the Human-AI interaction within the integrated development environment (IDE). To explore the impact of GenAI on IDEs, 33 experts from the Software Engineering, Artificial Intelligence, and Human-Computer Interaction domains gathered to discuss challenges and opportunities at Shonan Meeting 222, a four-day intensive research meeting. Four themes emerged as areas of interest for researchers and practitioners.

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arXiv2025

On the need to perform comprehensive evaluations of automated program repair benchmarks: Sorald case study

In supporting the development of high-quality software, especially necessary in the era of LLMs, automated program repair (APR) tools aim to improve code quality by automatically addressing violations detected by static analysis profilers. Previous research tends to evaluate APR tools only for their ability to clear violations, neglecting their potential introduction of new (sometimes severe) violations, changes to code functionality and degrading of code structure. There is thus a need for research to develop and assess comprehensive evaluation frameworks for APR tools. This study addresses this research gap, and evaluates Sorald (a state-of-the-art APR tool) as a proof of concept. Sorald's effectiveness was evaluated in repairing 3,529 SonarQube violations across 30 rules within 2,393 Java code snippets extracted from Stack Overflow. Outcomes show that while Sorald fixes specific rule violations, it introduced 2,120 new faults (32 bugs, 2088 code smells), reduced code functional correctness--as evidenced by a 24% unit test failure rate--and degraded code structure, demonstrating the utility of our framework. Findings emphasize the need for evaluation methodologies that capture the full spectrum of APR tool effects, including side effects, to ensure their safe and effective adoption.

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arXiv2025

Static Analysis as a Feedback Loop: Enhancing LLM-Generated Code Beyond Correctness

Large language models (LLMs) have demonstrated impressive capabilities in code generation, achieving high scores on benchmarks such as HumanEval and MBPP. However, these benchmarks primarily assess functional correctness and neglect broader dimensions of code quality, including security, reliability, readability, and maintainability. In this work, we systematically evaluate the ability of LLMs to generate high-quality code across multiple dimensions using the PythonSecurityEval benchmark. We introduce an iterative static analysis-driven prompting algorithm that leverages Bandit and Pylint to identify and resolve code quality issues. Our experiments with GPT-4o show substantial improvements: security issues reduced from >40% to 13%, readability violations from >80% to 11%, and reliability warnings from >50% to 11% within ten iterations. These results demonstrate that LLMs, when guided by static analysis feedback, can significantly enhance code quality beyond functional correctness.

cs.SE↗

arXiv2025

Resilient Auto-Scaling of Microservice Architectures with Efficient Resource Management

Horizontal Pod Auto-scalers (HPAs) are crucial for managing resource allocation in microservice architectures to handle fluctuating workloads. However, traditional HPAs fail to address resource disruptions caused by faults, cyberattacks, maintenance, and other operational challenges. These disruptions result in resource wastage, service unavailability, and HPA performance degradation. To address these challenges, we extend our prior work on Smart HPA and propose SecureSmart HPA, which offers resilient and resource-efficient auto-scaling for microservice architectures. SecureSmart HPA monitors microservice resource demands, detects disruptions, evaluates resource wastage, and dynamically adjusts scaling decisions to enhance the resilience of auto-scaling operations. Furthermore, SecureSmart HPA enables resource sharing among microservices, optimizing scaling efficiency in resource-constrained environments. Experimental evaluation at varying disruption severities, with 25%, 50%, and 75% resource wastage, demonstrates that SecureSmart HPA performs effectively across different levels of disruptions. It achieves up to a 57.2% reduction in CPU overutilization and a 51.1% increase in resource allocation compared to Smart HPA, highlighting its ability to deliver resilient and efficient auto-scaling operations in volatile and resource-constrained environments.

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arXiv2025

Information-Theoretic Detection of Unusual Source Code Changes

The code base of software projects evolves essentially through inserting and removing information to and from the source code. We can measure this evolution via the elements of information - tokens, words, nodes - of the respective representation of the code. In this work, we approach the measurement of the information content of the source code of open-source projects from an information-theoretic standpoint. Our focus is on the entropy of two fundamental representations of code: tokens and abstract syntax tree nodes, from which we derive definitions of textual and structural entropy. We proceed with an empirical assessment where we evaluate the evolution patterns of the entropy of 95 actively maintained open source projects. We calculate the statistical relationships between our derived entropy metrics and classic methods of measuring code complexity and learn that entropy may capture different dimensions of complexity than classic metrics. Finally, we conduct entropy-based anomaly detection of unusual changes to demonstrate that our approach may effectively recognise unusual source code change events with over 60% precision, and lay the groundwork for improvements to information-theoretic measurement of source code evolution, thus paving the way for a new approach to statically gauging program complexity throughout its development.

cs.SE↗

arXiv2024

TC-PDM: Temporally Consistent Patch Diffusion Models for Infrared-to-Visible Video Translation

Infrared imaging offers resilience against changing lighting conditions by capturing object temperatures. Yet, in few scenarios, its lack of visual details compared to daytime visible images, poses a significant challenge for human and machine interpretation. This paper proposes a novel diffusion method, dubbed Temporally Consistent Patch Diffusion Models (TC-DPM), for infrared-to-visible video translation. Our method, extending the Patch Diffusion Model, consists of two key components. Firstly, we propose a semantic-guided denoising, leveraging the strong representations of foundational models. As such, our method faithfully preserves the semantic structure of generated visible images. Secondly, we propose a novel temporal blending module to guide the denoising trajectory, ensuring the temporal consistency between consecutive frames. Experiment shows that TC-PDM outperforms state-of-the-art methods by 35.3% in FVD for infrared-to-visible video translation and by 6.1% in AP50 for day-to-night object detection. Our code is publicly available at https://github.com/dzungdoan6/tc-pdm

cs.CV↗

arXiv2024

Weakly Supervised Test-Time Domain Adaptation for Object Detection

Prior to deployment, an object detector is trained on a dataset compiled from a previous data collection campaign. However, the environment in which the object detector is deployed will invariably evolve, particularly in outdoor settings where changes in lighting, weather and seasons will significantly affect the appearance of the scene and target objects. It is almost impossible for all potential scenarios that the object detector may come across to be present in a finite training dataset. This necessitates continuous updates to the object detector to maintain satisfactory performance. Test-time domain adaptation techniques enable machine learning models to self-adapt based on the distributions of the testing data. However, existing methods mainly focus on fully automated adaptation, which makes sense for applications such as self-driving cars. Despite the prevalence of fully automated approaches, in some applications such as surveillance, there is usually a human operator overseeing the system's operation. We propose to involve the operator in test-time domain adaptation to raise the performance of object detection beyond what is achievable by fully automated adaptation. To reduce manual effort, the proposed method only requires the operator to provide weak labels, which are then used to guide the adaptation process. Furthermore, the proposed method can be performed in a streaming setting, where each online sample is observed only once. We show that the proposed method outperforms existing works, demonstrating a great benefit of human-in-the-loop test-time domain adaptation. Our code is publicly available at https://github.com/dzungdoan6/WSTTA

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arXiv2024

Detecting Fallacies in Climate Misinformation: A Technocognitive Approach to Identifying Misleading Argumentation

Misinformation about climate change is a complex societal issue requiring holistic, interdisciplinary solutions at the intersection between technology and psychology. One proposed solution is a "technocognitive" approach, involving the synthesis of psychological and computer science research. Psychological research has identified that interventions in response to misinformation require both fact-based (e.g., factual explanations) and technique-based (e.g., explanations of misleading techniques) content. However, little progress has been made on documenting and detecting fallacies in climate misinformation. In this study, we apply a previously developed critical thinking methodology for deconstructing climate misinformation, in order to develop a dataset mapping different types of climate misinformation to reasoning fallacies. This dataset is used to train a model to detect fallacies in climate misinformation. Our study shows F1 scores that are 2.5 to 3.5 better than previous works. The fallacies that are easiest to detect include fake experts and anecdotal arguments, while fallacies that require background knowledge, such as oversimplification, misrepresentation, and slothful induction, are relatively more difficult to detect. This research lays the groundwork for development of solutions where automatically detected climate misinformation can be countered with generative technique-based corrections.

cs.CL↗

arXiv2024

Creative and Correct: Requesting Diverse Code Solutions from AI Foundation Models

AI foundation models have the capability to produce a wide array of responses to a single prompt, a feature that is highly beneficial in software engineering to generate diverse code solutions. However, this advantage introduces a significant trade-off between diversity and correctness. In software engineering tasks, diversity is key to exploring design spaces and fostering creativity, but the practical value of these solutions is heavily dependent on their correctness. Our study systematically investigates this trade-off using experiments with HumanEval tasks, exploring various parameter settings and prompting strategies. We assess the diversity of code solutions using similarity metrics from the code clone community. The study identifies combinations of parameters and strategies that strike an optimal balance between diversity and correctness, situated on the Pareto front of this trade-off space. These findings offer valuable insights for software engineers on how to effectively use AI foundation models to generate code solutions that are diverse and accurate.

cs.SE↗

arXiv2024

Smart HPA: A Resource-Efficient Horizontal Pod Auto-scaler for Microservice Architectures

Microservice architectures have gained prominence in both academia and industry, offering enhanced agility, reusability, and scalability. To simplify scaling operations in microservice architectures, container orchestration platforms such as Kubernetes feature Horizontal Pod Auto-scalers (HPAs) designed to adjust the resources of microservices to accommodate fluctuating workloads. However, existing HPAs are not suitable for resource-constrained environments, as they make scaling decisions based on the individual resource capacities of microservices, leading to service unavailability and performance degradation. Furthermore, HPA architectures exhibit several issues, including inefficient data processing and a lack of coordinated scaling operations. To address these concerns, we propose Smart HPA, a flexible resource-efficient horizontal pod auto-scaler. It features a hierarchical architecture that integrates both centralized and decentralized architectural styles to leverage their respective strengths while addressing their limitations. We introduce resource-efficient heuristics that empower Smart HPA to exchange resources among microservices, facilitating effective auto-scaling of microservices in resource-constrained environments. Our experimental results show that Smart HPA outperforms the Kubernetes baseline HPA by reducing resource overutilization, overprovisioning, and underprovisioning while increasing resource allocation to microservice applications.

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arXiv2023

Community Battery Energy Storage Systems for Enhancing Distribution System Operation: A Multi-objective Optimization Approach

The growing penetration of distributed energy resources (DERs) in distribution networks (DNs) raises new operational challenges, particularly in terms of reliability and voltage regulation. In response to these challenges, we introduce an innovative DN operation framework with multi-objective optimization, leveraging community battery energy storage systems (C-BESS). The proposed framework targets two key operational objectives: first, to minimize voltage deviation, which is a concern for a distribution network service provider (DNSP), and second, to maximize the utilization of DERs on the demand side. Recognizing the conflicting nature of these objectives, we utilize C-BESS to enhance the system's adaptability to dynamically adjust DN operations. The multi-objective optimization problem is solved using the non-dominated sorting genetic algorithm-II (NSGA-II). Case studies using real-world data are conducted to validate the effectiveness of the proposed framework. The results show significant improvements in voltage regulation and DER utilization, demonstrating the potential of C-BESS in enabling more reliable DN operation. Our findings contribute to the ongoing discourse on the role of C-BESS in DN operation enhancement and DER integration.

cs.NI↗

arXiv2023

Using the TypeScript compiler to fix erroneous Node.js snippets

Most online code snippets do not run. This means that developers looking to reuse code from online sources must manually find and fix errors. We present an approach for automatically evaluating and correcting errors in Node.js code snippets: Node Code Correction (NCC). NCC leverages the ability of the TypeScript compiler to generate errors and inform code corrections through the combination of TypeScript's built-in codefixes, our own targeted fixes, and deletion of erroneous lines. Compared to existing approaches using linters, our findings suggest that NCC is capable of detecting a larger number of errors per snippet and more error types, and it is more efficient at fixing snippets. We find that 73.7% of the code snippets in NPM documentation have errors; with the use of NCC's corrections, this number was reduced to 25.1%. Our evaluation confirms that the use of the TypeScript compiler to inform code corrections is a promising strategy to aid in the reuse of code snippets from online sources.

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arXiv2023

Wait, wasn't that code here before? Detecting Outdated Software Documentation

Encountering outdated documentation is not a rare occurrence for developers and users in the software engineering community. To ensure that software documentation is up-to-date, developers often have to manually check whether the documentation needs to be updated whenever changes are made to the source code. In our previous work, we proposed an approach to automatically detect outdated code element references in software repositories and found that more than a quarter of the 1000 most popular projects on GitHub contained at least one outdated reference. In this paper, we present a GitHub Actions tool that builds on our previous work's approach that GitHub developers can configure to automatically scan for outdated code element references in their GitHub project's documentation whenever a pull request is submitted.

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