arXiv subjects

Ermeson Andrade

Publications and source records attributed to Ermeson Andrade.

5 recordsLinked to original sources

arXiv2026

Software Aging in LLM-Generated Applications: Runtime Evidence, Static Analysis, and Human-Written Comparisons

Large Language Models (LLMs) are increasingly used to generate executable software systems from natural language specifications, accelerating development and reducing manual implementation effort. Although recent studies have investigated the functional correctness, security, maintainability, and robustness of LLM-generated code, little is known about the long-term reliability of such systems under sustained execution. In this paper, we experimentally investigate software aging symptoms in LLM-generated service-based applications across generation-and-execution environments. Using backend scenarios derived from BaxBench, we generated applications targeting JavaScript, Python, and Rust through LLM-based generation platforms, validated them with BaxBench-derived tests, and subjected them to 48-hour workload executions. We monitored memory usage, response time, and throughput and analyzed them using the Mann--Kendall test and Sen's slope estimator. We further complemented the runtime evaluation with static analysis of the generated source code and an exploratory comparison with human-written implementations of related backend scenarios. The results show that memory usage is the most consistent indicator of potential software aging, with statistically significant upward trends in most application-environment combinations, while response time and throughput exhibit more heterogeneous behavior. Static analysis identified plausible code-level aging mechanisms, and the comparison with human-written systems showed that the aging symptoms observed in LLM-generated applications align with degradation patterns also found in manually developed implementations. These findings indicate that functional correctness alone is insufficient to assess the operational reliability of LLM-generated software before deployment in continuously running environments.

cs.SE↗

arXiv2025

Adaptive Detection of Software Aging under Workload Shift

Software aging is a phenomenon that affects long-running systems, leading to progressive performance degradation and increasing the risk of failures. To mitigate this problem, this work proposes an adaptive approach based on machine learning for software aging detection in environments subject to dynamic workload conditions. We evaluate and compare a static model with adaptive models that incorporate adaptive detectors, specifically the Drift Detection Method (DDM) and Adaptive Windowing (ADWIN), originally developed for concept drift scenarios and applied in this work to handle workload shifts. Experiments with simulated sudden, gradual, and recurring workload transitions show that static models suffer a notable performance drop when applied to unseen workload profiles, whereas the adaptive model with ADWIN maintains high accuracy, achieving an F1-Score above 0.93 in all analyzed scenarios.

cs.SE↗

arXiv2025

Investigating Software Aging in LLM-Generated Software Systems

Automatically generated software, especially code produced by Large Language Models (LLMs), is increasingly adopted to accelerate development and reduce manual effort. However, little is known about the long-term reliability of such systems under sustained execution. In this paper, we experimentally investigate the phenomenon of software aging in applications generated by LLM-based tools. Using the Bolt platform and standardized prompts from Baxbench, we generated four service-oriented applications and subjected them to 50-hour load tests. Resource usage, response time, and throughput were continuously monitored to detect degradation patterns. The results reveal significant evidence of software aging, including progressive memory growth, increased response time, and performance instability across all applications. Statistical analyzes confirm these trends and highlight variability in the severity of aging according to the type of application. Our findings show the need to consider aging in automatically generated software and provide a foundation for future studies on mitigation strategies and long-term reliability evaluation.

cs.SE↗

arXiv2025

Dependability of UAV-Based Networks and Computing Systems: A Survey

Uncrewed Aerial Vehicle (UAV) computing and networking are becoming a fundamental computation infrastructure for diverse cyber-physical application systems. UAVs can be empowered by AI on edge devices and can communicate with other UAVs and ground stations via wireless communication networks. Dynamic computation demands and heterogeneous computing resources are distributed in the system and need to be controlled to maintain the quality of services and to accomplish critical missions. With the evolution of UAV-based systems, dependability assurance of such systems emerges as a crucial challenge. UAV-based systems confront diverse sources of uncertainty that may threaten their dependability, such as software bugs, component failures, network disconnections, battery shortages, and disturbances from the real world. In this paper, we conduct systematic literature reviews on the dependability of UAV-based networks and computing systems. The survey report reveals emerging research trends in this field and summarizes the literature into comprehensive categories by threat types and adopted technologies. Based on our literature reviews, we identify eight research fields that require further exploration in the future to achieve dependable UAV-based systems.

cs.PF↗

arXiv2023

Predictive Maintenance Model Based on Anomaly Detection in Induction Motors: A Machine Learning Approach Using Real-Time IoT Data

With the support of Internet of Things (IoT) devices, it is possible to acquire data from degradation phenomena and design data-driven models to perform anomaly detection in industrial equipment. This approach not only identifies potential anomalies but can also serve as a first step toward building predictive maintenance policies. In this work, we demonstrate a novel anomaly detection system on induction motors used in pumps, compressors, fans, and other industrial machines. This work evaluates a combination of pre-processing techniques and machine learning (ML) models with a low computational cost. We use a combination of pre-processing techniques such as Fast Fourier Transform (FFT), Wavelet Transform (WT), and binning, which are well-known approaches for extracting features from raw data. We also aim to guarantee an optimal balance between multiple conflicting parameters, such as anomaly detection rate, false positive rate, and inference speed of the solution. To this end, multiobjective optimization and analysis are performed on the evaluated models. Pareto-optimal solutions are presented to select which models have the best results regarding classification metrics and computational effort. Differently from most works in this field that use publicly available datasets to validate their models, we propose an end-to-end solution combining low-cost and readily available IoT sensors. The approach is validated by acquiring a custom dataset from induction motors. Also, we fuse vibration, temperature, and noise data from these sensors as the input to the proposed ML model. Therefore, we aim to propose a methodology general enough to be applied in different industrial contexts in the future.

cs.LG↗