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IEEE International Conference on software Testing Verification and Validation Workshop, ICSTW

作者： Ridwan Hossain Akramul Azim Linda Cato Bruce Wilkins Department of Electrical Computer and Software Engineering Ontario Tech University Ontario Canada Team Eagle Ontario Canada

ISBN: (数字)9798350344790

ISBN: (纸本)9798350344806

Given the intricate composition and complex nature of modern software systems, it is necessary to ensure sufficient software quality throughout their entire life cycle. This paper highlights the development efforts made toward delivering an automated solution for software quality metric acquisition and the analysis of quality-related data for a real-world airfield operations software. The target software system, at the time of producing this paper, consists of over 110 K lines of code, requires over 10 K developer minutes to address quality issues, contains over 140 identified bugs, has approximately 50 security hotspots, and includes nearly 3 K code smells. Considering the abundance of quality-related items uncovered by the solution being developed, the airfield software was presented as an exemplary case study. This paper introduces a novel dual-framework architecture for software quality assurance that specifically targets the airfield software system in focus. This unique approach combines data logging for metric acquisition and machine learning for predictive analysis. This helps address real-time operations, integration challenges, and security concerns in the target software. This paper highlights the tools and technologies selected, the architecture implementing the frameworks and processes used, and the results of preliminary experiments and analysis activities.

关键词： Measurement Codes software quality Machine learning computer architecture software systems Airports

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A Pairwise Surrogate Model using GNN for Evolutionary Optimization

A Pairwise Surrogate Model using GNN for Evolutionary Optimi...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Gharavian, Vida Rahnamayan, Shahryar Bidgoli, Azam Asilian Makrehchi, Masoud Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada Department of Engineering Brock University St. CatharinesON Canada Faculty of Science Wilfrid Laurier University WaterlooON Canada Lab

ISBN: (纸本)9798350337020

Optimization problems widely arise in various science and engineering fields and can be computationally expensive in many real-world applications. Evaluation of the fitness function to assess a candidate solution is the main operation in all optimization procedures which can be heavily compute-intensive. Machine learning-based surrogate models can contribute to learning the specific pattern among the decision variables and objective values to consequently reduce the computation time of fitness evaluation. In this study, we have proposed a novel pairwise surrogate model to identify the superiority between candidate solutions in a pairwise comparison despite the fact that most of the surrogate models try to predict the exact fitness value. The proposed idea can significantly help the optimizer to reach better results in a shorter period of time. It seems comparing two candidate solutions for a greedy selection is much easier than approximating fitness values for both. We demonstrated Graph Neural Network (GNN) for this purpose to be trained on a limited number of pairwise ranks and then utilized to compare a pair of candidate solutions. In order to examine the efficacy of our model, we utilized different well-known single-objective optimization benchmarks in dimensions 10,20, and 30. Moreover, the results of the learning-based evaluation are compared with the results from the real fitness evaluation. The results, assessed in terms of the number of fitness calls and the best-found solution, showed that the proposed method is able to decrease the computing cost of fitness evaluation significantly while we achieve a comparable solution. Our model can be tested with any optimization algorithm which employs a comparison-based mechanism among its candidate solutions. © 2023 IEEE.

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Retrieval Augmented Generation Fine-Tuned LLM Model for Code Recommendations to Mitigate Lock Contention 25

Retrieval Augmented Generation Fine-Tuned LLM Model for Code...

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Companion of the 16th ACM/SPEC International Conference on Performance engineering

作者： Ashadullah Shawon Ramiro Liscano Akramul Azim Vijay Sundaresan Yee-Kang Chang Electrical Computer & Software Engineering Ontario Tech University Oshawa Ontario Canada IBM Toronto Lab Toronto Ontario Canada

ISBN: (纸本)9798400711305

Lock contention performance faults can lead to degradation in the performance of software applications. Unlike software bugs, per- formance faults do not lead to failures and application crashes but surface as a degradation in the response and execution of an ap- plication and can surface fairly late in the deployment life of an application. Tools exist for the identification and detection of lock performance faults but there is a lack of effective code refactor- ing recommendations for a developer to mitigate the performance degradation caused by lock-contention. Recent advances in Large Language Models (LLMs) have demonstrated positive results in code refactoring for fixing software bugs and mitigating run time faults. However, traditional LLM-based approaches often suffer from hal- lucination errors, where the generated code may not accurately reflect the context of the project or existing codebase. This thesis presents a novel approach that combines Retrieval Augmented Gen- eration (RAG) with a fine-tuned LLM model for refactored code recommendation aimed at reducing lock-contention performance faults in Java applications. The RAG fine-tuned model combines the strengths of contextual understanding from LLMs with the preci- sion of retrieval-based systems, thereby ensuring that the generated recommendations are relevant, accurate, and hallucination-free. Se- mantic and syntactic metrics of the recommendations generated by the combined RAG and LLM model show an accuracy of approxi- mately 90% compared to an accuracy of approximately 25% when a baseline LLM model is used.

关键词： code smell

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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

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computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Combined data driven and online impedance measurement-based lithium-ion battery state of health estimation for electric vehicle battery management systems

Combined data driven and online impedance measurement-based ...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Akash Samanta Alvin Huynh Niranjan Shrestha Sheldon Williamson Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada Department of Electrial Computer and Software Engineering Ontario Tech University Oshawa Canada

Impedance measurement-based lithium-ion battery state of health (SOH) estimation technique is the most accurate technique compared to the model-based and data-driven techniques. Typically, electrochemical impedance spectroscopy (EIS) is used to measure the impedance of the lithium-ion battery. However, installing EIS in an on-board battery management system (BMS) for online estimation of SOH is impractical in terms of complexity, cost, and increased weight of BMS. Aiming to provide a solution, a single frequency impedance measurement-based technique is proposed for precise estimation of battery SOH during charging without implementing EIS in electric vehicle BMS. Aim is to estimate battery SOH using battery charger. A Series of laboratory experiments are conducted to collect EIS data at different states of charge and temperatures. After critical analysis of the data, 30% SOC and 1 Hz frequency is considered for measuring the impedance during the charging period for SOH estimation. The proposed SOH estimation technique is highly accurate for all practical purposes BMS while at the same time it is convenient, simple, cost-effective, and does not require any historical usage data.

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Line Graph is the Key: An Exploration of Line Graph on Link Prediction with Social Networks

Line Graph is the Key: An Exploration of Line Graph on Link ...

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International Conference on Behavior, Economic and Social Computing (BESC)

作者： Chen Xing Masoud Makrehchi Dept. of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9798331531904

ISBN: (纸本)9798331531911

Network data plays a crucial role in various real-world applications, as connections between entities can be represented and analyzed through graphs. These include various types, such as social, information, and technical networks. However, the complex topologies of these networks present challenges in converting graph data into machine-readable vector formats. Existing models, such as Graph Neural Networks, Graph Attention Networks, and node2vec, have made strides in graph embeddings. For edge-related tasks, models such as node2vec typically use indirect methods like concatenating node vectors to represent edges. This approach is useful for tasks like link prediction. In this paper, we present LineDi2vec, a novel approach that improves the Node2vec embedding method by utilizing a line graph. The proposed LineDi2vec not only generalizes the original graphs, transforming the relationships between edges and nodes but also maintains the original graphs' topological integrity for effective node embedding by Node2vec. We evaluated LineDi2vec on four real-world datasets, focusing on link prediction. The results demonstrate that LineDi2vec outperforms traditional node concatenation methods.

关键词： Analytical models Social computing Social networking (online) Network topology Navigation Network analyzers Predictive models Vectors Robustness Topology

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Optimal Slot Utilization in IEEE 802.15.4e TSCH Networks using Reinforcement Learning

Optimal Slot Utilization in IEEE 802.15.4e TSCH Networks usi...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Tarana Ara Ramiro Liscano Dept. of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

This paper presents an innovative MAC scheduling algorithm to achieve energy savings in IEEE 802.15.4e TSCH sensor networks leveraging reinforcement learning to determine an optimal number of slots to keep active in order to maintain a specific packet delivery ratio for the network. The goal is to turn off slots in the 802.15.4e TSCH frame that are not highly utilized. Each node determines the slots that should be deactivated based on a threshold $\mathbf{Q}$ value. This scheduling strategy allows the nodes to conserve energy effectively by finding the optimal active timeslots for both transmission and reception. This algorithm we name it as RAST Reinforcement-based Slot utilization technique) algorithm. Through extensive simulations and evaluations across various network configurations, the RAST algorithm reveals a significant packet delivery ratio improvement as compared to Orchestra utilizing less active slots.

关键词： IEEE 802.15 Standard Scheduling algorithms Energy conservation Reinforcement learning Land mobile radio

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ML-Based Test Case Prioritization: A Research and Production Perspective in CI Environments

ML-Based Test Case Prioritization: A Research and Production...

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International Conference on software Testing, Verification, and Validation, ICST

作者： Md Asif Khan Akramul Azim Ramiro Liscano Kevin Smith Yee-Kang Chang Gkerta Seferi Qasim Tauseef Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Ontario International Business Machines Corporation (IBM)

ISBN: (数字)9798331508142

ISBN: (纸本)9798331508159

Test case prioritization (TCP) is essential for improving testing efficiency in large-scale continuous integration (CI) environments by reducing feedback time and efficient resource usage. Machine learning (ML) has shown promise in enhancing TCP, however, demonstrating its effectiveness in production environments remains a challenge. Using the IBM Open Liberty dataset, we developed and validated an ML-based TCP framework, showing how we identified the best-performing model step by step-from feature extraction and model training to hyperparameter tuning. After validating the framework in a research setting, we deployed it in IBM's live production system. The practical implications of this study are as follows. The production results closely mirrored the research outcomes, with models trained on recent data consistently outperforming older models and non-prioritized approaches. Specifically, prioritized builds achieved a mean Average Percentage of Faults Detected (APFD) value 50% higher than that of non-prioritized builds, leading to a substantial improvement in early fault detection. The consistent improvement of models trained on newer data (M-2023) over those trained on older data (M-2022) underscores the importance of regular model updates in maintaining optimal performance. This paper comprehensively compares research and production data, illustrating how our ML-driven TCP framework ensures optimal performance and detailing the steps necessary for successful implementation in dynamic CI environments.

关键词： Training software testing Production systems Fault detection Training data Machine learning Data models software software reliability Tuning

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Self-Supervised Learning Using Noisy-Latent Augmentation

Self-Supervised Learning Using Noisy-Latent Augmentation

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Khosrowshahli, Rasa Rahnamayan, Shahryar Bidgoli, Azam Asilian Makrehchi, Masoud Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada Engineering Department Brock University St. CatharinesON Canada Faculty of Science Wilfrid Laurier University WaterlooON Canada Lab Canada

ISBN: (纸本)9798350337020

Generally speaking, labeled data is difficult and expensive to provide for applications in machine learning and data mining. One of the earliest approaches to tackle this problem is semi-supervised self-training to take advantages of labeled and unlabeled data to create pseudo-labeled data. However, reaching a high level of confidence to predict the unseen data by a classifier with a limited number of labeled samples is a challenging task. Generating pseudo-labeled data can collaboratively improve the self-training to increase its confidence for further predictions. This paper proposes a collaborative framework between augmentation and self-training to accurately train a model with very limited labeled data. Our framework includes two components where the first component is an unsupervised technique to augment labeled data and feed to the second component which is self-training. The first component uses a custom variational autoencoder architecture (VAE) to generate new samples by adding randomly generated noise to encoded latent representation. As a result, the augmentation component can generate the unique and unexplored images with respect to limited input data distribution. We evaluated the proposed framework on handwritten MNIST image dataset. The conducted experiment shows that the generative component can be helpful in overcoming the problem of inaccurate self-training prediction when sufficient labeled data is not accessible. © 2023 IEEE.

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Statistics-Based Algorithm to Reduce Grid Harmonics due to EV Chargers

Statistics-Based Algorithm to Reduce Grid Harmonics due to E...

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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Yadunath Sapkota Ahmed Sheir Vijay K. Sood Department of Electrical Computer and Software Engineering Ontario Tech University Ontario Canada

The growing global market of electric vehicles (EVs) increasingly needs fast charging stations to be installed at many facilities such as university campuses, shopping malls, hospitals, parking facilities etc. When consumers start plugging EVs in large numbers, the grid power quality is expected to deteriorate. This paper proposes a new statistics-based algorithm to reduce the grid harmonics caused by EVs acting essentially as non-linear loads. The proposed algorithm helps reduce grid harmonics by phase-shifting the carrier-wave of each converter within a charging station so that some of the harmonics will cancel each other. This new technique does not require any centralized communication or information exchange between the charging bays to coordinate the carrier-waves. As an example, the algorithm is implemented in a charging station with 20 bays by utilizing the Mean values of the Cumulative Poisson Distribution. The methodology is simulated using the MATLAB Simulink platform. Results show an improvement of nearly 30% in the THD of the grid voltage.

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