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arXiv 2024年

作者： Qin, Qiaolin Li, Heng Merlo, Ettore Lamothe, Maxime Dept. of Computer and Software Engineering Polytechnique Montreal Montreal Canada

With the advent of data-centric and machine learning (ML) systems, data quality is playing an increasingly critical role for ensuring the overall quality of software systems. Data preparation, an essential step towards high data quality, is known to be a highly effort-intensive process. Although prior studies have dealt with one of the most impacting issues, data pattern violations, these studies usually require data-specific configurations (i.e., parameterized) or use carefully curated data as learning examples (i.e., supervised), relying on domain knowledge and deep understanding of the data, or demanding significant manual effort. In this paper, we introduce RIOLU: Regex Inferencer autO-parameterized Learning with Uncleaned data. RIOLU is fully automated, automatically parameterized, and does not need labeled samples. RIOLU can generate precise patterns from datasets in various domains, with a high F1 score of 97.2%, exceeding the state-of-the-art baseline. In addition, according to our experiment on five datasets with anomalies, RIOLU can automatically estimate a data column’s error rate, draw normal patterns, and predict anomalies from unlabeled data with higher performance (up to 800.4% improvement in terms of F1) than the state-of-the-art baseline, even outperforming ChatGPT in terms of both accuracy (12.3% higher F1) and efficiency (10% less inference time). A variant of RIOLU, with user guidance, can further boost its precision, with up to 37.4% improvement in terms of F1. Our evaluation in an industrial setting further demonstrates the practical benefits of *** Codes 68P01 © 2024, CC BY.

关键词： Self-supervised learning

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Assessing the Security of GitHub Copilot's Generated Code-A Targeted Replication Study 31

Assessing the Security of GitHub Copilot's Generated Code-A ...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Majdinasab, Vahid Bishop, Michael Joshua Rasheed, Shawn Moradidakhel, Arghavan Tahir, Amjed Khomh, Foutse Polytechnique Department of Computer and Software Engineering Montreal Canada School of Mathematical and Computational Sciences Massey University New Zealand Information & Communication Technology Group UCOL-Te Pukenga New Zealand

ISBN: (纸本)9798350330663

AI-powered code generation models have been developing rapidly, allowing developers to expedite code generation and thus improve their productivity. These models are trained on large corpora of code (primarily sourced from public repositories), which may contain bugs and vulnerabilities. Several concerns have been raised about the security of the code generated by these models. Recent studies have investigated security issues in AI-powered code generation tools such as GitHub Copilot and Amazon Code Whisperer, revealing several security weaknesses in the code generated by these tools. As these tools evolve, it is expected that they will improve their security protocols to prevent the suggestion of insecure code to developers. This paper replicates the study of Pearce et al., which investigated security weaknesses in Copilot and uncovered several weaknesses in the code suggested by Copilot across diverse scenarios and languages (Python, C, and Verilog). Our replication examines Copilot's security weaknesses using newer versions of Copilot and CodeQL (the security analysis framework). The replication focused on the presence of security vulnerabilities in Python code. Our results indicate that, with the improvements in newer versions of Copilot, the percentage of vulnerable code suggestions has reduced from 36.54% to 27.25%. Nonetheless, it remains evident that the model still suggests insecure code. © 2024 IEEE.

关键词： Network security

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Batch Execution of Microbenchmarks for Efficient Performance Testing 18

Batch Execution of Microbenchmarks for Efficient Performance...

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18th IEEE Conference on software Testing, Verification and Validation, ICST 2025

作者： Jangali, Mostafa Yao, Kundi Tang, Yiming Costa, Diego Elias Shang, Weiyi University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada Rochester Institute of Technology Department of Software Engineering United States Concordia University Computer Science and Software Engineering Montreal Canada

ISBN: (纸本)9798331508142

Performance microbenchmarking is essential for ensuring software quality by providing granular insights into code efficiency. While automated performance microbenchmark generation tools (e.g., ju2jmh) are proposed to alleviate practitioners from manually curating microbenchmarks, the high volume of generated benchmarks can lead to protracted benchmarking execution time, as many of the generated benchmarks are too short in nature to be valuable for evaluating performance. In this paper, we present a novel approach that optimizes microbenchmark execution through a batching strategy, i.e., grouping benchmarks with similar code coverage and treating them as a single unit to 1) reduce execution overhead and 2) reduce the bias from microbenchmarks that are too short. We evaluate the effectiveness of this enhancement across various Java projects, comparing the execution times of clustered and individual micro benchmarks. Our findings demonstrate substantial improvements in execution efficiency, reducing execution time by up to 89.81% while preserving high microbenchmark stability. © 2025 IEEE.

关键词： software quality

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On the Effectiveness of Log Representation for Log-based Anomaly Detection

arXiv

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arXiv 2023年

作者： Wu, Xingfang Li, Heng Khomh, Foutse Department of Computer Engineering and Software Engineering Polytechnique Montreal MontrealQC Canada

Logs are an essential source of information for people to understand the running status of a software system. Due to the evolving modern software architecture and maintenance methods, more research efforts have been devoted to automated log analysis. In particular, machine learning (ML) has been widely used in log analysis tasks. In ML-based log analysis tasks, converting textual log data into numerical feature vectors is a critical and indispensable step. However, the impact of using different log representation techniques on the performance of the downstream models is not clear, which limits researchers and practitioners’ opportunities of choosing the optimal log representation techniques in their automated log analysis workflows. Therefore, this work investigates and compares the commonly adopted log representation techniques from previous log analysis research. Particularly, we select six log representation techniques and evaluate them with seven ML models and four public log datasets (i.e., HDFS, BGL, Spirit and Thunderbird) in the context of log-based anomaly detection. We also examine the impacts of the log parsing process and the different feature aggregation approaches when they are employed with log representation techniques. From the experiments, we provide some heuristic guidelines for future researchers and developers to follow when designing an automated log analysis workflow. We believe our comprehensive comparison of log representation techniques can help researchers and practitioners better understand the characteristics of different log representation techniques and provide them with guidance for selecting the most suitable ones for their ML-based log analysis workflow. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Studying Logging Practice in Machine Learning-based Applications

arXiv

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arXiv 2023年

作者： Foalem, Patrick Loic Khomh, Foutse Li, Heng Department of Computer Engineering and Software Engineering Polytechnique Montreal MontrealQC Canada

Logging is a common practice in traditional software development. Several research works have been done to investigate the different characteristics of logging practices in traditional software systems (e.g., Android applications, JAVA applications, C/C++ applications). Nowadays, we are witnessing more and more development of Machine Learning-based applications (ML-based applications). Today, there are many popular libraries that facilitate and contribute to the development of such applications, among which we can mention: Pytorch, Tensorflow, Theano, MXNet, Scikit-Learn, Caffe, and Keras. Despite the popularity of ML, we don’t have a clear understanding of logging practices in ML applications. In this paper, we aim to fill this knowledge gap and help ML practitioners understand the characteristics of logging in ML-based applications. In particular, we conduct an empirical study on 110 open-source ML-based applications. Through a quantitative analysis, we find that logging practice in ML-based applications is less pervasive than in traditional applications including Android, JAVA, and C/C++ applications. Furthermore, the majority of logging statements in ML-based applications are in info and warn levels, compared to traditional applications where info is the majority of logging statement in C/C++ application and debug, error levels constitute the majority of logging statement in Android application. We also perform a quantitative and qualitative analysis of a random sample of logging statements to understand where ML developers put most of logging statements and examine why and how they are using logging. These analyses led to the following observations: (i) ML developers put most of the logging statements in model training, and in non-ML components. (ii) Data and model management appear to be the main reason behind the introduction of logging statements in ML-based applications. Indeed, ML developers use logging statements to keep track of the different stages of d

关键词： Machine learning

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Bug Characteristics in Quantum software Ecosystem

SSRN

引用

SSRN 2023年

作者： El Aoun, Mohamed Raed Li, Heng Khomh, Foutse Tidjon, Lionel Department of Computer Engineering and Software Engineering Polytechnique Montreal MontrealQC Canada

Recent advancements in quantum computing, exemplified by IBM's quantum computers, underscore the importance of quantum software. Differing fundamentally from classical programming, quantum programming's probabilistic state and inherent error-prone nature due to quantum instabilities pose unique challenges. This study investigates bug characteristics in quantum software projects to inform effective testing and debugging strategies. We analyzed bug reports from 125 quantum software projects on GitHub and found them more error-prone and costly to rectify compared to classical software. Key areas where bugs manifested include the compiler, gate operation, and state preparation components. The predominant bug types are Program anomaly, Configuration, and Data type & structure bugs. A notable challenge in quantum software development is the absence of comprehensive quantum computation libraries and specific quantum gate operation definitions. Additionally, there's a demand for dedicated data manipulation libraries akin to Numpy for quantum computing. Our insights spotlight areas for future research, emphasizing the need for specialized debugging tools for quantum circuits. © 2023, The Authors. All rights reserved.

关键词： Quantum computers

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Generalization of Urban Wind Environment Using Fourier Neural Operator Across Different Wind Directions and Cities

arXiv

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arXiv 2025年

作者： Chen, Cheng Tian, Geng Qin, Shaoxiang Geng, Dingyang Zhan, Dongxue Yang, Jinqiu Vidal, David Wang, Liangzhu Concordia University Department of Building Civil and Environmental Engineering Department of Computer Science and Software Engineering Montreal Canada Polytechnique Montreal Department of Mechanical Engineering Montreal Canada

Simulation of urban wind environments is crucial for urban planning, pollution control, and renewable energy utilization. However, the computational requirements of high-fidelity computational fluid dynamics (CFD) methods make them impractical for real cities. To address these limitations, this study investigates the effectiveness of the Fourier Neural Operator (FNO) model in predicting flow fields under different wind directions and urban layouts. In this study, we investigate the effectiveness of the Fourier Neural Operator (FNO) model in predicting urban wind conditions under different wind directions and urban layouts. By training the model on velocity data from large eddy simulation data, we evaluate the performance of the model under different urban configurations and wind conditions. The results show that the FNO model can provide accurate predictions while significantly reducing the computational time by 99%. Our innovative approach of dividing the wind field into smaller spatial blocks for training improves the ability of the FNO model to capture wind frequency features effectively. The SDF data also provides important spatial building information, enhancing the model’s ability to recognize physical boundaries and generate more realistic predictions. The proposed FNO approach enhances the AI model’s generalizability for different wind directions and urban layouts. Copyright © 2025, The Authors. All rights reserved.

关键词： Computational fluid dynamics

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Virtual Networks Link-Layer Topologies Discovery through Host-based Tracing

Virtual Networks Link-Layer Topologies Discovery through Hos...

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2022 IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Belkhiri, Adel Bushehri, Ahmad Shahnejat Dagenais, Michel Computer and Software Engineering École Polytechnique de Montréal Montreal Canada Mathematics and Industrial Engineering École Polytechnique de Montréal Montreal Canada

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

The network link-layer topology, also known as the physical topology, describes the networking hardware devices, their corresponding placement, and the interconnection between them. Discovering and maintaining updated information about the network topology is of utmost importance for many protocols and monitoring tools. For instance, the algorithms of most routing protocols include modules in charge of the automatic discovery of the link-layer topology. On the other hand, network virtualization is one of the technologies that enable Virtual Machines (VMs) in a cloud platform to share the same physical network infrastructure. This technology has been one of the most important key players in the Cloud Computing industry. Unfortunately, research communities have put a modest effort into developing efficient algorithms capable of discovering the topology of Virtual Networks (VNs).In this paper, we address this issue and propose an approach to recover the link-layer topology of a VN from an execution trace. Our approach uses tracing techniques to collect data from the kernels of host machines. Since we limit the data collection only to the host machine, this approach is completely transparent to VMs. Furthermore, we propose a prototype framework that implements our approach. © 2022 IEEE.

关键词： Network topology

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Workshop on Challenges in Performance Methods for software Development (WOSP-C) 15

Workshop on Challenges in Performance Methods for Software D...

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15th ACM/SPEC International Conference on Performance engineering, ICPE 2024

作者： Traini, Luca Li, Heng Department of Information Engineering Computer Science and Mathematics University of l'Aquila L'Aquila Italy Polytechnique Montréal Montreal Canada

ISBN: (纸本)9798400704451

We are pleased to welcome you to the 9thWorkshop on Challenges in Performance Methods for software Development - WOSP-C 2024 (https://***/wosp-c-24/). This year's edition continues its tradition of being the forum for discussions on novel and unresolved challenges in the field of software performance engineering. The primary areas of interest for the workshop remain consistent with previous editions, but the scope of this year's edition has been extended to include additional novel topics. WOSP-C 2024 will cover topics such as performance assurance processes, the interplay between performance and privacy, green software, performance modeling, and simulation, among others. © 2024 Copyright held by the owner/author(s).

关键词： software design

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Peel-And-Bound: Generating Stronger Relaxed Bounds with Multivalued Decision Diagrams 28

Peel-And-Bound: Generating Stronger Relaxed Bounds with Mult...

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28th International Conference on Principles and Practice of Constraint Programming, CP 2022

作者： Rudich, Isaac Cappart, Quentin Rousseau, Louis-Martin Mathematics and Industrial Engineering Department Polytechnique Montréal Canada Computer Engineering and Software Engineering Department Polytechnique Montréal Canada

ISBN: (纸本)9783959772402

Decision diagrams are an increasingly important tool in cutting-edge solvers for discrete optimization. However, the field of decision diagrams is relatively new, and is still incorporating the library of techniques that conventional solvers have had decades to build. We drew inspiration from the warm-start technique used in conventional solvers to address one of the major challenges faced by decision diagram based methods. Decision diagrams become more useful the wider they are allowed to be, but also become more costly to generate, especially with large numbers of variables. We present a method of peeling off a sub-graph of previously constructed diagrams and using it as the initial diagram for subsequent iterations that we call peel-and-bound. We test the method on the sequence ordering problem, and our results indicate that our peel-and-bound scheme generates stronger bounds than a branch-and-bound scheme using the same propagators, and at significantly less computational cost. © Isaac Rudich, Quentin Cappart, and Louis-Martin Rousseau.

关键词： Constraint theory

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