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IEEE International Conference on Automated software engineering (ASE)

作者： Yuanjie Xia Zishuo Ding Weiyi Shang Department of Computer Science and Software Engineering Concordia University Montreal Canada

Highly configurable systems enable customers to flexibly configure the systems in diverse deployment environments. The flexibility of configurations also poses challenges for performance testing. On one hand, there exist a massive number of possible configurations; while on the other hand, the time and resources are limited for performance testing, which is already a costly process during software development. Modeling the performance of configurations is one of the solutions to reduce the cost of configuration performance testing. Although prior research proposes various modeling and sampling techniques to build configuration performance models, the sampling approaches used in the model typically do not consider the accuracy of the performance models, leading to potential sub-optimal performance modeling results in practice. In this paper, we present a modeling-driven sampling approach (CoMSA) to improve the performance modeling of highly configurable systems. The intuition of CoMSA is to select samples based on their uncertainties to the performance models. In other words, the configurations that have the more uncertain performance prediction results by the performance models are more likely to be selected as further training samples to improve the model. CoMSA is designed by considering both scenarios where 1) the software projects do not have historical performance testing results (cold start) and 2) there exist historical performance testing results (warm start). We evaluate the performance of our approach in four subjects, namely LRZIP, LLVM, x264, and SQLite. Through the evaluation result, we can conclude that our sampling approaches could highly enhance the accuracy of the prediction models and the efficiency of configuration performance testing compared to other baseline sampling approaches.

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Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis

arXiv

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arXiv 2023年

作者： Pepe, Inés Gonzalez Sivakolunthu, Vinuyan Park, Hae Lang Chatelain, Yohan Glatard, Tristan Department of Computer Science and Software Engineering Concordia University Montreal Canada

This paper investigates the numerical uncertainty of Convolutional Neural Networks (CNNs) inference for structural brain MRI analysis. It applies Random Rounding-a stochastic arithmetic technique-to CNN models employed in non-linear registration (SynthMorph) and whole-brain segmentation (FastSurfer), and compares the resulting numerical uncertainty to the one measured in a reference image-processing pipeline (FreeSurfer recon-all). Results obtained on 32 representative subjects show that CNN predictions are substantially more accurate numerically than traditional image-processing results (non-linear registration: 19 vs 13 significant bits on average;whole-brain segmentation: 0.99 vs 0.92 Sørensen-Dice score on average), which suggests a better reproducibility of CNN results across execution environments. © 2023, CC BY.

关键词： Stochastic systems

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Unlocking DtS-IoT Medium Access Through the Massively Scalable Distributed Queuing Protocol 10

Unlocking DtS-IoT Medium Access Through the Massively Scalab...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Gerard, Jason Fraire, Juan A. Cespedes, Sandra Concordia University Department of Computer Science and Software Engineering Montreal Canada Inria INSA Lyon CITI UR3720 Villeurbanne69621 France CONICET - Universidad Nacional de Córdoba Córdoba Argentina

ISBN: (纸本)9798350373011

Direct-to-satellite Internet of Things networks (DtSIoT) offer a cost-effective way to establish global communication between IoT devices and low Earth orbit satellites. However, budget-conscious DtS-IoT systems face resource constraints in user, space, and ground segments. An increasing limitation in the performance of satellite IoT networks is observed in the link layer. This stems from the prevalent use of ALOHA-based MAC protocols, leading to constraints in managing larger networks. The requirements of DtS-IoT deployments with extensive network sizes amplify the growing demands for energy-efficient and scalable MAC protocols. In this paper, we formulate a new energy-efficient MAC protocol, Massively Scalable Distributed Queuing (MSDQ), inspired by components of the Distributed Queuing (DQ) protocol and the RESS-IoT protocol. Utilizing FLoRaSat, a realistic LoRa-based DtS-IoT network simulator, we showcase the performance of MSDQ, DQ, and RESS-IoT. Results indicate that MSDQ outperforms DQ by 2.2 times the throughput and 19 times the node energy efficiency of RESSIoT. These findings highlight the performance gap of existing MAC protocols for large and geographically distributed DtSIoT systems. © 2024 IEEE.

关键词： Budget control

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On the Temporal Relations between Logging and Code

On the Temporal Relations between Logging and Code

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International Conference on software engineering (ICSE)

作者： Zishuo Ding Yiming Tang Yang Li Heng Li Weiyi Shang Department of Computer Science and Software Engineering Concordia University Montreal Canada Beijing University of Posts and Telecommunications Beijing China Polytechnique Montréal Montreal Canada

Prior work shows that misleading logging texts (i.e., the textual descriptions in logging statements) can be counterproductive for developers during their use of logs. One of the most important types of information provided by logs is the temporal information of the recorded system behavior. For example, a logging text may use a perfective aspect to describe a fact that an important system event has finished. Although prior work has performed extensive studies on automated logging suggestions, few of these studies investigate the temporal relations between logging and code. In this work, we make the first attempt to comprehensively study the temporal relations between logging and its corresponding source code. In particular, we focus on two types of temporal relations: (1) logical temporal relations, which can be inferred from the execution order between the logging statement and the corresponding source code; and (2) semantic temporal relations, which can be inferred based on the semantic meaning of the logging text. We first perform qualitative analyses to study these two types of logging-code temporal relations and the inconsistency between them. As a result, we derive rules to detect these two types of temporal relations and their inconsistencies. Based on these rules, we propose a tool named TempoLo to automatically detect the issues of temporal inconsistencies between logging and code. Through an evaluation of four projects, we find that TempoLo can effectively detect temporal inconsistencies with a small number of false positives. To gather developers' feedback on whether such inconsistencies are worth fixing, we report 15 detected instances from these projects to developers. 13 instances from three projects are confirmed and fixed, while two instances of the remaining project are pending at the time of this writing. Our work lays the foundation for describing temporal relations between logging and code and demonstrates the potential for a deeper understandin

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Meta Episodic learning with Dynamic Task Sampling for CLIP-based Point Cloud Classification

arXiv

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arXiv 2024年

作者： Ghose, Shuvozit Wang, Yang Department of Computer Science University of Manitoba Winnipeg Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

Point cloud classification refers to the process of assigning semantic labels or categories to individual points within a point cloud data structure. Recent works have explored the extension of pre-trained CLIP to 3D recognition. In this direction, CLIP-based point cloud models like PointCLIP, CLIP2Point have become state-of-the-art methods in the few-shot setup. Although these methods show promising performance for some classes like airplanes, desks, guitars, etc, the performance for some classes like the cup, flower pot, sink, nightstand, etc is still far from satisfactory. This is due to the fact that the adapter of CLIP-based models is trained using randomly sampled N-way K-shot data in the standard supervised learning setup. In this paper, we propose a novel meta-episodic learning framework for CLIP-based point cloud classification, addressing the challenges of limited training examples and sampling unknown classes. Additionally, we introduce dynamic task sampling within the episode based on performance memory. This sampling strategy effectively addresses the challenge of sampling unknown classes, ensuring that the model learns from a diverse range of classes and promotes the exploration of underrepresented categories. By dynamically updating the performance memory, we adaptively prioritize the sampling of classes based on their performance, enhancing the model’s ability to handle challenging and real-world scenarios. Experiments show an average performance gain of 3-6% on ModelNet40 and ScanobjectNN datasets in a few-shot setup. © 2024, CC BY.

关键词： Image classification

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Node Compass: Multilevel Tracing and Debugging of Request Executions in JavaScript-Based Web-Servers

arXiv

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arXiv 2023年

作者： Kabamba, Herve M. Khouzam, Matthew Dagenais, Michel Computer and Software Engineering Department Polytechnique Montréal Canada Ericsson Canada Canada

Adequate consideration is crucial to ensure that services in a distributed application context are running satisfactorily with the resources available. Due to the asynchronous nature of tasks and the need to work with multiple layers that deliver coordinated results in a single-threaded context, analysing performance is a challenging task in event-loop-based systems. The existing performance analysis methods for environments such as *** rely on higher-level instrumentation but lack precision, as they can't capture the relevant underlying application flow. As a solution, we propose a streamlined method for recovering the asynchronous execution path of requests called the Nested Bounded Context Algorithm (NBCA). The proposed technique tracks the application execution flow through multiple layers and showcases it on an interactive interface for further assessment. Furthermore, we introduce the vertical span concept. This representation of a span as a multidimensional object (horizontal and vertical) with a start and end of execution, along with its sub-layers and triggered operations, enables the granular identification and diagnosis of performance issues. We proposed a new technique called the Bounded Context Tracking Algorithm (BCTA) for event matching and request reassembling in a multi-layer trace. The two techniques allow aligning the executions of the request in a tree-based data structure for developed visualisations. These visualisations permit performance debugging of complex performance issues in ***. © 2023, CC BY-NC-ND.

关键词： Visualization

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Vnode: Low-overhead Transparent Tracing of ***-based Microservice Architectures

arXiv

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arXiv 2023年

作者： Kabamba, Herve Mbikayi Khouzam, Matthew Dagenais, Michel Computer and Software Engineering Department Polytechnique Montréal Canada Ericsson Canada Canada

Tracing serves as a key method for evaluating the performance of microservices-based architectures, which are renowned for their scalability, resource efficiency, and high availability. Despite their advantages, these architectures often pose unique debugging challenges that necessitate trade-offs, including the burden of instrumentation overhead. With *** emerging as a leading development environment, recognized for its rapidly growing ecosystem, there is a pressing need for innovative approaches that reduce the telemetry data collection efforts, and the overhead incurred by the environment instrumentation. In response, we introduce a new approach designed for transparent tracing and seamless deployment of microservices in cloud settings. This approach is centered around our newly developed Internal Transparent Tracing and Context Reconstruction (ITTCR) algorithm. ITTCR is adept at correlating internal metrics from various distributed trace files, to reconstruct the intricate execution contexts of microservices operating in a *** environment. Our method achieves transparency by directly instrumenting the *** virtual machine, enabling the collection and analysis of trace events in a transparent manner. This process facilitates the creation of visualization tools, enhancing the understanding and analysis of microservice performance in cloud environments. © 2023, CC BY-NC-ND.

关键词： Economic and social effects

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Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks

arXiv

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arXiv 2022年

作者： Abouaomar, Amine Taik, Afaf Filali, Abderrahime Cherkaoui, Soumaya The Department of Computer and Software Engineering Polytechnique Montreal Canada Université de Sherbrooke Canada

Radio access network (RAN) slicing is a key element in enabling current 5G networks and next-generation networks to meet the requirements of different services in various verticals. However, the heterogeneous nature of these services’ requirements, along with the limited RAN resources, makes the RAN slicing very complex. Indeed, the challenge that mobile virtual network operators (MVNOs) face is to rapidly adapt their RAN slicing strategies to the frequent changes of the environment constraints and service requirements. Machine learning techniques, such as deep reinforcement learning (DRL), are increasingly considered a key enabler for automating the management and orchestration of RAN slicing operations. Nerveless, the ability to generalize DRL models to multiple RAN slicing environments may be limited, due to their strong dependence on the environment data on which they are trained. Federated learning enables MVNOs to leverage more diverse training inputs for DRL without the high cost of collecting this data from different RANs. In this paper, we propose a federated deep reinforcement learning approach for RAN slicing. In this approach, MVNOs collaborate to improve the performance of their DRL-based RAN slicing models. Each MVNO trains a DRL model and sends it for aggregation. The aggregated model is then sent back to each MVNO for immediate use and further training. The simulation results show the effectiveness of the proposed DRL approach. © 2022, CC BY.

关键词： Reinforcement learning

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On Codex Prompt engineering for OCL Generation: An Empirical Study

arXiv

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arXiv 2023年

作者： Abukhalaf, Seif Hamdaqa, Mohammad Khomh, Foutse Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada

The Object Constraint Language (OCL) is a declarative language that adds constraints and object query expressions to Meta-Object Facility (MOF) models. OCL can provide precision and conciseness to UML models. Nevertheless, the unfamiliar syntax of OCL has hindered its adoption by software practitioners. LLMs, such as GPT-3, have made significant progress in many NLP tasks, such as text generation and semantic parsing. Similarly, researchers have improved on the downstream tasks by fine-tuning LLMs for the target task. Codex, a GPT-3 descendant by OpenAI, has been fine-tuned on publicly available code from GitHub and has proven the ability to generate code in many programming languages, powering the AI-pair programmer Copilot. One way to take advantage of Codex is to engineer prompts for the target downstream task. In this paper, we investigate the reliability of the OCL constraints generated by Codex from natural language specifications. To achieve this, we compiled a dataset of 15 UML models and 168 specifications from various educational resources. We manually crafted a prompt template with slots to populate with the UML information and the target task in the prefix format to complete the template with the generated OCL constraint. We used both zero- and few-shot learning methods in the experiments. The evaluation is reported by measuring the syntactic validity and the execution accuracy metrics of the generated OCL constraints. Moreover, to get insight into how close or natural the generated OCL constraints are compared to human-written ones, we measured the cosine similarity between the sentence embedding of the correctly generated and human-written OCL constraints. Our findings suggest that by enriching the prompts with the UML information of the models and enabling few-shot learning, the reliability of the generated OCL constraints increases. Furthermore, the results reveal a close similarity based on sentence embedding between the generated OCL constraints an

关键词： Syntactics

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An Area-efficient Memory-based Architecture for P4-programmable Streaming Parsers in FPGAs

An Area-efficient Memory-based Architecture for P4-programma...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Parisa Mashreghi-Moghadam Tarek Ould-Bachir Yvon Savaria Department of Electrical Engineering Polytechnique Montréal Montréal Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada

Moving toward software-defined networking and function virtualization, flexibility and reconfigurability of the network have become more and more critical. Packet parsing, the first processing stage of programmable switches, requires high performance and reconfigurability to allow implementing low-latency and highly flexible data networks. This paper proposes an overlay architecture for an FPGA-based P4-programmable streaming packet parser. The purpose of this architecture is to allow supporting different functionality with a fixed hardware design by changing a program stored in an embedded memory. This program is derived from the parser section of a P4 code, describing a parsing graph. This approach eliminates a pipeline of parsing blocks in favor of a single parsing block, thereby reducing the design's complexity. Our architecture offers an 11 Gb/s data rate on a Xilinx Virtex-7 XC7VX690 FPGA, while its implementation requires 312 LUTs and 1135 FFs.

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