ABSTRACT Background In the data era, big data systems have emerged as pivotal tools, underscoring the importance of performance prediction in enhancing the efficiency of big data clusters. Numerous performance models have been proposed, often grounded in artificial intelligence or simulation methodologies. While the bulk of research focuses on refining prediction precision and minimizing overhead, limited attention has been given to the consignation and standardization of these models. Objectives To bridge this gap between model developers and end‐users, this paper introduces AMORA—a novel versatile framework tailored for predicting the performance of big data systems. Methods Leveraging the identified behavior descriptions‐computation submodels (BD‐CS) pattern that is prevalent among various big data job performance models, AMORA allows access to different plugins accommodating different performance models' implementations. This framework also integrates a novel mutable computation graph technique to facilitate backtracking computation. Furthermore, AMORA's functionality extends to comprehensive end‐to‐end usability by enabling the acceptance of origin configuration files from diverse big data systems and presenting easily interpretable prediction reports. Results This work demonstrates AMORA's efficacy in producing an accurate trace of Hadoop job through the selection of appropriate performance model plugins and parameter adjustments and showcasing the application of the proposed mutable computation graph technique in calculating the starting moment of an early‐start reducer. Additionally, two validation experiments are conducted, involving the implementation of various Hadoop and Spark performance models, respectively. The experiment results manifest the prediction precision and overheads of these performance models. Conclusion These experiments exhibit AMORA's role as a benchmark platform for implementing various types of big data job performance models catered to diverse big data systems.

Weiwei Lin

Haojun Xu

Haocheng Zhong

Fan Chen

Zhengyang Hu

Software Practice and Experience

AMORA: An Advanced Malleable and Operational Framework for Performance Prediction of Big Data Systems

AMORA：一种用于大数据系统性能预测的高级可塑性和操作性框架

ABSTRACT Introduction Much of the effort spent on software development is allocated on detecting and fixing bugs or, more generally, violations that lead to erroneous or inefficient code. Although static analysis tools aspire to automate bug detection, their usage is typically limited to code style rules and typical violations, while they only provide generic instructions for bug fixing. As a result, contemporary approaches focus on extracting bug‐fix patterns from source code revisions (commits). Methodology In this work, we build a methodology that extracts fix patterns for violations detected by the PMD static analysis tool. In contrast to current approaches, which employ specific data sources and focus on particular violations, our system extracts source code edits from multiple GitHub repositories and maps them into 36 different types of violations. By employing a detailed syntax tree representation and a tree edit distance technique, we build a similarity scheme for source code edits, which is used to group them into clusters. We employ two clustering algorithms, K‐medoids and DBSCAN, and further optimize them using a purity metric to produce clusters that correspond to specific fixes. Results Our evaluation indicates that DBSCAN extracts more cohesive clusters (purity more than 0.9), which effectively target specific PMD rules, while K‐medoids extracts generic clusters (purity around 0.7) that pinpoint common edits. Conclusion Finally, upon analyzing the diversity of the sources from which fixes are derived (commits and repositories), we conclude that they are generic enough to reflect how similar issues are dealt with by the developer community.

Michael Karatzas

Themistoklis Diamantopoulos

Andreas Symeonidis

Extracting Fix Patterns for Static Analysis Violations Based on Collective Developer Knowledge

基于集体开发者知识的静态分析违规修复模式提取

ABSTRACT Many programming languages and technologies have appeared for the purpose of software development. When choosing a programming language, the developers' cognitive attributes, such as the Big5 personality traits (BPT), may play a role. The developers' personality traits can be reflected in their social media content (e.g., tweets, statuses, Q&amp;A, reputation). In this article, we predict the developers' programming language preferences (i.e., the pattern of picking up a language) from their BPT derived from their content produced on social media. We randomly collected data from a total of 820 Twitter (currently X) and Stack Overflow (SO) users. Then, we collected user features (i.e., BPT, word embedding of tweets) from Twitter and programming preferences (i.e., programming tags, reputation, question, answer) from SO. We applied various machine learning (ML) and deep learning (DL) techniques to predict their programming language preferences from their BPT. We also investigated other interesting insights, such as how reputation and question‐asking/replying are associated with the users' BPT. The findings suggest that developers with high openness, conscientiousness, and extraversion are inclined to mobile applications, object‐oriented programming, and web programming, respectively. Furthermore, developers with high openness and conscientiousness traits have a high reputation in the SO community. Our ML and DL techniques classify the developers' programming language preferences using their BPT with an average accuracy of 78%.

Md. Saddam Hossain Mukta

Badrun Nessa Antu

Nasreen Azad

Iftekharul Abedeen

Najmul Islam

Analysis of Software Developers' Programming Language Preferences and Community Behavior From Big5 Personality Traits

基于大五人格特质的软件开发者编程语言偏好与社区行为分析

Abstract In contrast to a conventional virtual machine (VM), a container is a lightweight virtualization technology. Containers are becoming a prominent technology for cloud services because of their portable, scalable, and flexible deployments, especially in the Internet of Things (IoT), smart devices, and fog and edge computing. It is a type of operating system‐level virtualization in which the kernel allows multiple isolated containers to run independently. Container placement (CP) is a nontrivial problem in Container‐as‐a‐Service (CaaS). CP is mapping to a container over virtual machines (VMs) to execute an application. Designing an efficient CP strategy is complex due to several intertwined challenges. These challenges arise from a diverse spectrum of computing resources, like on‐demand and unpredictable fluctuations of IT resources by multiple tenants. In this article, we propose a modified sum‐based container placement algorithm called a multi‐objective optimization‐based container placement algorithm (MSBCPA). In the proposed algorithm, we have considered two metrics: makespan and monetary costs for optimizing available IT resources. We have conducted comprehensive simulation experiments to validate the effectiveness of the proposed algorithm over the CloudSim 4.0 simulator. The proposed optimization algorithm (MSBCPA) aims to minimize the makespan and the execution monetary costs simultaneously. In the simulation, we found that the execution cost and energy consumption cost reduce by 20% to 30% and achieve the best possible cost‐makespan trade‐offs compared to competing algorithms.

Md. Akram Khan

Bibhudatta Sahoo

Sambit Kumar Mishra

Achyut Shankar

Multi‐objective based container placement strategy in CaaS

基于多目标的CaaS容器放置策略

Abstract The progress made in the field of medicine and the consequent increase in the prospect of life have contributed to rise people's interest towards a healthier lifestyle. Fitness activity is becoming a must for those who aspire to live more and better. However, this should be accompanied by additional good practices to safeguard individuals' life from risks that could undermine their health. Most of these risks are linked to personal, surrounding, and contextual conditions that technology can detect and monitor. Recommender systems can adequately support fitness activity by performing data analyzes aimed at identifying possible risk factors for users, starting from their physiological data and those related to the closest context where they are. This article introduces the architecture of a recommender system called App4Health in the context related to both mobile crowd sensing and wellness. The App4Health architecture consists of a smart application platform, capable of interfacing and managing data from heterogeneous edge sources, such as mobile phones, IoT, and sensors. The analysis result consists of the semantic generation of healthy behavioral conducts to the user as Telegram BOT messages. For evaluating the proposed solution, the article also provides a case study and a testbed. The testbed consists of a comparative stress test of two edge software components of the App4Health 's architecture in order to identify the performance degradation threshold of these components, assuming that they can be deployed on edge‐level hardware devices with different technical specifications and configurations.

Angelo Martella

Antonella Longo

Marco Zappatore

Beniamino Di Martino

Antonio Esposito

A semantically enabled architecture for interoperable edge‐cloud continuum applied to the e‐health scenario

应用于电子健康场景的语义启用的互操作边缘-云连续体架构

Abstract Federated learning represents a decentralized approach to machine learning, enabling numerous devices to collaboratively contribute to model training while ensuring the privacy of individual data. However, the existing incentive mechanism of hierarchical federated learning (HFL) only considers the data contribution of a single round, which needs to be revised. For non‐IID data sets, the continuous selection of any end devices will cause the weights to diverge in a specific direction. Therefore, a new metric is needed to avoid continuously selecting a certain end device to ensure the overall effectiveness. We introduce a metric to describe the importance of updates: age of update (AoU), which can help select end devices not selected in the previous round to promote a faster model convergence. We put forward an incentive mechanism based on AoU, reputation, and data quantity in HFL (ARDHFL). We have derived the optimal equilibrium solution for the three‐stage Stackelberg game. Based on this solution, we can ensure maximum edge‐cloud utility while incentivizing end devices to engage actively in HFL tasks and providing superior data to train the HFL model. Finally, we conducted extensive experiments to prove that ARDHFL can effectively improve the performance. Compared with the fixed scheme, random scheme, FMore and InFEDge, the testing accuracy of ARDHFL in the MNIST dataset has been improved by 29.7%, 9.3%, 6.8% and 6.1%, respectively. In the CIFAR‐10 dataset, it has been improved by 40.2%, 33.1%, 16.4% and 14.2%, respectively, and demands fewer communication iterations to achieve the same testing accuracy.

Zhaohua Zheng

Yiming Hong

Xin Xie

Keqiu Li

Qiquan Chen

A multi‐dimensional incentive mechanism based on age of update in hierarchical federated learning

基于更新年龄的分层联邦学习多维激励机制

Abstract This article presents an innovative concept that harnesses the capabilities of large language models (LLMs) to revolutionize human‐robot interaction. This work aims to connect large language models with the Robot Operating System (ROS), the primary development framework for robotics applications. We develop a package for ROS that seamlessly integrates ChatGPT with ROS2‐based robotic systems. The core idea is to leverage prompt engineering with LLMs, utilizing unique properties such as ability eliciting, chain‐of‐thought, and instruction tuning. The concept employs ontology development to convert unstructured natural language commands into structured robotic instructions specific to the application context through prompt engineering. We capitalize on LLMs' zero‐shots and few‐shots learning capabilities by eliciting structured robotic commands from unstructured human language inputs. To demonstrate the feasibility of this concept, we implemented a proof‐of‐concept that integrates ChatGPT with ROS2, showcasing the transformation of human language instructions into spatial navigation commands for a ROS2‐enabled robot. Besides, we quantitatively evaluated this transformation over three use cases (ground robot, unmanned aerial vehicle, and Robotic arm) and five LLMs (LLaMA‐7b, LLaMA2‐7b, LLaMA2‐70b, GPT‐3.5, and GPT‐4) on a set of 3000 natural language commands. Our system serves as a new stride towards Artificial General Intelligence (AGI) and paves the way for the robotics and natural language processing communities to collaborate in creating novel, intuitive human‐robot interactions. The open‐source implementation of our system on ROS 2 is available on GitHub.

Anis Koubaa

Adel Ammar

Wadii Boulila

Next‐generation human‐robot interaction with ChatGPT and robot operating system

下一代人机交互与ChatGPT和机器人操作系统

Abstract This article introduces a novel vulnerability measure, based on the structure of complex network communities, to assess the significance and security of network communities, influencing complex network security, connectivity, and the prevention of cascading failures. Initially, the spectral clustering algorithm is applied to identify the communities of complex networks. Determining the appropriate number of communities is crucial in the proposed vulnerability measure and security approach. The number of communities is estimated based on the characteristics of the normalized Laplace matrix within the algorithm. Subsequently, leveraging the community structure, a vulnerability measure is proposed for community evaluation by considering three aspects of internal criteria, external criteria and node location criterion. Weight parameters are also incorporated to customize the measure according to the importance of each factor in varying security scenarios. Finally, the effectiveness of the proposed vulnerability measure as a security strategy is evaluated on ten real‐world complex networks from different categories. The experimental results demonstrate the effectiveness and efficiency of the proposed measure in assessing community vulnerability and consequently using appropriate maps and policies for the complex network security.

Morteza Jouyban

Soodeh Hosseini

A novel vulnerability measure based on complex network communities

基于复杂网络社区的新型脆弱性度量

Abstract The most complicated process in multi‐cloud computing is resource allocation, as it needs to cope with a number of configurations and constraints of cloud providers and customers. At the time of resource allocation, the centralized cloud broker monitors the virtual machines (VM) status, scheduling process, and fitness. However, VM scheduling is found tedious and has received huge attention in business, academia, and research. This enhances the demand for both task scheduling and resource allocation in a multi‐cloud environment. To bridge the gap between the consumer requirement and server infrastructure, a joint optimization‐based resource allocation and task scheduling concept is analyzed in the proposed framework. The first phase introduces the task scheduling mechanism, which uses Hybrid Electro Search and Beetle Swarm Optimization to determine the optimal task for specific VMs. The optimal selection procedure is done by analyzing a multi‐cloud environment's makespan, energy, cost, and throughput parameters. In the second step, an Adaptive Game Theory‐based Seagull optimization approach performs several rounds of reassignment iteratively to minimize the variation in the expected completion time, consequently decreasing high energy consumption and load balancing. The experimental analysis for the proposed model is implemented using Python. The proposed methodology is shown to achieve cheaper costs, shorter waiting times, improved resource allocation, and efficient load balancing. Finally, a comparative analysis is performed with some hybrid optimization models, which illustrate the efficiency of the proposed hybrid optimization model.

Gavini Sreelatha

C. Kishor Kumar Reddy

Marlia Mohd Hanafiah

R. Madana Mohana

Hybrid Electro search beetle optimization based task scheduling and game theory SOA based resource allocation in multi cloud computing

基于混合电搜索甲虫优化的任务调度和基于博弈论的面向服务架构多云计算资源分配

Abstract We present algorithms for generating small random samples without replacement. We consider two cases. We present an algorithm for sampling a pair of distinct integers, and an algorithm for sampling a triple of distinct integers. The worst‐case runtime of both algorithms is constant, while the worst‐case runtimes of common algorithms for the general case of sampling elements from a set of increase with . Java implementations of both algorithms are included in the open source library .

Vincent A. Cicirello

Algorithms for generating small random samples

生成小规模随机样本的算法

SOFTWARE: PRACTICE AND EXPERIENCE 是一种国际上受尊重和严格参考的工具，用于传播和讨论针对系统和应用程序的新的和已建立的软件的实践经验。发表在期刊上的文章必须与软件的设计和实施直接相关从有用的编程技术一直到大型软件系统。正如该期刊的名称所暗示的那样，重点是软件本身的实践和经验。该杂志不能也不会试图涵盖软件工程的所有方面。发表论文的关键标准是它做出的贡献可以使从事软件设计和实施的其他人从中受益。原创性也很重要。但是，对于那些明显众所周知的技术没有出现在现成的文献中的情况，可以例外。定期投稿：提供可以作为“如何做”模型的已完成软件系统项目的详细说明为同一领域的未来工作；提供可用于广泛领域的编程技术的简短报告；记录有助于解决软件构建问题的新技术和工具；解释应对大型企业特殊需求的方法/技术规模的软件项目。然而，软件过程和软件项目的管理被认为超出了期刊的范围。重点始终是实践经验；具有理论或数学内容的文章仅在对理论的理解将导致更好的实践系统的情况下被收录。如果不清楚手稿是否适合在该期刊上发表，参考出版物列表通常会提供强有力的指示.如果没有引用 SOFTWARE: PRACTICE AND EXPERIENCE 论文（或类似范围的期刊中的论文，如 JSS），则该手稿很可能不适合该期刊。此外，可以联系该期刊的一位编辑，就特定主题的适用性提供建议。

Software - Practice and Experience is a highly respected and rigorously peer-reviewed international journal dedicated to the dissemination and discussion of both new and established software practices related to systems and applications. Articles published in the journal must be directly related to the design and implementation of software, covering a range from useful programming techniques to large software systems. As the journal's name suggests, the focus is on the practical experiences of software itself. The journal does not attempt to cover all aspects of software engineering. The key criterion for publication is that the contribution should benefit others engaged in software design and implementation. Originality is important; however, exceptions may be made for well-known techniques that have not been adequately addressed in existing literature. Regular submissions include detailed descriptions of completed software system projects that can serve as 'how-to' models for future work in the same field; brief reports on programming techniques applicable to a wide range of areas; documentation of new technologies and tools that help solve software construction problems; and explanations of methods/techniques for addressing the specific needs of large-scale software projects. However, the management of software processes and projects is considered beyond the scope of the journal. The emphasis is always on practical experience; articles with theoretical or mathematical content are only accepted if the understanding of the theory leads to better practical systems. If there is uncertainty about the suitability of a manuscript for the journal, the reference publication list usually provides strong guidance. If a manuscript does not cite papers from Software - Practice and Experience (or similar scope journals like JSS), it is likely not suitable for the journal. Additionally, authors may contact an editor of the journal for advice on the applicability of specific topics.

《Software - Practice and Experience》是一本国际上备受尊重并经过严格审查的期刊，旨在传播和讨论与系统和应用程序相关的新旧软件实践经验。期刊发表的文章必须直接与软件的设计和实施相关，涵盖从实用的编程技术到大型软件系统的各个方面。正如期刊名称所示，重点在于软件本身的实践和经验。该期刊并不试图涵盖软件工程的所有领域。发表论文的关键标准是其贡献能够使从事软件设计和实施的其他人受益。原创性是重要的，但对于那些在现有文献中未被充分讨论的显而易见技术，亦可考虑发表。定期投稿包括：提供可作为“如何做”模型的已完成软件系统项目的详细说明；简短报告可用于广泛领域的编程技术；记录有助于解决软件构建问题的新技术和工具；解释应对大型企业特殊需求的方法或技术。然而，软件过程和项目管理被认为超出了期刊的范围。始终强调实践经验；具有理论或数学内容的文章仅在其理论理解能够促进更好的实践系统的情况下被接受。如果不确定手稿是否适合该期刊，参考出版物列表通常能提供有力的指引。如果没有引用《Software - Practice and Experience》论文（或类似范围的期刊，如JSS），则该手稿很可能不适合该期刊。此外，作者可以联系期刊编辑以获取特定主题的适用性建议。

Software - Practice and Experience

期刊	Software - Practice and Experience
ISSN	0038-0644, 1097-024X
h-index	77
Journal Impact	2.72
国际分区	COMPUTER SCIENCE, SOFTWARE ENGINEERING(Q2)
国内分区	计算机科学(4区)计算机科学计算机软件工程(4区)