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MicroFI: Non-Intrusive and Prioritized Request-Level Fault Injection for Microservice Applications

IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2024年第5期21卷 4921-4938页

作者： Chen, Hongyang Chen, Pengfei Yu, Guangba Li, Xiaoyun He, Zilong Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China

Microservice is a widely-adopted architecture for constructing cloud-native applications. To test application resiliency, chaos engineering is widely used to inject faults proactively in applications. However, the searching space formed by possible injection locations is huge due to the scale and complexity of the application. Although some methods are proposed to effectively explore injection space, they cannot prioritize high-impact injection solutions. Additionally, the blast radius of faults injected by existing methods is typically full of uncertainty, causing faults of multiple application functions. Although some tools are designed to conduct request-level injection, they require instrumentation on application code. To tackle these problems, this paper presents MicroFI, a non-intrusive fault injection framework, aiming to efficiently test different application functions with request-level injection. Request-level injection limits the blast radius to specified requests without any source code modification. Additionally, MicroFI leverages historical injection results and parallel technique to accelerate the searching. Moreover, An enhanced PageRank is used to measure the impact of faults and prioritize high-impact faults that fail more functions. Evaluations on three microservice applications show that MicroFI precisely injects faults and reduces up to 91% redundant faults on average. Additionally, by employing prioritization, MicroFI reduces an average of 47.3% injection budgets to cover all high-impact faults.

关键词： Microservice architectures Computer crashes Space exploration source coding Instruments Aerospace electronics Resilience Microservice fault injection chaos engineering service mesh tracing

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Speed and Conversational Large Language Models: Not All Is About Tokens per Second

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COMPUTER 2024年第8期57卷 74-80页

作者： Conde, Javier Gonzalez, Miguel Reviriego, Pedro Gao, Zhen Liu, Shanshan Lombardi, Fabrizio Univ Politecn Madrid ETSI Telecomunicac Madrid 28040 Spain Tianjin Univ Tianjin 300072 Peoples R China Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China Northeastern Univ Boston MA 02115 USA

The speed of open-weights large language models (LLMs) and its dependency on the task at hand, when run on GPUs, is studied to present a comparative analysis of the speed of the most popular open LLMs.

关键词： Large Language Models Chatbots Artificial Intelligence User Interfaces Business Open source Software source coding Performance Evaluation Costs Investment Modeling Large Language Models Benchmark Application Programming Interface Speed Rate Wide Range Of Topics Computing Units Speed Of The Model User Agreement Paraphrase Multiple Choice Questions GB Memory Results Of Task Input Text Text Generation Input Tokens Tokenized

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Multi-Label Confusion Tensor

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IEEE ACCESS 2024年 12卷 9860-9870页

作者： Krstinic, Damir Skelin, Ana Kuzmanic Slapnicar, Ivan Braovic, Maja Univ Split Fac Elect Engn Mech Engn & Naval Architecture Dept Modelling & Intelligent Syst Split 21000 Croatia Univ Split Fac Elect Engn Mech Engn & Naval Architecture Dept Control & Syst Split 21000 Croatia Univ Split Fac Elect Engn Mech Engn & Naval Architecture Dept Math & Phys Split 21000 Croatia

The confusion matrix is the tool commonly used for the evaluation of the performance of a classification algorithm. While the computation of the confusion matrix for multi-class classification follows a well-developed procedure, the common approach for computing the confusion matrix for multi-label classification suffers from the ambiguity related to one-vs-rest strategy and ignores the possibility that predictions could be partially correct, which also leads to inaccuracies of the derived evaluation metric. Only recently, the two approaches dealing with the computation of multi-label confusion matrix have been proposed which take into account the specifics of multi-label classification. In this work, a new method for calculating evaluation metrics for multi-label classification is proposed. The proposed method is based on the calculation of two confusion matrices combined into the confusion tensor. It builds upon the insights into the shortcomings of the two existing approaches for calculating the multi-label confusion matrix. The main drawback of these techniques is their inability to compute precision and recall precisely. The Multi-Label Confusion Tensor was tested on synthetic and real data and compared with existing methods for calculating the multi-label confusion matrix. The source code and the data used to test the methodology are made publicly available.

关键词： Measurement Tensors Task analysis source coding Mechanical engineering Electrical engineering Computer architecture Multi-label classification confusion matrix classification performance machine learning

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Conceptual Framework for Software Change

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IT PROFESSIONAL 2024年第2期26卷 57-66页

作者： Saied, Mohamed Elfatatry, Ahmed Mohammed Guirguis, Shawkat Kamal Alexandria Univ Inst Grad Studies & Res Dept Informat Technol Alexandria 21526 Egypt Alexandria Univ Inst Grad Studies & Res software Engn field Alexandria 21526 Egypt Alexandria Univ Inst Grad Studies & Res Dept Informat Technol software Engn Alexandria 21526 Egypt Alexandria Univ Inst Grad Studies & Res Dept Informat Technol Comp Sci & informat Alexandria 21526 Egypt

Ever since the invention of software, change has been a destabilizing factor. Although many new software changes are being applied, the terminologies used to describe them are often inconsistent. This restricts practitioners to designing and evaluating their changes. This article aims to develop a conceptual framework of software change based on six main dimensions regarding the source, essence, and consequences of software change. To evaluate the proposed framework, benchmarking is applied against selected 11 previous studies.

关键词： Software systems Codes Cloud computing Software engineering Computer architecture source coding Software maintenance Change detection algorithms Software development management

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Emerging Trends in Code Quality: Introducing Kotlin-Specific Bad Smell Detection Tool for Android Apps

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IEEE ACCESS 2024年 12卷 63895-63903页

作者： Novendra, Radinal Dwiki Sunindyo, Wikan Danar Bandung Inst Technol Sch Elect Engn & Informat Bandung 40132 West Java Indonesia

The increasing demand for Android applications in line with technological evolution and the development of new features often leads to frequent updates and releases of applications. However, in this update cycle, developers sometimes make hasty changes that result in poor design choices, leading to the emergence of bad smell code. The impact of smells involves a decrease in quality, performance, and understanding, and impedes the software maintenance process. Some previously developed smell detection tools rely on JavaParser to parse source code into an Abstract Syntax Tree representation, meaning the extracted information is limited to Java programming language source code. Meanwhile, Google recommends Kotlin as the language for developing Android applications, and over 60% of professional Android developers use Kotlin. This research is using Program Structure Interface representation and applies a software metric-based approach as a method of smell detection and implements it into a Kotlin Android bad smell code detection tool. This approach was chosen because the characteristics of each smell can be represented with software metrics. The tool's evaluation results on five Kotlin Android projects showed an F-measure of 100% for Brain Class detection, 93.77% for God Class, and 85.71% for Brain Method. Comparing the tool's detection results with the iPlasma tool on the Quran project showed that the developed tool detected more smell occurrences. Usability testing results indicated that seven participants strongly agreed with the easy-to-understand detection results, easy-to-find presented functionality, clearly presented information, and the tool helped to perform metric calculation and smell detection more quickly and efficiently. The average rating given for each question was between 4.29 - 4.71.

关键词： Codes Operating systems Java Software Complexity theory Syntactics source coding Androids Detection algorithms Olfactory Code smells android Kotlin

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Optimizing Pre-Trained Code Embeddings With Triplet Loss for Code Smell Detection

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IEEE ACCESS 2025年 13卷 31335-31350页

作者： Nizam, Ali Islamoglu, Ertugrul Kerem Adali, Omer Aydin, Musa Fatih Sultan Mehmet Vakif Univ Software Engn Dept TR-34445 Istanbul Turkiye Fatih Sultan Mehmet Vakif Univ Artificial Intelligence & Data Engn Dept TR-34445 Istanbul Turkiye Fatih Sultan Mehmet Vakif Univ Comp Engn Dept TR-34445 Istanbul Turkiye

Code embedding represents code semantics in vector form. Although code embedding-based systems have been successfully applied to various source code analysis tasks, further research is required to enhance code embedding for better code analysis capabilities, aiming to surpass the performance and functionality of static code analysis tools. In addition, standard methods for improving code embedding are essential to develop more effective embedding-based systems, similar to augmentation techniques in the image processing domain. This study aims to create a contrastive learning-based system to explore the potential of a generic method for enhancing code embedding for code classification tasks. A triplet loss-based deep learning network is designed to optimize in-class similarity and increase the distance between classes. An experimental dataset that contains code from Java, Python, and PHP programming languages and 4 different code smells is created by collecting code from open-source repositories on GitHub. We evaluate the proposed system's effectiveness with widely used BERT, CodeBERT, and GraphCodeBERT pretrained models to create code embedding for the code classification task of code smell detection. Our findings indicate that the proposed system may offer improvements in accuracy, an average of 8% and a maximum of 13% for models. These results suggest that incorporating contrastive learning techniques into the generation process of code representation as a preprocessing step can enhance performance in code analysis.

关键词： Codes source coding Contrastive learning Semantics Vectors Transformers Measurement Analytical models Syntactics Software Code embedding contrastive learning triplet loss code smell detection

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Universal Polarization for Processes With Memory

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IEEE TRANSACTIONS ON INFORMATION THEORY 2025年第3期71卷 1705-1757页

作者： Shuval, Boaz Tal, Ido Technion Israel Inst Technol Viterbi Dept Elect & Comp Engn IL-320003 H_efa Israel

A transform that is universally polarizing over a set of channels with memory is presented. Memory may be present in both the input to the channel and the channel itself. Both the encoder and the decoder are aware of the input distribution, which is fixed. However, only the decoder is aware of the actual channel being used. The transform can be used to design a universal code for this scenario. The code is to have vanishing error probability when used over any channel in the set, and achieve the infimal information rate over the set. The setting considered is, in fact, more general: we consider a set of processes with memory. Universal polarization is established for the case where each process in the set: 1) has memory in the form of an underlying hidden Markov state sequence that is aperiodic and irreducible;and 2) satisfies a 'forgetfulness' property. Forgetfulness, which we believe to be of independent interest, occurs when two hidden Markov states become approximately independent of each other given a sufficiently long sequence of observations between them. We show that aperiodicity and irreducibility of the underlying Markov chain is not sufficient for forgetfulness, and develop a sufficient condition for a hidden Markov process to be forgetful.

关键词： Decoding Polar codes Codes Hidden Markov models Transforms Error probability Encoding Information rates source coding Upper bound universal polarization universal codes channels with memory hidden Markov processes

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A Retrospective on Mining Version Histories to Guide Software Changes

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2025年第3期51卷 842-847页

作者： Zimmermann, Thomas Weissgerber, Peter Diehl, Stephan Zeller, Andreas Univ Calif Irvine Irvine CA 92697 USA Amazon Payments Europe SCA L-1855 Luxembourg Luxembourg Univ Trier Software Engn D-54296 Trier Germany CISPA Helmholtz Ctr Informat Secur D-66123 Saarbrucken Germany

Twenty years ago we published a paper titled "Mining Version Histories to Guide Software Changes" in the IEEE Transactions of Software Engineering. The paper is considered to be one of the seminal papers of the mining software repositories (MSR) field. In this retrospective, we reflect on the original work, the field of mining software repositories and its community, and its impact on software engineering.

关键词： Software Data mining History Software engineering Control systems Software development management Conferences source coding Navigation Codes Mining software repositories recommendation system retrospective

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A Generative AI-Driven Method-Level Semantic Clone Detection Based on the Structural and Semantical Comparison of Methods

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IEEE ACCESS 2024年 12卷 70773-70791页

作者： Gupta, Aditi Goyal, Rinkaj Guru Gobind Singh GGS Indraprastha Univ Univ Sch Informat Commun & Technol New Delhi 110078 India

Code fragments with identical or similar functionality are called code clones. This study aims to detect semantic clones in Java-based programs, focusing on the method-level granularity. To accomplish this, our approach extracts and compares both semantical and structural features of Java methods and applies certain heuristics to obtain the final result set. The semantics of a method are often described by its documentation, while its structural details are characterized by its implementation within the code body. However, Java methods may not always be accompanied by descriptive documentation. To address this, we have employed a Generative AI tool named ChatGPT, which has the ability to understand the given code and generate its documentation. To compare the documentation of methods, we have used various corpus-based and knowledge-based information retrieval (IR) techniques. However, for comparing a method's structural details, we tokenized the method's body and applied an information retrieval technique called VSM (Vector Space Modelling) on the tokenized body. Additionally, we also extracted and compared certain method-level metrics for this purpose. Further, while doing the semantical comparison of methods, eight IR variants are formed based on the internal processing requirements of different IR techniques. The technique proposed in this paper relies on the textual and metrics-based analysis of Java programs with few parsing requirements, making it lightweight and less computation-intensive. To examine the efficiency of the proposed technique, we have validated it using a semantic clone benchmark. The results show that the proposed technique detects semantic clones with high recall values ranging from 67% to 81% and precision values ranging from 60% to 96% for different IR variants explored in our research.

关键词： Cloning Semantics Codes Documentation Large scale integration Measurement source coding Information retrieval Generative AI Java Clone detection semantic clones information retrieval metrics documentation

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A Structured coding Framework for Communication and Computation Over Continuous Networks

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第3期70卷 1629-1651页

作者： Shirani, Farhad Pradhan, S. Sandeep Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA Univ Michigan Elect Engn & Comp Sci Dept Ann Arbor MI 48109 USA

This work considers an information-theoretic characterization of the set of achievable rates, costs, and distortions in a broad class of distributed communication and function computation scenarios with general continuous-valued sources and channels. A framework is presented which involves fine discretization of the source and channel variables followed by communication over the resulting discretized network. In order to evaluate the resulting achievable regions, convergence results for information measures are provided under the proposed discretization process. Prior works have considered such convergence results for mutual information quantities written in terms of univariate functions of random variables, and sums of independent random variables. These convergence results have been used to derive achievable regions in point-to-point communication scenarios and specific multiterminal scenarios with continuous alphabets. However, the best-known achievability results for distributed communication and function computation scenarios, which are based on structured coding strategies, involve mutual information quantities written in terms of bivariate functions of random variables, e.g., sum of two (not necessarily independent) random variables. A main contribution of this work is to show the convergence of mutual information quantities written in terms of sums of quantized random variables. This is an essential step in evaluating the achievable regions in continuous distributed computation scenarios by generalizing the structured coding strategies which have been previously used to derive the best-known achievable regions in discrete networks. The framework is used to provide achievability results for the problems of function computation over multiple-access channels, distributed source coding, function reconstruction (two-help-one), and multiple-descriptions source coding. In each scenario, discrete structured coding strategies along with the aforementioned convergence

关键词： Data communication information theory rate distortion theory channel capacity channel coding source coding

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