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Boundary State Generation for Testing and Improvement of Autonomous Driving Systems IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-07-01 Matteo Biagiola, Paolo Tonella
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A Scalable t-wise Coverage Estimator: Algorithms and Applications IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-06-27 Eduard Baranov, Sourav Chakraborty, Axel Legay, Kuldeep S. Meel, N. Variyam Vinodchandran
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Optimization of Automated and Manual Software Tests in Industrial Practice: A Survey and Historical Analysis IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-06-24 Roman Haas, Raphael Nömmer, Elmar Juergens, Sven Apel
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A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-06-22 Arturo Montejo-Ráez, M. Dolores Molina-González, Salud María Jiménez-Zafra, Miguel Ángel García-Cumbreras, Luis Joaquín García-López
For years, the scientific community has researched monitoring approaches for the detection of certain mental disorders and risky behaviors, like depression, eating disorders, gambling, and suicidal ideation among others, in order to activate prevention or mitigation strategies and, in severe cases, clinical treatment. Natural Language Processing is one of the most active disciplines dealing with the
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A Closest Point Method for PDEs on Manifolds with Interior Boundary Conditions for Geometry Processing ACM Trans. Graph. (IF 7.8) Pub Date : 2024-06-17 Nathan King, Haozhe Su, Mridul Aanjaneya, Steven Ruuth, Christopher Batty
Many geometry processing techniques require the solution of partial differential equations (PDEs) on manifolds embedded in \(\mathbb {R}^2 \) or \(\mathbb {R}^3 \), such as curves or surfaces. Such manifold PDEs often involve boundary conditions (e.g., Dirichlet or Neumann) prescribed at points or curves on the manifold’s interior or along the geometric (exterior) boundary of an open manifold. However
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A comprehensive review on transformer network for natural and medical image analysis Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-06-14 Ramkumar Thirunavukarasu, Evans Kotei
The Transformer network is the main application area for natural language processing. It has gained traction lately and exhibits potential in the field of computer vision. This cutting-edge method has proven to offer a significant impact on image analysis, a crucial area of computer vision. The transformer's outstanding performance in vision computing places it as an alternative to the convolutional
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Auto-scaling mechanisms in serverless computing: A comprehensive review Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-06-13 Mohammad Tari, Mostafa Ghobaei-Arani, Jafar Pouramini, Mohsen Ghorbian
The auto-scaling feature is fundamental to serverless computing, and it automatically allows applications to scale as needed. Hence, this allows applications to be configured to adapt to current traffic and demands and acquire resources as necessary without the need to manage servers directly. Auto-scaling is an important principle in developing serverless applications that is considered and increasingly
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Chaos Game Optimization: A comprehensive study of its variants, applications, and future directions Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-06-07 Raja Oueslati, Ghaith Manita, Amit Chhabra, Ouajdi Korbaa
Chaos Game Optimization Algorithm (CGO) is a novel advancement in metaheuristic optimization inspired by chaos theory. It addresses complex optimization problems in dynamical systems, exhibiting unique behaviours such as fractals and self-organized patterns. CGO’s design exemplifies adaptability and robustness, making it a significant tool for tackling intricate optimization scenarios. This study presents
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Backbones-review: Feature extractor networks for deep learning and deep reinforcement learning approaches in computer vision Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-06-07 Omar Elharrouss, Younes Akbari, Noor Almadeed, Somaya Al-Maadeed
To understand the real world using various types of data, Artificial Intelligence (AI) is the most used technique nowadays. While finding the pattern within the analyzed data represents the main task. This is performed by extracting representative features step, which is proceeded using the statistical algorithms or using some specific filters. However, the selection of useful features from large-scale
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Deep learning with the generative models for recommender systems: A survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-06-04 Ravi Nahta, Ganpat Singh Chauhan, Yogesh Kumar Meena, Dinesh Gopalani
The variety of enormous information on the web encourages the field of recommender systems (RS) to flourish. In recent times, deep learning techniques have significantly impacted information retrieval tasks, including RS. The probabilistic and non-linear views of neural networks emerge to generative models for recommendation tasks. At present, there is an absence of extensive survey on deep generative
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DDoS attacks & defense mechanisms in SDN-enabled cloud: Taxonomy, review and research challenges Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-06-04 Jasmeen Kaur Chahal, Abhinav Bhandari, Sunny Behal
Software-defined Networking (SDN) is a transformative approach for addressing the limitations of legacy networks due to decoupling of control planes from data planes. It offers increased programmability and flexibility for designing of cloud-based data centers. SDN-Enabled cloud data centers help in managing the huge traffic very effectively and efficiently. However, the security of SDN-Enabled Cloud
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Reducing the Length of Field-replay Based Load Testing IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-05-31 Yuanjie Xia, Lizhi Liao, Jinfu Chen, Heng Li, Weiyi Shang
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GenMorph: Automatically Generating Metamorphic Relations via Genetic Programming IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-05-31 Jon Ayerdi, Valerio Terragni, Gunel Jahangirova, Aitor Arrieta, Paolo Tonella
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Evaluating SZZ Implementations: An Empirical Study on the Linux Kernel IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-05-29 Yunbo Lyu, Hong Jin Kang, Ratnadira Widyasari, Julia Lawall, David Lo
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Analytic rotation-invariant modelling of anisotropic finite elements ACM Trans. Graph. (IF 7.8) Pub Date : 2024-05-28 Huancheng Lin, Floyd Mulenga Chitalu, Taku Komura
Anisotropic hyperelastic distortion energies are used to solve many problems in fields like computer graphics and engineering with applications in shape analysis, deformation, design, mesh parameterization, biomechanics and more. However, formulating a robust anisotropic energy that is low-order and yet sufficiently non-linear remains a challenging problem for achieving the convergence promised by
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A Framework for Solving Parabolic Partial Differential Equations on Discrete Domains ACM Trans. Graph. (IF 7.8) Pub Date : 2024-05-28 Leticia Mattos Da Silva, Oded Stein, Justin Solomon
We introduce a framework for solving a class of parabolic partial differential equations on triangle mesh surfaces, including the Hamilton-Jacobi equation and the Fokker-Planck equation. PDE in this class often have nonlinear or stiff terms that cannot be resolved with standard methods on curved triangle meshes. To address this challenge, we leverage a splitting integrator combined with a convex optimization
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Cross-Language Taint Analysis: Generating Caller-Sensitive Native Code Specification for Java IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-05-27 Shuangxiang Kan, Yuhao Gao, Zexin Zhong, Yulei Sui
Cross-language programming is a common practice within the software development industry, offering developers a multitude of advantages such as expressiveness, interoperability, and cross-platform compatibility, for developing large-scale applications. As an important example, JNI (Java Native Interface) programming is widely used in diverse scenarios where Java interacts with code written in other
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More than a framework: Sketching out technical enablers for natural language-based source code generation Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-05-25 Chen Yang, Yan Liu, Changqing Yin
Natural Language-based Source Code Generation (NLSCG) holds the promise to revolutionize the way how software is developed by means of facilitating a collection of intelligent technical enablers, based on sustained improvements on the natural language to source code pipelines and continuous adoption of new coding paradigms. In recent years, a large variety of NLSCG technical solutions have been proposed
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Just-In-Time TODO-Missed Commits Detection IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-05-24 Haoye Wang, Zhipeng Gao, Xing Hu, David Lo, John Grundy, Xinyu Wang
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Differentiable solver for time-dependent deformation problems with contact ACM Trans. Graph. (IF 7.8) Pub Date : 2024-05-22 Zizhou Huang, Davi Colli Tozoni, Arvi Gjoka, Zachary Ferguson, Teseo Schneider, Daniele Panozzo, Denis Zorin
We introduce a general differentiable solver for time-dependent deformation problems with contact and friction. Our approach uses a finite element discretization with a high-order time integrator coupled with the recently proposed incremental potential contact method for handling contact and friction forces to solve ODE- and PDE-constrained optimization problems on scenes with complex geometry. It
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View-Independent Adjoint Light Tracing for Lighting Design Optimization ACM Trans. Graph. (IF 7.8) Pub Date : 2024-05-22 Lukas Lipp, David Hahn, Pierre Ecormier-Nocca, Florian Rist, Michael Wimmer
Differentiable rendering methods promise the ability to optimize various parameters of three-dimensional (3D) scenes to achieve a desired result. However, lighting design has so far received little attention in this field. In this article, we introduce a method that enables continuous optimization of the arrangement of luminaires in a 3D scene via differentiable light tracing. Our experiments show
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A Lean Simulation Framework for Stress Testing IoT Cloud Systems IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-05-21 Jia Li, Behrad Moeini, Shiva Nejati, Mehrdad Sabetzadeh, Michael McCallen
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Fusing Code Searchers IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-05-20 Shangwen Wang, Mingyang Geng, Bo Lin, Zhensu Sun, Ming Wen, Yepang Liu, Li Li, Tegawendé F. Bissyandé, Xiaoguang Mao
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MR2-KG: A multi-relation multi-rationale knowledge graph for modeling software engineering knowledge on Stack Overflow IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-05-20 Lina Gong, Haoxiang Zhang
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ContractCheck: Checking Ethereum Smart Contracts in Fine-Grained Level IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-05-15 Xite Wang, Senping Tian, Wei Cui
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Plug-and-Play Algorithms for Dynamic Non-line-of-sight Imaging ACM Trans. Graph. (IF 7.8) Pub Date : 2024-05-14 Juntian Ye, Yu Hong, Xiongfei Su, Xin Yuan, Feihu Xu
Non-line-of-sight (NLOS) imaging has the ability to recover 3D images of scenes outside the direct line of sight, which is of growing interest for diverse applications. Despite the remarkable progress, NLOS imaging of dynamic objects is still challenging. It requires a large amount of multibounce photons for the reconstruction of single frame data. To overcome this obstacle, we develop a computational
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A comprehensive review on applications of Raspberry Pi Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-05-14 Sudha Ellison Mathe, Hari Kishan Kondaveeti, Suseela Vappangi, Sunny Dayal Vanambathina, Nandeesh Kumar Kumaravelu
Raspberry Pi is an invaluable and popular prototyping tool in scientific research for experimenting with a wide variety of ideas, ranging from simple to complex projects. This review article explores how Raspberry Pi is used in various studies, discussing its pros and cons along with its applications in various domains such as home automation, agriculture, healthcare, industrial control, and advanced
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SQLPsdem: A Proxy-based Mechanism towards Detecting, Locating and Preventing Second-Order SQL Injections IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-05-14 Bing Zhang, Rong Ren, Jia Liu, Mingcai Jiang, Jiadong Ren, Jingyue Li
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Isolating Compiler Bugs by Generating Effective Witness Programs with Large Language Models IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-05-07 Haoxin Tu, Zhide Zhou, He Jiang, Imam Nur Bani Yusuf, Yuxian Li, Lingxiao Jiang
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Darcy: Automatic Architectural Inconsistency Resolution in Java IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-05-03 Negar Ghorbani, Tarandeep Singh, Joshua Garcia, Sam Malek
Many mainstream programming languages lack extensive support for architectural constructs, such as software components, which limits software developers in employing many benefits of architecture-based development. To address this issue, Java, one of the most popular and widely-used programming languages, has introduced the Java Platform Module System (JPMS) in its 9th and subsequent versions. JPMS
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I❤MESH: A DSL for Mesh Processing ACM Trans. Graph. (IF 7.8) Pub Date : 2024-05-01 Yong Li, Shoaib Kamil, Keenan Crane, Alec Jacobson, Yotam Gingold
Mesh processing algorithms are often communicated via concise mathematical notation (e.g., summation over mesh neighborhoods). However, conversion of notation into working code remains a time consuming and error-prone process which requires arcane knowledge of low-level data structures and libraries—impeding rapid exploration of high-level algorithms. We address this problem by introducing a domain-specific
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Automated Infrastructure as Code Program Testing IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-05-01 Daniel Sokolowski, David Spielmann, Guido Salvaneschi
Infrastructure as Code (IaC) enables efficient deployment and operation, which are crucial to releasing software quickly. As setups can be complex, developers implement IaC programs in general-purpose programming languages like TypeScript and Python, using PL-IaC solutions like Pulumi and AWS CDK. The reliability of such IaC programs is even more relevant than in traditional software because a bug
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How do Developers Adapt Code Snippets to Their Contexts? An Empirical Study of Context-Based Code Snippet Adaptations IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-30 Tanghaoran Zhang, Yao Lu, Yue Yu, Xinjun Mao, Yang Zhang, Yuxin Zhao
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CRPWarner: Warning the Risk of Contract-Related Rug Pull in DeFi Smart Contracts IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-30 Zewei Lin, Jiachi Chen, Jiajing Wu, Weizhe Zhang, Yongjuan Wang, Zibin Zheng
In recent years, Decentralized Finance (DeFi) has grown rapidly due to the development of blockchain technology and smart contracts. As of March 2023, the estimated global cryptocurrency market cap has reached approximately $949 billion. However, security incidents continue to plague the DeFi ecosystem, and one of the most notorious examples is the “Rug Pull” scam. This type of cryptocurrency scam
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Concretely Mapped Symbolic Memory Locations for Memory Error Detection IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-30 Haoxin Tu, Lingxiao Jiang, Jiaqi Hong, Xuhua Ding, He Jiang
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Evaluating gesture generation in a large-scale open challenge: The GENEA Challenge 2022 ACM Trans. Graph. (IF 7.8) Pub Date : 2024-04-27 Taras Kucherenko, Pieter Wolfert, Youngwoo Yoon, Carla Viegas, Teodor Nikolov, Mihail Tsakov, Gustav Eje Henter
This paper reports on the second GENEA Challenge to benchmark data-driven automatic co-speech gesture generation. Participating teams used the same speech and motion dataset to build gesture-generation systems. Motion generated by all these systems was rendered to video using a standardised visualisation pipeline and evaluated in several large, crowdsourced user studies. Unlike when comparing different
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A survey on modeling for behaviors of complex intelligent systems based on generative adversarial networks Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-04-27 Yali Lv, Jingpu Duan, Xiong Li
This paper provides an extensive and in-depth survey of behavior modeling for complex intelligent systems, focusing specifically on the innovative applications of Generative Adversarial Networks (GANs). The survey not only delves into the fundamental principles of GANs, but also elucidates their pivotal role in accurately modeling the behaviors exhibited by complex intelligent systems. By categorizing
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Harnessing Heterogeneous Information Networks: A systematic literature review Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-04-27 Leila Outemzabet, Nicolas Gaud, Aurélie Bertaux, Christophe Nicolle, Stéphane Gerart, Sébastien Vachenc
The integration of multiple heterogeneous data into graph models has been the subject of extensive research in recent years. Harnessing these resulting Heterogeneous Information Networks (HINs) is a complex task that requires reasoning to perform various prediction tasks.
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VarGAN: Adversarial Learning of Variable Semantic Representations IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-25 Yalan Lin, Chengcheng Wan, Shuwen Bai, Xiaodong Gu
Variable names are of critical importance in code representation learning. However, due to diverse naming conventions, variables often receive arbitrary names, leading to long-tail, out-of-vocabulary (OOV), and other well-known problems. While the Byte-Pair Encoding (BPE) tokenizer has addressed the surface-level recognition of low-frequency tokens, it has not noticed the inadequate training of low-frequency
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TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree Transformation IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-25 Zixiang Xian, Rubing Huang, Dave Towey, Chunrong Fang, Zhenyu Chen
Artificial intelligence (AI) has revolutionized software engineering (SE) by enhancing software development efficiency. The advent of pre-trained models (PTMs) leveraging transfer learning has significantly advanced AI for SE. However, existing PTMs that operate on individual code tokens suffer from several limitations: They are costly to train and fine-tune; and they rely heavily on labeled data for
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Neural Library Recommendation by Embedding Project-Library Knowledge Graph IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-24 Bo Li, Haowei Quan, Jiawei Wang, Pei Liu, Haipeng Cai, Yuan Miao, Yun Yang, Li Li
The prosperity of software applications brings fierce market competition to developers. Employing third-party libraries (TPLs) to add new features to projects under development and to reduce the time to market has become a popular way in the community. However, given the tremendous TPLs ready for use, it is challenging for developers to effectively and efficiently identify the most suitable TPLs. To
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Twenty-two years since revealing cross-site scripting attacks: A systematic mapping and a comprehensive survey Comput. Sci. Rev. (IF 13.3) Pub Date : 2024-04-23 Abdelhakim Hannousse, Salima Yahiouche, Mohamed Cherif Nait-Hamoud
Cross-site scripting (XSS) is one of the major threats menacing the privacy of data and the navigation of trusted web applications. Since its disclosure in late 1999 by Microsoft security engineers, several techniques have been developed with the aim of securing web navigation and protecting web applications against XSS attacks. XSS has been and is still in the top 10 list of web vulnerabilities reported
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No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPT IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-23 Zhijie Liu, Yutian Tang, Xiapu Luo, Yuming Zhou, Liang Feng Zhang
Large language models (LLMs) have demonstrated impressive capabilities across various natural language processing (NLP) tasks, such as machine translation, question answering, summarization, and so on. Additionally, LLMs are also highly valuable in supporting software engineering tasks, particularly in the field of code generation. Automatic code generation is a process of automatically generating
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Clopper-Pearson Algorithms for Efficient Statistical Model Checking Estimation IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-23 Hao Bu, Meng Sun
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Real-Time Neural Appearance Models ACM Trans. Graph. (IF 7.8) Pub Date : 2024-04-20 Tizian Zeltner, Fabrice Rousselle, Andrea Weidlich, Petrik Clarberg, Jan Novák, Benedikt Bitterli, Alex Evans, Tomáš Davidovič, Simon Kallweit, Aaron Lefohn
We present a complete system for real-time rendering of scenes with complex appearance previously reserved for offline use. This is achieved with a combination of algorithmic and system level innovations. Our appearance model utilizes learned hierarchical textures that are interpreted using neural decoders, which produce reflectance values and importance-sampled directions. To best utilize the modeling
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A Platform-Agnostic Framework for Automatically Identifying Performance Issue Reports with Heuristic Linguistic Patterns IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-17 Yutong Zhao, Lu Xiao, Sunny Wong
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ConceptLab: Creative Concept Generation using VLM-Guided Diffusion Prior Constraints ACM Trans. Graph. (IF 7.8) Pub Date : 2024-04-16 Elad Richardson, Kfir Goldberg, Yuval Alaluf, Daniel Cohen-Or
Recent text-to-image generative models have enabled us to transform our words into vibrant, captivating imagery. The surge of personalization techniques that has followed has also allowed us to imagine unique concepts in new scenes. However, an intriguing question remains: How can we generate a new, imaginary concept that has never been seen before? In this paper, we present the task of creative text-to-image
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Pretrain, Prompt, and Transfer: Evolving Digital Twins for Time-to-Event Analysis in Cyber-Physical Systems IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-15 Qinghua Xu, Tao Yue, Shaukat Ali, Maite Arratibel
Cyber-physicalnd systems (CPSs), e.g., elevators and autonomous driving systems, are progressively permeating our everyday lives. To ensure their safety, various analyses need to be conducted, such as anomaly detection and time-to-event analysis (the focus of this paper). Recently, it has been widely accepted that digital Twins (DTs) can be an efficient method to aid in developing, maintaining, and
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MMO: Meta Multi-Objectivization for Software Configuration Tuning IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-15 Pengzhou Chen, Tao Chen, Miqing Li
Software configuration tuning is essential for optimizing a given performance objective (e.g., minimizing latency). Yet, due to the software's intrinsically complex configuration landscape and expensive measurement, there has been a rather mild success, particularly in preventing the search from being trapped in local optima. To address this issue, in this paper we take a different perspective. Instead
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Generic Sensitivity: Generics-Guided Context Sensitivity for Pointer Analysis IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-12 Haofeng Li, Tian Tan, Yue Li, Jie Lu, Haining Meng, Liqing Cao, Yongheng Huang, Lian Li, Lin Gao, Peng Di, Liang Lin, ChenXi Cui
Generic programming has found widespread application in object-oriented languages like Java. However, existing context-sensitive pointer analyses fail to leverage the benefits of generic programming. This paper introduces generic sensitivity , a new context customization scheme targeting generics. We design our context customization scheme in such a way that generic instantiation sites, i.e., locations
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LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Types IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-11 Xin-Cheng Wen, Cuiyun Gao, Feng Luo, Haoyu Wang, Ge Li, Qing Liao
Prior studies generally focus on software vulnerability detection and have demonstrated the effectiveness of Graph Neural Network (GNN)-based approaches for the task. Considering the various types of software vulnerabilities and the associated different degrees of severity, it is also beneficial to determine the type of each vulnerable code for developers. In this paper, we observe that the distribution
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Characterizing Timeout Builds in Continuous Integration IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-11 Nimmi Weeraddana, Mahmoud Alfadel, Shane McIntosh
Compute resources that enable Continuous Integration (CI, i.e., the automatic build and test cycle applied to the change sets that development teams produce) are a shared commodity that organizations need to manage. To prevent (erroneous) builds from consuming a large amount of resources, CI service providers often impose a time limit. CI builds that exceed the time limit are automatically terminated
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Domain-Driven Design for Microservices: An Evidence-Based Investigation IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-10 Chenxing Zhong, Shanshan Li, Huang Huang, Xiaodong Liu, Zhikun Chen, Yi Zhang, He Zhang
MicroService Architecture (MSA), a predominant architectural style in recent years, still faces the arduous task of identifying the boundaries of microservices. Domain-Driven Design (DDD) is regarded as one of the major design methods for addressing this task in practice, which aims to iteratively build domain models using a series of patterns, principles, and practices. The adoption of DDD for MSA
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Controller Synthesis for Autonomous Systems With Deep-Learning Perception Components IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-10 Radu Calinescu, Calum Imrie, Ravi Mangal, Genaína Nunes Rodrigues, Corina Păsăreanu, Misael Alpizar Santana, Gricel Vázquez
We present DeepDECS, a new method for the synthesis of correct-by-construction software controllers for autonomous systems that use deep neural network (DNN) classifiers for the perception step of their decision-making processes. Despite major advances in deep learning in recent years, providing safety guarantees for these systems remains very challenging. Our controller synthesis method addresses
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Importance Sampling BRDF Derivatives ACM Trans. Graph. (IF 7.8) Pub Date : 2024-04-09 Yash Belhe, Bing Xu, Sai Praveen Bangaru, Ravi Ramamoorthi, Tzu-Mao Li
We propose a set of techniques to efficiently importance sample the derivatives of a wide range of Bidirectional Reflectance Distribution Function (BRDF) models. In differentiable rendering, BRDFs are replaced by their differential BRDF counterparts, which are real-valued and can have negative values. This leads to a new source of variance arising from their change in sign. Real-valued functions cannot
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Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians ACM Trans. Graph. (IF 7.8) Pub Date : 2024-04-09 Jiawei Huang, Akito Iizuka, Hajime Tanaka, Taku Komura, Yoshifumi Kitamura
Importance sampling techniques significantly reduce variance in physically based rendering. In this article, we propose a novel online framework to learn the spatial-varying distribution of the full product of the rendering equation, with a single small neural network using stochastic ray samples. The learned distributions can be used to efficiently sample the full product of incident light. To accomplish
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HQ3DAvatar: High-quality Implicit 3D Head Avatar ACM Trans. Graph. (IF 7.8) Pub Date : 2024-04-09 Kartik Teotia, Mallikarjun B R, Xingang Pan, Hyeongwoo Kim, Pablo Garrido, Mohamed Elgharib, Christian Theobalt
Multi-view volumetric rendering techniques have recently shown great potential in modeling and synthesizing high-quality head avatars. A common approach to capture full head dynamic performances is to track the underlying geometry using a mesh-based template or 3D cube-based graphics primitives. While these model-based approaches achieve promising results, they often fail to learn complex geometric
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A Dual-Particle Approach for Incompressible SPH Fluids ACM Trans. Graph. (IF 7.8) Pub Date : 2024-04-09 Shusen Liu, Xiaowei He, Yuzhong Guo, Yue Chang, Wencheng Wang
Tensile instability is one of the major obstacles to particle methods in fluid simulation, which would cause particles to clump in pairs under tension and prevent fluid simulation to generate small-scale thin features. To address this issue, previous particle methods either use a background pressure or a finite difference scheme to alleviate the particle clustering artifacts, yet still fail to produce
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Joint Stroke Tracing and Correspondence for 2D Animation ACM Trans. Graph. (IF 7.8) Pub Date : 2024-04-09 Haoran Mo, Chengying Gao, Ruomei Wang
To alleviate human labor in redrawing keyframes with ordered vector strokes for automatic inbetweening, we for the first time propose a joint stroke tracing and correspondence approach. Given consecutive raster keyframes along with a single vector image of the starting frame as a guidance, the approach generates vector drawings for the remaining keyframes while ensuring one-to-one stroke correspondence
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DMHomo: Learning Homography with Diffusion Models ACM Trans. Graph. (IF 7.8) Pub Date : 2024-04-09 Haipeng Li, Hai Jiang, Ao Luo, Ping Tan, Haoqiang Fan, Bing Zeng, Shuaicheng Liu
Supervised homography estimation methods face a challenge due to the lack of adequate labeled training data. To address this issue, we propose DMHomo, a diffusion model-based framework for supervised homography learning. This framework generates image pairs with accurate labels, realistic image content, and realistic interval motion, ensuring that they satisfy adequate pairs. We utilize unlabeled image