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A Systematic Literature Review of Model-Driven Engineering using Machine Learning IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-07-18 Ana C. Marcén, Antonio Iglesias, Raúl Lapeña, Francisca Pérez, Carlos Cetina
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HSTCG: State-Aware Simulink Model Test Case Generation with Heuristic Strategy IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-07-15 Zhuo Su, Zehong Yu, Dongyan Wang, Yixiao Yang, Rui Wang, Wanli Chang, Aiguo Cui, Yu Jiang
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Mole: Efficient Crash Reproduction in Android Applications with Enforcing Necessary UI Events IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-07-15 Maryam Masoudian, Heqing Huang, Morteza Amini, Charles Zhang
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Towards Efficient Fine-tuning of Language Models with Organizational Data for Automated Software Review IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-07-15 Mona Nashaat, James Miller
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Unity is Strength: Enhancing Precision in Reentrancy Vulnerability Detection of Smart Contract Analysis Tools IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-07-12 Zexu Wang, Jiachi Chen, Peilin Zheng, Yu Zhang, Weizhe Zhang, Zibin Zheng
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Vulnerability Detection via Multiple-Graph-Based Code Representation IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-07-12 Fangcheng Qiu, Zhongxin Liu, Xing Hu, Xin Xia, Gang Chen, Xinyu Wang
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BinCola: Diversity-sensitive Contrastive Learning for Binary Code Similarity Detection IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-07-08 Shuai Jiang, Cai Fu, Shuai He, Jianqiang Lv, Lansheng Han, Hong Hu
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Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasets IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-07-05 Partha Chakraborty, Krishna Kanth Arumugam, Mahmoud Alfadel, Meiyappan Nagappan, Shane McIntosh
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API2Vec++: Boosting API Sequence Representation for Malware Detection and Classification IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-07-04 Lei Cui, Junnan Yin, Jiancong Cui, Yuede Ji, Peng Liu, Zhiyu Hao, Xiaochun Yun
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Local and Global Explainability for Technical Debt Identification IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-07-04 Dimitrios Tsoukalas, Nikolaos Mittas, Elvira-Maria Arvanitou, Apostolos Ampatzoglou, Alexander Chatzigeorgiou, Dionysios Kechagias
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To Do or Not to Do: Semantics and Patterns for Do Activities in UML PSSM State Machines IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-07-04 Márton Elekes, Vince Molnár, Zoltán Micskei
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ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-07-03 Chunrong Fang, Weisong Sun, Yuchen Chen, Xiao Chen, Zhao Wei, Quanjun Zhang, Yudu You, Bin Luo, Yang Liu, Zhenyu Chen
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Characterizing the Prevalence Distribution and Duration of Stale Reviewer Recommendations IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-07-03 Farshad Kazemi, Maxime Lamothe, Shane McIntosh
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Multi-objective Software Defect Prediction via Multi-source Uncertain Information Fusion and Multi-task Multi-view Learning IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-07-03 Minghao Yang, Shunkun Yang, W. Eric Wong
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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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LUNA: A Model-Based Universal Analysis Framework for Large Language Models IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-06-18 Da Song, Xuan Xie, Jiayang Song, Derui Zhu, Yuheng Huang, Felix Juefei-Xu, Lei Ma
Over the past decade, Artificial Intelligence (AI) has had great success recently and is being used in a wide range of academic and industrial fields. More recently, Large Language Models (LLMs) have made rapid advancements that have propelled AI to a new level, enabling and empowering even more diverse applications and industrial domains with intelligence, particularly in areas like software engineering
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Practical, Automated Scenario-Based Mobile App Testing IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-06-14 Shengcheng Yu, Chunrong Fang, Mingzhe Du, Zimin Ding, Zhenyu Chen, Zhendong Su
The importance of mobile application (app) quality assurance is increasing with the rapid development of the mobile Internet. Automated test generation approaches, as a dominant direction of app quality assurance, follow specific models or strategies, targeting at optimizing the code coverage. Such approaches lead to a huge gap between testing execution and app business logic. Test scripts developed
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Improving Issue-PR Link Prediction via Knowledge-Aware Heterogeneous Graph Learning IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-06-03 Shuotong Bai, Huaxiao Liu, Enyan Dai, Lei Liu
Links between issues and pull requests (PRs) assist GitHub developers in tackling technical challenges, gaining development inspiration, and improving repository maintenance. In realistic repositories, these links are still insufficiently established. Aiming at this situation, existing works focus on issues and PRs themselves and employ text similarity with additional information like issue size to
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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
Metamorphic testing is a popular approach that aims to alleviate the oracle problem in software testing. At the core of this approach are Metamorphic Relations (MRs), specifying properties that hold among multiple test inputs and corresponding outputs. Deriving MRs is mostly a manual activity, since their automated generation is a challenging and largely unexplored problem. This paper presents GenMorph
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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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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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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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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
The Internet of Things (IoT) connects a plethora of smart devices globally across various applications like smart cities, autonomous vehicles, and health monitoring. Simulation plays a key role in the testing of IoT systems, noting that field testing of a complete IoT product may be infeasible or prohibitively expensive. This paper addresses a specific yet important need in simulation-based testing
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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
Code search, which consists in retrieving relevant code snippets from a codebase based on a given query, provides developers with useful references during software development. Over the years, techniques alternatively adopting different mechanisms to compute the relevance score between a query and a code snippet have been proposed to advance the state of the art in this domain, including those relying
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MR${}^{2}$ 2-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
Stack Overflow is a knowledge sharing platform where its users create and share informative content from both inside and outside the site. Prior studies have leveraged the relation across Stack Overflow posts through internal links to build services and applications to enhance the accessibility of knowledge. However, they focused on studying a knowledge unit that consists of a question post and all
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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
The blockchain has been the main computing scenario for smart contracts, and the decentralized infrastructure of the blockchain is effectively implemented in a de-trusted and executable environment. However, vulnerabilities in smart contracts are particularly vulnerable to exploitation by malicious attackers and have always been a key issue in blockchain security. Existing traditional tools are inefficient
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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
Due to well-hidden and stage-triggered properties of second-order SQL injections in web applications, current approaches are ineffective in addressing them and still report high false negatives and false positives. To reduce false results, we propose a P roxy-based s tatic analysis and dy namic ex ecution m echanism towards detecting, locating and preventing second-order SQL injections (SQLPsdem).
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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
Compiler bugs pose a significant threat to safety-critical applications, and promptly as well as effectively isolating these bugs is crucial for assuring the quality of compilers. However, the limited availability of debugging information on reported bugs complicates the compiler bug isolation task. Existing compiler bug isolation approaches convert the problem into a test program mutation problem
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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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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
Memory allocation is a fundamental operation for managing memory objects in many programming languages. Misusing allocated memory objects (e.g., buffer overflow and use-after-free ) can have catastrophic consequences. Symbolic execution-based approaches have been used to detect such memory errors, benefiting from their capabilities in automatic path exploration and test case generation. However, existing
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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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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
Statistical model checking (SMC) is a simulation-based formal verification technique to deal with the scalability problem faced by traditional model checking. The main workflow of SMC is to perform iterative simulations. The number of simulations depends on users’ requirement for the verification results, which can be very large if users require a high level of confidence and precision. Therefore,
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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
Software performance is critical for system efficiency, with performance issues potentially resulting in budget overruns, project delays, and market losses. Such problems are reported to developers through issue tracking systems, which are often under-tagged, as the manual tagging process is voluntary and time-consuming. Existing automated performance issue tagging techniques, such as keyword matching
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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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Test Input Prioritization for Graph Neural Networks IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-04-05 Yinghua Li, Xueqi Dang, Weiguo Pian, Andrew Habib, Jacques Klein, Tegawendé F. Bissyandé
GNNs have shown remarkable performance in a variety of classification tasks. The reliability of GNN models needs to be thoroughly validated before their deployment to ensure their accurate functioning. Therefore, effective testing is essential for identifying vulnerabilities in GNN models. However, given the complexity and size of graph-structured data, the cost of manual labelling of GNN test inputs
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DAppSCAN: Building Large-Scale Datasets for Smart Contract Weaknesses in DApp Projects IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-03-29 Zibin Zheng, Jianzhong Su, Jiachi Chen, David Lo, Zhijie Zhong, Mingxi Ye
The Smart Contract Weakness Classification Registry (SWC Registry) is a widely recognized list of smart contract weaknesses specific to the Ethereum platform. Despite the SWC Registry not being updated with new entries since 2020, the sustained development of smart contract analysis tools for detecting SWC-listed weaknesses highlights their ongoing significance in the field. However, evaluating these
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ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generation IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-03-29 Yutian Tang, Zhijie Liu, Zhichao Zhou, Xiapu Luo
Recent advancements in large language models (LLMs) have demonstrated exceptional success in a wide range of general domain tasks, such as question answering and following instructions. Moreover, LLMs have shown potential in various software engineering applications. In this study, we present a systematic comparison of test suites generated by the ChatGPT LLM and the state-of-the-art SBST tool EvoSuite
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Automated Code Editing With Search-Generate-Modify IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-03-27 Changshu Liu, Pelin Cetin, Yogesh Patodia, Baishakhi Ray, Saikat Chakraborty, Yangruibo Ding
Code editing is essential in evolving software development. In literature, several automated code editing tools are proposed, which leverage Information Retrieval-based techniques and Machine Learning-based code generation and code editing models. Each technique comes with its own promises and perils, and for this reason, they are often used together to complement their strengths and compensate for
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Understanding and Detecting Real-World Safety Issues in Rust IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-03-25 Boqin Qin, Yilun Chen, Haopeng Liu, Hua Zhang, Qiaoyan Wen, Linhai Song, Yiying Zhang
Rust is a relatively new programming language designed for systems software development. Its objective is to combine the safety guarantees typically associated with high-level languages with the performance efficiency often found in executable programs implemented in low-level languages. The core design of Rust is a set of strict safety rules enforced through compile-time checks. However, to support
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MASTER: Multi-Source Transfer Weighted Ensemble Learning for Multiple Sources Cross-Project Defect Prediction IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-03-25 Haonan Tong, Dalin Zhang, Jiqiang Liu, Weiwei Xing, Lingyun Lu, Wei Lu, Yumei Wu
Multi-source cross-project defect prediction (MSCPDP) attempts to transfer defect knowledge learned from multiple source projects to the target project. MSCPDP has drawn increasing attention from academic and industry communities owing to its advantages compared with single-source cross-project defect prediction (SSCPDP). However, two main problems, which are how to effectively extract the transferable
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Evaluating Search-Based Software Microbenchmark Prioritization IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-03-22 Christoph Laaber, Tao Yue, Shaukat Ali
Ensuring that software performance does not degrade after a code change is paramount. A solution is to regularly execute software microbenchmarks, a performance testing technique similar to (functional) unit tests, which, however, often becomes infeasible due to extensive runtimes. To address that challenge, research has investigated regression testing techniques, such as test case prioritization (TCP)
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Shaken, Not Stirred: How Developers Like Their Amplified Tests IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-03-22 Carolin Brandt, Ali Khatami, Mairieli Wessel, Andy Zaidman
Test amplification makes systematic changes to existing, manually written tests to provide tests complementary to an automated test suite. We consider developer-centric test amplification, where the developer explores, judges and edits the amplified tests before adding them to their maintained test suite. However, it is as yet unclear which kind of selection and editing steps developers take before
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Toward a Theory of Causation for Interpreting Neural Code Models IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-03-21 David Nader Palacio, Alejandro Velasco, Nathan Cooper, Alvaro Rodriguez, Kevin Moran, Denys Poshyvanyk
Neural Language Models of Code, or Neural Code Models (NCMs), are rapidly progressing from research prototypes to commercial developer tools. As such, understanding the capabilities and limitations of such models is becoming critical. However, the abilities of these models are typically measured using automated metrics that often only reveal a portion of their real-world performance. While, in general
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Microservice Extraction Based on a Comprehensive Evaluation of Logical Independence and Performance IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-03-21 Zhijun Ding, Yuehao Xu, Binbin Feng, Changjun Jiang
Monolithic architectures are becoming increasingly difficult to cope with complex applications, and microservice architectures, which offer flexibility and logical independence in development and maintenance, are the new choice for companies and developers. Migrating a legacy monolithic architecture application to a microservice architecture rather than building it from scratch is considered an easy
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Toward Cost-Effective Adaptive Random Testing: An Approximate Nearest Neighbor Approach IEEE Trans. Softw. Eng. (IF 6.5) Pub Date : 2024-03-21 Rubing Huang, Chenhui Cui, Junlong Lian, Dave Towey, Weifeng Sun, Haibo Chen
Adaptive Random Testing (ART) enhances the testing effectiveness (including fault-detection capability) of Random Testing (RT) by increasing the diversity of the random test cases throughout the input domain. Many ART algorithms have been investigated such as Fixed-Size-Candidate-Set ART (FSCS) and Restricted Random Testing (RRT), and have been widely used in many practical applications. Despite its