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VarGAN: Adversarial Learning of Variable Semantic Representations IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-04-25 Yalan Lin, Chengcheng Wan, Shuwen Bai, Xiaodong Gu
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TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree Transformation IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-04-25 Zixiang Xian, Rubing Huang, Dave Towey, Chunrong Fang, Zhenyu Chen
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Neural Library Recommendation by Embedding Project-Library Knowledge Graph IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-04-24 Bo Li, Haowei Quan, Jiawei Wang, Pei Liu, Haipeng Cai, Yuan Miao, Yun Yang, Li Li
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No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPT IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-04-23 Zhijie Liu, Yutian Tang, Xiapu Luo, Yuming Zhou, Liang Feng Zhang
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Clopper-Pearson Algorithms for Efficient Statistical Model Checking Estimation IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-04-23 Hao Bu, Meng Sun
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A Platform-Agnostic Framework for Automatically Identifying Performance Issue Reports with Heuristic Linguistic Patterns IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-04-17 Yutong Zhao, Lu Xiao, Sunny Wong
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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 7.4) Pub Date : 2024-04-15 Qinghua Xu, Tao Yue, Shaukat Ali, Maite Arratibel
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MMO: Meta Multi-Objectivization for Software Configuration Tuning IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-04-15 Pengzhou Chen, Tao Chen, Miqing Li
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Generic Sensitivity: Generics-Guided Context Sensitivity for Pointer Analysis IEEE Trans. Softw. Eng. (IF 7.4) 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
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LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Types IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-04-11 Xin-Cheng Wen, Cuiyun Gao, Feng Luo, Haoyu Wang, Ge Li, Qing Liao
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Characterizing Timeout Builds in Continuous Integration IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-04-11 Nimmi Weeraddana, Mahmoud Alfadel, Shane McIntosh
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Domain-Driven Design for Microservices: An Evidence-based Investigation IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-04-10 Chenxing Zhong, Shanshan Li, Huang Huang, Xiaodong Liu, Zhikun Chen, Yi Zhang, He Zhang
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Controller Synthesis for Autonomous Systems with Deep-Learning Perception Components IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-04-10 Radu Calinescu, Calum Imrie, Ravi Mangal, Genaína Nunes Rodrigues, Corina Păsăreanu, Misael Alpizar Santana, Gricel Vázquez
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Test Input Prioritization for Graph Neural Networks IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-04-05 Yinghua Li, Xueqi Dang, Weiguo Pian, Andrew Habib, Jacques Klein, Tegawendé Bissyandé
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DAppSCAN: Building Large-Scale Datasets for Smart Contract Weaknesses in DApp Projects IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-29 Zibin Zheng, Jianzhong Su, Jiachi Chen, David Lo, Zhijie Zhong, Mingxi Ye
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ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generation IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-29 Yutian Tang, Zhijie Liu, Zhichao Zhou, Xiapu Luo
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Automated Code Editing with Search-Generate-Modify IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-27 Changshu Liu, Pelin Cetin, Yogesh Patodia, Baishakhi Ray, Saikat Chakraborty, Yangruibo Ding
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Understanding and Detecting Real-World Safety Issues in Rust IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-25 Boqin Qin, Yilun Chen, Haopeng Liu, Hua Zhang, Qiaoyan Wen, Linhai Song, Yiying Zhang
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MASTER: Multi-Source Transfer Weighted Ensemble Learning for Multiple Sources Cross-Project Defect Prediction IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-25 Haonan Tong, Dalin Zhang, Jiqiang Liu, Weiwei Xing, Lingyun Lu, Wei Lu, Yumei Wu
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Evaluating Search-Based Software Microbenchmark Prioritization IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-22 Christoph Laaber, Tao Yue, Shaukat Ali
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Shaken, Not Stirred. How Developers Like Their Amplified Tests IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-22 Carolin Brandt, Ali Khatami, Mairieli Wessel, Andy Zaidman
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Toward a Theory of Causation for Interpreting Neural Code Models IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-21 David N. Palacio, Alejandro Velasco, Nathan Cooper, Alvaro Rodriguez, Kevin Moran, Denys Poshyvanyk
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Microservice Extraction Based on a Comprehensive Evaluation of Logical Independence and Performance IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-21 Zhijun Ding, Yuehao Xu, Binbin Feng, Changjun Jiang
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Toward Cost-effective Adaptive Random Testing: An Approximate Nearest Neighbor Approach IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-21 Rubing Huang, Chenhui Cui, Junlong Lian, Dave Towey, Weifeng Sun, Haibo Chen
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hmCodeTrans: Human-Machine Interactive Code Translation IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-20 Jiaqi Liu, Fengming Zhang, Xin Zhang, Zhiwen Yu, Liang Wang, Yao Zhang, Bin Guo
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Distinguished Reviewers 2023 IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-18 Sebastian Uchitel
Lists the reviewers who contributed to this publication in 2023.
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Methods and Benchmark for Detecting Cryptographic API Misuses in Python IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-18 Miles Frantz, Ya Xiao, Tanmoy Sarkar Pias, Na Meng, Danfeng Daphne Yao
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Mutation Testing in Practice: Insights from Open-Source Software Developers IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-18 Ana B. Sánchez, José A. Parejo, Sergio Segura, Amador Durán, Mike Papadakis
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Asking and Answering Questions During Memory Profiling IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-13 Alison Fernandez Blanco, Araceli Queriolo Córdova, Alexandre Bergel, Juan Pablo Sandoval Alcocer
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Active Code Learning: Benchmarking Sample-Efficient Training of Code Models IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-13 Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon
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Evaluation Framework for Autonomous Systems: The Case of Programmable Electronic Medical Systems IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-11 Andrea Bombarda, Silvia Bonfanti, Martina De Sanctis, Angelo Gargantini, Patrizio Pelliccione, Elvinia Riccobene, Patrizia Scandurra
This paper proposes an evaluation framework for autonomous systems, called LENS. It is an instrument to make an assessment of a system through the lens of abilities related to adaptation and smartness. The assessment can then help engineers understand in which direction it is worth investing to make their system smarter. It also helps to identify possible improvement directions and to plan for concrete
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Provably Valid and Diverse Mutations of Real-World Media Data for DNN Testing IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-07 Yuanyuan Yuan, Qi Pang, Shuai Wang
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Exploring the Role of Team Security Climate in the Implementation of Security by Design: A Case Study in the Defense Sector IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-06 Micha Prudjinski, Irit Hadar, Gil Luria
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An Empirical Study of JVMs’ Behaviors on Erroneous JNI Interoperations IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-03-05 Sungjae Hwang, Sungho Lee, Sukyoung Ryu
Java Native Interface (JNI) allows Java applications to access native libraries, but it is challenging to develop correct JNI programs. By leveraging native code, the JNI enables Java developers to implement efficient applications and reuse code written in other programming languages such as C and C++. The core Java libraries use the JNI to provide system features like graphical user interfaces, and
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Automatic Debugging of Design Faults in MapReduce Applications IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-26 Jesús Morán, Antonia Bertolino, Claudio de la Riva, Javier Tuya
Among the current technologies to analyse large data, the MapReduce processing model stands out in Big Data. MapReduce is implemented in frameworks such as Hadoop, Spark or Flink that are able to manage the program executions according to the resources available at runtime. The developer should design the program in order to support all possible non-deterministic executions. However, the program may
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Factoring Expertise, Workload, and Turnover Into Code Review Recommendation IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-23 Fahimeh Hajari, Samaneh Malmir, Ehsan Mirsaeedi, Peter C. Rigby
Developer turnover is inevitable on software projects and leads to knowledge loss, a reduction in productivity, and an increase in defects. Mitigation strategies to deal with turnover tend to disrupt and increase workloads for developers. In this work, we suggest that through code review recommendation we can distribute knowledge and mitigate turnover while more evenly distributing review workload
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A Testing Program and Pragma Combination Selection Based Framework for High-Level Synthesis Tool Pragma-Related Bug Detection IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-22 He Jiang, Zun Wang, Zhide Zhou, Xiaochen Li, Shikai Guo, Weifeng Sun, Tao Zhang
High-Level Synthesis (HLS) tools convert C/C++ design code into Hardware Description Language (HDL) code automatically, which are often used for Field Programmable Gate Array (FPGA) design. HLS tools provide many pragmas, which are a kind of directive to be inserted into C/C++ code, for designers to efficiently control the synthesis of code components (e.g., arrays and loops) to generate FPGA implementations
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On the Understandability of MLOps System Architectures IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-20 Stephen John Warnett, Uwe Zdun
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Software Testing With Large Language Models: Survey, Landscape, and Vision IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-20 Junjie Wang, Yuchao Huang, Chunyang Chen, Zhe Liu, Song Wang, Qing Wang
Pre-trained large language models (LLMs) have recently emerged as a breakthrough technology in natural language processing and artificial intelligence, with the ability to handle large-scale datasets and exhibit remarkable performance across a wide range of tasks. Meanwhile, software testing is a crucial undertaking that serves as a cornerstone for ensuring the quality and reliability of software products
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Mask–Mediator–Wrapper Architecture as a Data Mesh Driver IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-19 Juraj Dončević, Krešimir Fertalj, Mario Brcic, Mihael Kovač
The data mesh is a novel data management concept that emphasizes the importance of a domain before technology. The concept is still in the early stages of development and many efforts to implement and use it are expected to have negative consequences for organizations due to a lack of technological guidelines and best practices. To mitigate the risk of negative outcomes this paper proposes the use
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Guess the State: Exploiting Determinism to Improve GUI Exploration Efficiency IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-16 Diego Clerissi, Giovanni Denaro, Marco Mobilio, Leonardo Mariani
Many automatic Web testing techniques generate test cases by analyzing the GUI of the Web applications under test, aiming to exercise sequences of actions that are similar to the ones that testers could manually execute. However, the efficiency of the test generation process is severely limited by the cost of analyzing the content of the GUI screens after executing each action. In this paper, we introduce
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Coverage Goal Selector for Combining Multiple Criteria in Search-Based Unit Test Generation IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-16 Zhichao Zhou, Yuming Zhou, Chunrong Fang, Zhenyu Chen, Xiapu Luo, Jingzhu He, Yutian Tang
Unit testing is critical to the software development process, ensuring the correctness of basic programming units in a program (e.g., a method). Search-based software testing (SBST) is an automated approach to generating test cases. SBST generates test cases with genetic algorithms by specifying the coverage criterion (e.g., branch coverage). However, a good test suite must have different properties
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Measuring and Characterizing (Mis)compliance of the Android Permission System IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-12 Anna Barzolevskaia, Enrico Branca, Natalia Stakhanova
Within the Android mobile operating system, Android permissions act as a system of safeguards designed to restrict access to potentially sensitive data and privileged components. Multiple research studies indicate flaws and limitations of the Android permission system, prompting Google to implement a more regulated and fine-grained permission model. This newly-introduced complexity creates confusion
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Automatic Commit Message Generation: A Critical Review and Directions for Future Work IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-12 Yuxia Zhang, Zhiqing Qiu, Klaas-Jan Stol, Wenhui Zhu, Jiaxin Zhu, Yingchen Tian, Hui Liu
Commit messages are critical for code comprehension and software maintenance. Writing a high-quality message requires skill and effort. To support developers and reduce their effort on this task, several approaches have been proposed to automatically generate commit messages. Despite the promising performance reported, we have identified three significant and prevalent threats in these automated approaches:
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A Systematic Review of IoT Systems Testing: Objectives, Approaches, Tools, and Challenges IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-12 Jean Baptiste Minani, Fatima Sabir, Naouel Moha, Yann-Gaël Guéhéneuc
Internet of Things (IoT) systems are becoming prevalent in various domains, from healthcare to smart homes. Testing IoT systems is critical in ensuring their reliability. Previous papers studied separately the objectives, approaches, tools, and challenges of IoT systems testing. However, despite the rapid evolution of the IoT domain, no review has been undertaken to investigate all four aspects collectively
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Stealthy Backdoor Attack for Code Models IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-09 Zhou Yang, Bowen Xu, Jie M. Zhang, Hong Jin Kang, Jieke Shi, Junda He, David Lo
Code models, such as CodeBERT and CodeT5, offer general-purpose representations of code and play a vital role in supporting downstream automated software engineering tasks. Most recently, code models were revealed to be vulnerable to backdoor attacks. A code model that is backdoor-attacked can behave normally on clean examples but will produce pre-defined malicious outputs on examples injected with
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DynAMICS: A Tool-Based Method for the Specification and Dynamic Detection of Android Behavioral Code Smells IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-06 Dimitri Prestat, Naouel Moha, Roger Villemaire, Florent Avellaneda
Code smells are the result of poor design choices within software systems that complexify source code and impede evolution and performance. Therefore, detecting code smells within software systems is an important priority to decrease technical debt. Furthermore, the emergence of mobile applications (apps) has brought new types of Android-specific code smells, which relate to limitations and constraints
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Automated Smell Detection and Recommendation in Natural Language Requirements IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-01 Alvaro Veizaga, Seung Yeob Shin, Lionel C. Briand
Requirement specifications are typically written in natural language (NL) due to its usability across multiple domains and understandability by all stakeholders. However, unstructured NL is prone to quality problems (e.g., ambiguity) when writing requirements, which can result in project failures. To address this issue, we present a tool, named Paska, that takes as input any NL requirements, automatically
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On the Usefulness of Automatically Generated Microservice Architectures IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-01 Luiz Carvalho, Thelma Elita Colanzi, Wesley K. G. Assunção, Alessandro Garcia, Juliana Alves Pereira, Marcos Kalinowski, Rafael Maiani de Mello, Maria Julia de Lima, Carlos Lucena
The modernization of monolithic legacy systems with microservices has been a trend in recent years. As part of this modernization, identifying microservice candidates starting from legacy code is challenging, as maintainers may consider many criteria simultaneously. Multi-objective search-based approaches represent a promising state-of-the-art solution to support this decision-making process. However
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Neural Density Estimation of Response Times in Layered Software Systems IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-30 Zifeng Niu, Giuliano Casale
Layered queueing networks (LQNs) are a class of performance models for software systems in which multiple distributed resources may be possessed simultaneously by a job. Estimating response times in a layered system is an essential but challenging analysis dimension in Quality of Service (QoS) assessment. Current analytic methods are capable of providing accurate estimates of mean response times. However
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Accelerating Patch Validation for Program Repair With Interception-Based Execution Scheduling IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-30 Yuan-An Xiao, Chenyang Yang, Bo Wang, Yingfei Xiong
Long patch validation time is a limiting factor for automated program repair (APR). Though the duality between patch validation and mutation testing is recognized, so far there exists no study of systematically adapting mutation testing techniques to general-purpose patch validation. To address this gap, we investigate existing mutation testing techniques and identify five classes of acceleration techniques
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Code Comment Inconsistency Detection Based on Confidence Learning IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-29 Zhengkang Xu, Shikai Guo, Yumiao Wang, Rong Chen, Hui Li, Xiaochen Li, He Jiang
Code comments are a crucial source of software documentation that captures various aspects of the code. Such comments play a vital role in understanding the source code and facilitating communication between developers. However, with the iterative release of software, software projects become larger and more complex, leading to a corresponding increase in issues such as mismatched, incomplete, or outdated
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Accelerating Finite State Machine-Based Testing Using Reinforcement Learning IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-25 Uraz Cengiz Türker, Robert M. Hierons, Khaled El-Fakih, Mohammad Reza Mousavi, Ivan Y. Tyukin
Testing is a crucial phase in the development of complex systems, and this has led to interest in automated test generation techniques based on state-based models. Many approaches use models that are types of finite state machine (FSM). Corresponding test generation algorithms typically require that certain test components, such as reset sequences (RSs) and preset distinguishing sequences (PDSs), have
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Tracking the Evolution of Static Code Warnings: The State-of-the-Art and a Better Approach IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-24 Junjie Li, Jinqiu Yang
Static bug detection tools help developers detect problems in the code, including bad programming practices and potential defects. Recent efforts to integrate static bug detectors in modern software development workflows, such as in code review and continuous integration, are shown to better motivate developers to fix the reported warnings on the fly. A proper mechanism to track the evolution of the
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Test Data Generation for Mutation Testing Based on Markov Chain Usage Model and Estimation of Distribution Algorithm IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-24 Changqing Wei, Xiangjuan Yao, Dunwei Gong, Huai Liu
Mutation testing, a mainstream fault-based software testing technique, can mimic a wide variety of software faults by seeding them into the target program and resulting in the so-called mutants. Test data generated in mutation testing should be able to kill as many mutants as possible, hence guaranteeing a high fault-detection effectiveness of testing. Nevertheless, the test data generation can be
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Multi-Language Software Development: Issues, Challenges, and Solutions IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-24 Haoran Yang, Yu Nong, Shaowei Wang, Haipeng Cai
Developing software projects that incorporate multiple languages has been a prevalent practice for many years. However, the issues encountered by developers during the development process, the underlying challenges causing these issues, and the solutions provided to developers remain unknown. In this paper, our objective is to provide answers to these questions by conducting a study on developer discussions
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Distilling Quality Enhancing Comments from Code Reviews to Underpin Reviewer Recommendation IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-22 Guoping Rong, Yongda Yu, Yifan Zhang, He Zhang, Haifeng Shen, Dong Shao, Hongyu Kuang, Min Wang, Zhao Wei, Yong Xu, Juhong Wang
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Range Specification Bug Detection in Flight Control System Through Fuzzing IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-17 Ruidong Han, Siqi Ma, Juanru Li, Surya Nepal, David Lo, Zhuo Ma, JianFeng Ma
Developers and manufacturers provide configurable control parameters for flight control programs to support various environments and missions, along with suggested ranges for these parameters to ensure flight safety. However, this flexible mechanism can also introduce a vulnerability known as range specification bugs. The vulnerability originates from the evidence that certain combinations of parameter
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APPT: Boosting Automated Patch Correctness Prediction via Fine-Tuning Pre-Trained Models IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-17 Quanjun Zhang, Chunrong Fang, Weisong Sun, Yan Liu, Tieke He, Xiaodong Hao, Zhenyu Chen
Automated program repair (APR) aims to fix software bugs automatically without human debugging efforts and plays a crucial role in software development and maintenance. Despite the recent significant progress in the number of fixed bugs, APR is still challenged by a long-standing overfitting problem (i.e., the generated patch is plausible but overfitting). Various techniques have thus been proposed