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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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DynAMICS: A tool-based method for the specification and dynamic detection of Android behavioural code smells IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-02-06 Dimitri Prestat, Naouel Moha, Roger Villemaire, Florent Avellaneda
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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
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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
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Improving Test Data Generation for MPI Program Path Coverage With FERPSO-IMPR and Surrogate-Assisted Models IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-17 Yong Wang, Wenzhong Cui, Gai-Ge Wang, Jian Wang, Dunwei Gong
Message passing interface (MPI) is a powerful tool for parallel computing, originally designed for high-performance computing on massively parallel computers. In this paper, we combine FERPSO-IMPR (fitness Euclidean distance ratio particle swarm optimizer with information migration-based penalty and population reshaping) and surrogate-assisted models to generate test cases for MPI program path coverage
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Understanding Newcomers’ Onboarding Process in Deep Learning Projects IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-12 Junxiao Han, Jiahao Zhang, David Lo, Xin Xia, Shuiguang Deng, Minghui Wu
Attracting and retaining newcomers are critical for the sustainable development of Open Source Software (OSS) projects. Considerable efforts have been made to help newcomers identify and overcome barriers in the onboarding process. However, fewer studies focus on newcomers’ activities before their successful onboarding. Given the rising popularity of deep learning (DL) techniques, we wonder what the
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Corrections to “Uncovering Bugs in Code Coverage Profilers via Control Flow Constraint Solving” IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-10 Yang Wang, Peng Zhang, Maolin Sun, Zeyu Lu, Yibiao Yang, Yutian Tang, Junyan Qian, Zhi Li, Yuming Zhou
In [1, p. 4967], a figure citation is incorrect and “Fig. 3(c)” should be “Fig. 1(c)” in the left column, the fourth line from the bottom. It is corrected below.
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An Empirical Study on Correlations Between Deep Neural Network Fairness and Neuron Coverage Criteria IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-08 Wei Zheng, Lidan Lin, Xiaoxue Wu, Xiang Chen
Recently, with the widespread use of deep neural networks (DNNs) in high-stakes decision-making systems (such as fraud detection and prison sentencing), concerns have arisen about the fairness of DNNs in terms of the potential negative impact they may have on individuals and society. Therefore, fairness testing has become an important research topic in DNN testing. At the same time, the neural network
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Test Input Prioritization for Machine Learning Classifiers IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-05 Xueqi Dang, Yinghua Li, Mike Papadakis, Jacques Klein, Tegawendé F. Bissyandé, Yves Le Traon
Machine learning has achieved remarkable success across diverse domains. Nevertheless, concerns about interpretability in black-box models, especially within Deep Neural Networks (DNNs), have become pronounced in safety-critical fields like healthcare and finance. Classical machine learning (ML) classifiers, known for their higher interpretability, are preferred in these domains. Similar to DNNs, classical
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Behind the Intent of Extract Method Refactoring: A Systematic Literature Review IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-04 Eman Abdullah AlOmar, Mohamed Wiem Mkaouer, Ali Ouni
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Federated Learning for Software Engineering: A Case Study of Code Clone Detection and Defect Prediction IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-03 Yanming Yang, Xing Hu, Zhipeng Gao, Jinfu Chen, Chao Ni, Xin Xia, David Lo
In various research domains, artificial intelligence (AI) has gained significant prominence, leading to the development of numerous learning-based models in research laboratories, which are evaluated using benchmark datasets. While the models proposed in previous studies may demonstrate satisfactory performance on benchmark datasets, translating academic findings into practical applications for industry
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Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression Against Heterogeneous Attacks Toward AI Software Deployment IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-01 Jie Zhu, Leye Wang, Xiao Han, Anmin Liu, Tao Xie
The size of deep learning models in artificial intelligence (AI) software is increasing rapidly, hindering the large-scale deployment on resource-restricted devices ( e.g ., smartphones). To mitigate this issue, AI software compression plays a crucial role, which aims to compress model size while keeping high performance. However, the intrinsic defects in a big model may be inherited by the compressed
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Code Review Automation: Strengths and Weaknesses of the State of the Art IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2024-01-01 Rosalia Tufano, Ozren Dabić, Antonio Mastropaolo, Matteo Ciniselli, Gabriele Bavota
The automation of code review has been tackled by several researchers with the goal of reducing its cost. The adoption of deep learning in software engineering pushed the automation to new boundaries, with techniques imitating developers in generative tasks, such as commenting on a code change as a reviewer would do or addressing a reviewer's comment by modifying code. The performance of these techniques
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Meta-Path Based Attentional Graph Learning Model for Vulnerability Detection IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2023-12-28 Xin-Cheng Wen, Cuiyun Gao, Jiaxin Ye, Yichen Li, Zhihong Tian, Yan Jia, Xuan Wang
In recent years, deep learning (DL)-based methods have been widely used in code vulnerability detection. The DL-based methods typically extract structural information from source code, e.g., code structure graph, and adopt neural networks such as Graph Neural Networks (GNNs) to learn the graph representations. However, these methods fail to consider the heterogeneous relations in the code structure
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On Effectiveness and Efficiency of Gamified Exploratory GUI Testing IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2023-12-28 Riccardo Coppola, Tommaso Fulcini, Luca Ardito, Marco Torchiano, Emil Alègroth
Context : Gamification appears to improve enjoyment and quality of execution of software engineering activities, including software testing. Though commonly employed in industry, manual exploratory testing of web application GUIs was proven to be mundane and expensive. Gamification applied to that kind of testing activity has the potential to overcome its limitations, though no empirical research has
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Simulation-Based Testing of Simulink Models With Test Sequence and Test Assessment Blocks IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2023-12-25 Federico Formica, Tony Fan, Akshay Rajhans, Vera Pantelic, Mark Lawford, Claudio Menghi
Simulation-based software testing supports engineers in finding faults in Simulink ® models. It typically relies on search algorithms that iteratively generate test inputs used to exercise models in simulation to detect design errors. While simulation-based software testing techniques are effective in many practical scenarios, they are typically not fully integrated within the Simulink environment
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Revisiting Knowledge-Based Inference of Python Runtime Environments: A Realistic and Adaptive Approach IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2023-12-25 Wei Cheng, Wei Hu, Xiaoxing Ma
The reuse and integration of existing code is a common practice for efficient software development. Constantly updated Python interpreters and third-party packages introduce many challenges to Python runtime environment inference. Existing knowledge-based approaches have achieved good performance but still suffer from several limitations in the real world, especially from incomplete domain knowledge
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Answering Uncertain, Under-Specified API Queries Assisted by Knowledge-Aware Human-AI Dialogue IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2023-12-25 Qing Huang, Zishuai Li, Zhenchang Xing, Zhengkang Zuo, Xin Peng, Xiwei Xu, Qinghua Lu
Developers’ API needs should be more pragmatic, such as seeking suggestive, explainable, and extensible APIs rather than the so-called best result. Existing API search research cannot meet these pragmatic needs because they are solely concerned with query-API relevance. This necessitates a focus on enhancing the entire query process, from query definition to query refinement through intent clarification
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INSPECT: Intrinsic and Systematic Probing Evaluation for Code Transformers IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2023-12-12 Anjan Karmakar, Romain Robbes
Pre-trained models of source code have recently been successfully applied to a wide variety of Software Engineering tasks; they have also seen some practical adoption in practice, e.g. for code completion. Yet, we still know very little about what these pre-trained models learn about source code. In this article, we use probing —simple diagnostic tasks that do not further train the models—to discover
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An Assessment of Rules of Thumb for Software Phase Management, and the Relationship Between Phase Effort and Schedule Success IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2023-12-11 Daniel Long, Scott Drylie, Jonathan D. Ritschel, Clay Koschnick
In the planning of a software development project, managers must estimate the amount of effort needed for distinct phases of activity. A number of rules of thumb exist in the literature to help the program manager in this task. However, very little work has been done to validate these rules of thumb. Applying least square models and Hotelling's $T^{2}$ test, we evaluate these rules of thumb against
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The Double-Edged Sword of Diversity: How Diversity, Conflict, and Psychological Safety Impact Software Teams IEEE Trans. Softw. Eng. (IF 7.4) Pub Date : 2023-12-08 Christiaan Verwijs, Daniel Russo
Team diversity can be seen as a double-edged sword. It brings additional cognitive resources to teams at the risk of increased conflict. Few studies have investigated how different types of diversity impact software teams. This study views diversity through the lens of the categorization-elaboration model (CEM) . We investigated how diversity in gender, age, role, and cultural background impacts team