Article
Computer Science, Information Systems
Miao Zhang, Jacky Wai Keung, Tsong Yueh Chen, Yan Xiao
Summary: This study tackles the test oracle problem in class integration test order generation systems by applying Metamorphic Testing (MT) with five effective Metamorphic Relations (MRs) to ensure system quality. Empirical experiments demonstrated successful failure detection of faulty programs in three different systems implementing typical class integration test order generation approaches. The proposed MRs systematically and effectively detect faults in class integration test order generation systems, expanding the applications of MT in Software Engineering.
INFORMATION AND SOFTWARE TECHNOLOGY
(2021)
Article
Engineering, Civil
Bing Zhu, Peixing Zhang, Jian Zhao, Weiwen Deng
Summary: The paper proposes an Optimization Searching method for enhanced generation in hazardous scenarios, utilizing modules such as Exploration and Exploitation, Parameter Moving Probability Determination, Step Size Determination, Memory Function, and Result Analysis. The method effectively explores functional boundaries in the automated vehicle context and shows significant improvement in test speed in ACC algorithm testing.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Software Engineering
Chang-ai Sun, Baoli Liu, An Fu, Yiqiang Liu, Huai Liu
Summary: Metamorphic testing is a technique that constructs new test cases based on software properties, with recent research focusing on generating and selecting effective source test cases. A novel approach based on path constraints to generate source test cases is proposed in this paper, leveraging path distance to guide the prioritization and improve efficiency.
JOURNAL OF SYSTEMS AND SOFTWARE
(2022)
Article
Computer Science, Hardware & Architecture
Zhan-wei Hui, Xiaojuan Wang, Song Huang, Sen Yang
Summary: This article introduces a novel method based on adaptive random testing and metamorphic relations for metamorphic testing test case generation, which outperforms other algorithms in test effectiveness, test efficiency, and test coverage. The study concludes that considering the effectiveness of metamorphic relations and test cases can lead to better results, both source test cases and follow-up test cases should be considered together, and average distance performs better in test case selection for metamorphic testing.
IEEE TRANSACTIONS ON RELIABILITY
(2021)
Article
Computer Science, Software Engineering
Chang-Ai Sun, Hepeng Dai, Huai Liu, Tsong Yueh Chen
Summary: Metamorphic testing has gained increasing attention for its efficacy in revealing software faults. This study proposes a new approach called feedback-directed metamorphic testing, which leverages test execution feedback to improve cost-effectiveness. The empirical results show that feedback-directed metamorphic testing can use fewer test cases and less time to detect the same number of faults compared to traditional metamorphic testing.
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
(2023)
Review
Chemistry, Analytical
Yu Zhu, Jian Wang, Fanqiang Meng, Tongtao Liu
Summary: The advancement of autonomous driving technology has had a significant impact on transportation networks and people's lives. Traditional testing methods are no longer sufficient, leading to the emergence of simulation testing as a new technique. Creating test scenarios is a crucial aspect of simulation testing.
Article
Computer Science, Software Engineering
Walter Cazzola, Luca Favalli
Summary: The correctness of compilers and interpreters is crucial for the reliable execution of software. Testing, including mutation testing, is an important method to detect errors and improve test suite quality. This work presents a mutation approach for programming languages that mitigates computing resource demands.
JOURNAL OF SYSTEMS AND SOFTWARE
(2023)
Article
Computer Science, Information Systems
Pilsu Jung, Seonah Lee, Uicheon Lee
Summary: This study proposes an automated code-based approach (ActSPL) for reusing SPL test cases by utilizing source code and test cases. The results show that ActSPL achieves high precision and recall, and significantly reduces the time required for testing a new product.
INFORMATION AND SOFTWARE TECHNOLOGY
(2024)
Article
Computer Science, Software Engineering
Xiuting Ge, Shengcheng Yu, Chunrong Fang, Qi Zhu, Zhihong Zhao
Summary: Crowdsourced testing is a promising approach for large-scale and user-oriented testing of mobile applications, but the varying levels of testing experience among crowdworkers pose a threat to the quality of crowdsourced testing. To address this problem, this study proposes a testing assistance approach that leverages Android automated testing to improve crowdsourced testing. The approach constructs a model for the App Under Test (AUT) and provides test task extraction, recommendation, and guidance to assist crowdworkers. Experimental evaluation shows that the approach effectively and efficiently assists crowdsourced testing, and user study confirms its usefulness.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2023)
Article
Computer Science, Information Systems
Pablo Gomez-Abajo, Pablo C. Canizares, Alberto Nunez, Esther Guerra, Juan de Lara
Summary: We propose a model-driven engineering approach using the Gotten platform to automate the construction of domain-specific MT environments, which can support the definition and evaluation of domain-specific metamorphic relations, as well as the generation of new test cases and detailed reporting of testing results.
INFORMATION AND SOFTWARE TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Yi Zhong, Mengyu Shi, Youran Xu, Chunrong Fang, Zhenyu Chen
Summary: Automated testing has been widely used for quality assurance of mobile apps, but manual testing can achieve higher coverage in complex activities. The effectiveness of manual testing depends on the user operation process (UOP) of experienced testers. This study proposes an iterative Android automated testing (IAAT) method that utilizes UOPs to guide the test logic and improve coverage. Experimental results show that IAAT significantly improves code coverage compared to Monkey and initial automated tests, with an average increase of 13.98% to 37.83% under a 60-minute test time.
FRONTIERS OF COMPUTER SCIENCE
(2023)
Article
Computer Science, Information Systems
Mutlu Beyazit, Tugkan Tuglular, Dilek Ozturk Kaya
Summary: One way to develop fast, effective, and high-quality software products is to reuse previously developed software components and products. The software product line (SPL) approach can make reuse more effective in the case of a product family. This paper proposes an incremental model-based approach to test products in SPLs by utilizing event-based behavioral models.
Article
Computer Science, Information Systems
Shibli Nisar, Muhammad Asghar Khan, Fahad Algarni, Abdul Wakeel, M. Irfan Uddin, Insaf Ullah
Summary: This paper proposes a novel method for measuring eyesight deficiency, utilizing an adaptive filter bank and feature extraction. The research demonstrates that this method can achieve comparable results to expert ophthalmologist tests, offering a potential second opinion for ophthalmologists and serving as a cost-effective pre-screening test.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Environmental Sciences
Yang Yang, Ting Fong May Chui
Summary: This study introduces a method based on metamorphic testing (MT) to assess the prediction reliability of machine learning models in hydrological studies, where actual outputs are unavailable. The research found that prediction accuracy and consistency were not correlated, and investigated factors such as input similarity to observed data influencing assessment results. Overall, MT is shown to be an effective method for detecting inconsistent model predictions and is recommended for new condition predictions.
WATER RESOURCES RESEARCH
(2021)
Article
Computer Science, Software Engineering
Zhen Yang, Song Huang, Changyou Zheng, Xingya Wang, Yang Wang, Chunyan Xia
Summary: The study introduces MetaLiDAR, an automated metamorphic testing methodology for LiDAR-based autonomous driving systems. MetaLiDAR generates natural-looking point clouds and utilizes metamorphic relations and evaluation metrics for automated testing, significantly enhancing the robustness of models.
JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS
(2023)
Article
Computer Science, Software Engineering
Beatriz Bernardez, Amador Duran, Jose A. Parejo, Natalia Juristo, Antonio Ruiz-Cortes
Summary: This study found that software engineering students showed improved conceptual modeling performance in terms of quality and productivity after practicing mindfulness. These findings indicate the positive impact of mindfulness on enhancing students' performance in software engineering tasks.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Software Engineering
Juan C. Alonso, Alberto Martin-Lopez, Sergio Segura, Jose Maria Garcia, Antonio Ruiz-Cortes
Summary: In this article, the authors present ARTE, an approach for automated extraction of realistic test data for web APIs from knowledge bases. ARTE leverages natural language processing, search-based, and knowledge extraction techniques to automatically search for realistic test inputs based on the API specification. The evaluation results demonstrate the potential of ARTE for enhancing web API testing tools and achieving a higher level of automation.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2023)
Article
Computer Science, Theory & Methods
Antonio Quina-Mera, Pablo Fernandez, Jose Maria Garcia, Antonio Ruiz-Cortes
Summary: GraphQL is a query language and execution engine proposed as an alternative to improve data access problems and versioning of APIs. This article presents a systematic mapping study of 84 primary studies to analyze the trends and knowledge gaps in the GraphQL field. The study concludes that GraphQL adoption is increasing as a strong alternative for implementing APIs, but more empirical evidence collection is needed in industry and government studies.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Theory & Methods
James Baxter, Ana Cavalcanti, Maciej Gazda, Robert M. Hierons
Summary: This research introduces a new approach for timed refinement and testing using a dialect of CSP called tock-CSP. It provides a novel semantics for testing by distinguishing input and output events in tock-CSP. Additionally, a new testing theory for timewise refinement is presented, based on novel definitions of test and test execution. The paper also establishes a relationship between timed ioco testing and refinement in tock-CSP with inputs and outputs.
ACM TRANSACTIONS ON COMPUTATIONAL LOGIC
(2023)
Article
Computer Science, Theory & Methods
Margarita Cruz, Beatriz Bernardez, Amador Duran, Cathy Guevara-Vega, Antonio Ruiz-Cortes
Summary: The main goal of this article is to provide a systematic tool-supported approach for the specification and reporting of changes in replications of empirical studies in Computer Science. The developed artifact includes a metamodel, templates and linguistic patterns, and a model-based software tool. A multiple case study with 9 families of empirical studies was conducted to validate the approach, revealing some initial limitations. The proposed method seems to be applicable not only in Computer Science but also in other research areas.
Correction
Computer Science, Theory & Methods
Margarita Cruz, Beatriz Bernardez, Amador Duran, Cathy Guevara-Vega, Antonio Ruiz-Cortes
Article
Computer Science, Hardware & Architecture
Mohammad Reza Mousavi, Ana Cavalcanti, Michael Fisher, Louise Dennis, Rob Hierons, Bilal Kaddouh, Effie Lai-Chong Law, Rob Richardson, Jan Oliver Ringer, Ivan Tyukin, Jim Woodcock
Summary: Autonomous systems have the potential to solve societal challenges, but trustworthiness is crucial. A U.K. consortium conducted research to address the central issue of establishing verifiability.
Article
Computer Science, Hardware & Architecture
Jon Ayerdi, Pablo Valle, Sergio Segura, Aitor Arrieta, Goiuria Sagardui, Maite Arratibel
Summary: Cyber-physical systems (CPSs) are a new generation of systems that integrate software with physical processes. Metamorphic testing has shown great potential for alleviating the test oracle problem by utilizing the relationships among the inputs and outputs of different system executions. This article proposes an MR pattern (PV) for identifying performance-driven MRs and evaluates its effectiveness in two CPSs from different domains.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Computer Science, Software Engineering
Neil Walkinshaw, Robert M. Hierons
Summary: In this article, a method for modelling finite state machines using Subjective Logic is introduced. This method allows modellers to capture their own uncertainty about the stated probabilities, enhancing the accuracy and reliability of the models.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2023)
Article
Computer Science, Theory & Methods
Maciej Gazda, Robert M. Hierons
Summary: We investigate the problem of finding a minimal complete test suite for refusal trace semantics. Our approach is based on generating a minimal complete set of forbidden refusal traces and utilizing insightful insights into the semantics. We identify a key class of refusals called fundamental refusals, which essentially determine the refusal trace semantics and associated equivalence relation. We propose a small but not necessarily minimal test suite and provide a method to remove redundant traces while maintaining uniform completeness if desired.
INFORMATION AND COMPUTATION
(2023)
Review
Computer Science, Information Systems
Richard J. Somers, James A. Douthwaite, David J. Wagg, Neil Walkinshaw, Robert M. Hierons
Summary: This study aims to summarize the existing literature on digital-twin-based testing. Digital twins enhance physical systems through visualization, future state prediction, and communication. The adoption of digital twin testing has been increasing in different domains, and the testing techniques are continuously evolving.
INFORMATION AND SOFTWARE TECHNOLOGY
(2023)
Article
Computer Science, Software Engineering
Uraz Cengiz Turker, Robert M. Hierons, Gerassimos Barlas, Khaled El-Fakih
Summary: The increasing complexity and criticality of software systems have led to a growing interest in automated test generation, particularly using model-based testing (MBT). This study proposes a generalization of incomplete adaptive distinguishing sequences (ADSs) to handle non-deterministic partial and observable finite state machines (FSMs). It also presents a novel algorithm to generate incomplete ADSs and demonstrates its superior performance in identifying states of the implementation under test (IUT) compared to existing methods.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2023)
Article
Automation & Control Systems
Manuel Nunez, Robert M. Hierons, Raluca Lefticaru
Summary: This paper focuses on systematically testing robotic control software based on state-based models. It provides a testing theory for cyclic systems, where time is represented and probabilities are used to model non-deterministic choices. The paper also considers refusals and different testing scenarios, leading to a range of implementation relations. It offers formal definitions of implementation relations that can be used for sound automated testing and validates them through alternative characterizations using observers.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2023)
Correction
Computer Science, Theory & Methods
Margarita Cruz, Beatriz Bernardez, Amador Duran, Cathy Guevara-Vega, Antonio Ruiz-Cortes
Article
Computer Science, Information Systems
Hao Ren, Yanhui Li, Lin Chen, Yuming Zhou, Changhai Nie
Summary: This study aims to explore the breakable blocking bugs (BBBs) through quantitative and qualitative analysis. The analysis reveals that BBBs have higher levels of involvement, longer fix time, and more complex source code compared to other bugs. The study also identifies four reasons for breaking blocking relationships between bugs and three measures adopted by developers to break these relationships.
INFORMATION AND SOFTWARE TECHNOLOGY
(2024)
Article
Computer Science, Information Systems
Chunyong Zhang, Tianxiang Yu, Bin Liu, Yang Xin
Summary: This paper proposes a vulnerability detection framework based on federated learning (VDBFL), which combines code property graph, graph neural networks, and convolutional neural networks to detect vulnerability code. The experimental results show that this method outperforms other vulnerability detection methods.
INFORMATION AND SOFTWARE TECHNOLOGY
(2024)
Article
Computer Science, Information Systems
Martin Stancek, Ivan Polasek, Tibor Zalabai, Juraj Vincur, Rodi Jolak, Michel Chaudron
Summary: The aim of this research is to support distributed software design activities in Virtual Reality (VR). Using design science research methodology, a tool for collaborative design in VR is designed and evaluated. The efficiency of collaboration and recall of design information when using VR software design environment compared to non-VR environment are evaluated. Furthermore, the perceptions and preferences of users are collected to explore the opportunities and challenges of using VR software design environment.
INFORMATION AND SOFTWARE TECHNOLOGY
(2024)
Article
Computer Science, Information Systems
Zhen Yang, Jacky Wai Keung, Zeyu Sun, Yunfei Zhao, Ge Li, Zhi Jin, Shuo Liu, Yishu Li
Summary: This paper presents MetaCoder, a meta-learning code generation approach that efficiently extracts general-purpose knowledge from large-scale source languages and rapidly adapts to domain-specific scenarios.
INFORMATION AND SOFTWARE TECHNOLOGY
(2024)
Article
Computer Science, Information Systems
Pilsu Jung, Seonah Lee, Uicheon Lee
Summary: This study proposes an automated code-based approach (ActSPL) for reusing SPL test cases by utilizing source code and test cases. The results show that ActSPL achieves high precision and recall, and significantly reduces the time required for testing a new product.
INFORMATION AND SOFTWARE TECHNOLOGY
(2024)
Article
Computer Science, Information Systems
Chao Liu, Runfeng Cai, Yiqun Zhou, Xin Chen, Haibo Hu, Meng Yan
Summary: This paper conducts an empirical study on the implementation issues of deep learning frameworks, focusing on relevant questions on Stack Overflow. The study identifies various implementation issues and constructs a taxonomy, revealing that data processing, model setting, model training, and model prediction are the most common categories. The paper also provides suggestions for future research and aims to help developers and researchers understand these issues better.
INFORMATION AND SOFTWARE TECHNOLOGY
(2024)
Article
Computer Science, Information Systems
Muhammad Azeem Akbar, Arif Ali Khan, Mohammad Shameem, Mohammad Nadeem
Summary: This study identifies key variables in quantum software development (QSD) and develops a model for predicting the success probability of QSD projects. The results show that as the QSD process matures, project success probability significantly increases and costs are notably reduced. The developed prediction model can help practitioners focus on key areas for successful implementation of QSD projects.
INFORMATION AND SOFTWARE TECHNOLOGY
(2024)
Article
Computer Science, Information Systems
Md. Shamsujjoha, John Grundy, Hourieh Khalajzadeh, Qinghua Lu, Li Li
Summary: This paper investigates the challenges and benefits of incorporating human aspects into eHealth app development and usage from the perspectives of developers and end-users. The study used a mixed-method approach and gathered data from online surveys and interviews. The findings suggest that addressing human aspects throughout the app development life-cycle is beneficial for more effective eHealth apps.
INFORMATION AND SOFTWARE TECHNOLOGY
(2024)
Article
Computer Science, Information Systems
Yuchao Jiang, Boualem Benatallah, Marcos Baez
Summary: This paper reports on interviews and surveys with early-stage researchers (ESRs) and explores the potential of online research communities in supporting ESRs to learn from diverse perspectives and experiences. The results reveal the limited adoption of research communities for learning and identify unmet needs in their design. Design implications for future socio-technical systems are provided to support the development of research skills.
INFORMATION AND SOFTWARE TECHNOLOGY
(2024)
Article
Computer Science, Information Systems
Jyoti Prakash Meher, Sourav Biswas, Rajib Mall
Summary: Accurate bug classification is important for speeding up bug triage, code inspection, and repair tasks. To improve classification, this study proposes a novel bug classification approach based on deep learning. The approach includes building a bug taxonomy with eight bug classes using keywords, annotating a large set of bug resolution reports, and utilizing attention-based classification techniques. Experimental results show that the proposed technique outperforms existing methods in terms of F1-Score by an average of 16.88% on the considered dataset.
INFORMATION AND SOFTWARE TECHNOLOGY
(2024)
Article
Computer Science, Information Systems
Rodrigo Santos, Eleni Constantinou, Pablo Antonino, Jan Bosch
Summary: In the last decade, software engineering has faced challenges beyond technical aspects. The field now considers technological, organizational, and social aspects together in research and practice to handle complexity and provide solutions to the industry's demands. Systems-of-systems (SoS) and software ecosystems (SECO) have emerged as topics of interest, bringing together researchers and practitioners to understand how to manage and engineer software-intensive systems in modern, complex, distributed, dynamic, and open environments.
INFORMATION AND SOFTWARE TECHNOLOGY
(2024)
Article
Computer Science, Information Systems
Zhuo Wu, Zan Wang, Junjie Chen, Hanmo You, Ming Yan, Lanjun Wang
Summary: In this paper, a statistical method called Stratified random Sampling with Optimum Allocation (SSOA) is proposed to provide an unbiased estimation of model accuracy with the smallest estimation variance. The unlabeled test set is first divided into strata based on predictive confidences. Then, two stratum accuracy variance estimation methods are designed to allocate the given budget to each stratum based on the optimum allocation strategy. Multiple experiments are conducted to evaluate the effectiveness and stability of SSOA by comparing it with baseline methods.
INFORMATION AND SOFTWARE TECHNOLOGY
(2024)
Review
Computer Science, Information Systems
Jorge Chueca, Javier Veron, Jaime Font, Francisca Perez, Carlos Cetina
INFORMATION AND SOFTWARE TECHNOLOGY
(2024)
