Article
Chemistry, Multidisciplinary
Fakeeha Jafari, Aamer Nadeem, Qamar Uz Zaman
Summary: Magnetic resonance imaging (MRI) is a valuable tool for diagnostics and machine learning. Testing image processing applications (IPA) is crucial for reliable results. Accurate disease detection through IPA leads to improved treatment quality. The lack of test cases is a challenge for IPA testing, which can be addressed through metamorphic testing.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Software Engineering
Xintao Niu, Yanjie Sun, Huayao Wu, Gang Li, Changhai Nie, Lei Yu, Xiaoyin Wang
Summary: This paper proposes a novel CT methodology called COMER, which enhances traditional CT by accounting for Metamorphic Relations (MRs). COMER can automatically determine the correctness of test cases and detect test oracle violations. Empirical studies have shown the effectiveness and performance of COMER.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Information Systems
Yan Xiaobo, Liu Bin, Wang Shihai, An Dong, Zhu Feng, Yang Yelin
Summary: This study aims to construct a framework to filter unlabelled test cases using entropy-based methods for better fault localization efficiency. Results show that the framework significantly improves fault localization efficiency, serving as a solution for practical scenarios lacking test oracles.
INFORMATION AND SOFTWARE TECHNOLOGY
(2021)
Article
Computer Science, Interdisciplinary Applications
Joshua D. Ellis, Razib Iqbal, Keiichi Yoshimatsu
Summary: FTIR is a commonly used technique in chemical analysis that predicts the presence of various substructures in compounds based on their absorbance patterns. Researchers developed a neural network training system and applied metamorphic testing to enhance the training process, which was proven to be effective in developing classifier neural networks.
JOURNAL OF COMPUTATIONAL SCIENCE
(2021)
Article
Computer Science, Software Engineering
Kun Qiu, Zheng Zheng, Tsong Chen, Pak-Lok Poon
Summary: Research investigates using MR composition to reduce testing costs and improve fault detection capability in MT.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
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
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)
Article
Computer Science, Software Engineering
Arianna Blasi, Alessandra Gorla, Michael D. Ernst, Mauro Pezze, Antonio Carzaniga
Summary: Software testing relies on effective oracles, with formal specification-based oracles revealing application-specific failures but being costly to obtain and maintain. MeMo is a technique and tool that automatically derives metamorphic equivalence relations from natural language documentation, effectively detecting defects when used as oracles in test cases.
JOURNAL OF SYSTEMS AND SOFTWARE
(2021)
Article
Mathematical & Computational Biology
Dong-Gun Lee, Yeong-Seok Seo
Summary: In software engineering, testing is an important research area in maintenance. This paper proposes a method that uses mutation testing to identify defects that are not discovered in a single round of testing. By applying multiple mutants simultaneously to define and record relationships between lines of code, the method examines these relationships and recommends potential defects from recorded candidates. The evaluation shows that the proposed method outperforms seven other fault localization methods.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Computer Science, Information Systems
Yue Yan, Shujuan Jiang, Yanmei Zhang, Shenggang Zhang, Cheng Zhang
Summary: This paper proposes a method to solve the "tie" problem in SBFL techniques by applying fault propagation context, which improves the performance of fault localization. Experimental results show that the proposed approach outperforms existing fault context-based methods and traditional SBFL methods.
INFORMATION AND SOFTWARE TECHNOLOGY
(2023)
Article
Chemistry, Multidisciplinary
Shih-DA Wu, Jung-Hua Lo
Summary: Spectrum-based fault localization (SBFL) uses spectrum information of test cases to calculate the suspiciousness of each statement in a program, helping reduce developers' effort. However, using redundant test cases for fault localization is burdensome, particularly in resource-constrained environments, and inspecting the results of each test input is expensive and impractical. Prioritizing/selecting appropriate test cases is crucial for practical application of SBFL, ensuring it achieves similar effectiveness as using all tests.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Software Engineering
Chang-ai Sun, Hui Jin, Siyi Wu, An Fu, Zuoyi Wang, Wing Kwong Chan
Summary: Metamorphic testing is an effective technique to address the test oracle problem. This paper proposes mu$$ \mu $$MT, a data mutation directed approach to identifying metamorphic relations (MRs), necessary properties of software under test. The empirical study shows that mu$$ \mu $$MT is able to effectively identify MRs for numeric programs, with high fault detection capability and statement coverage.
SOFTWARE-PRACTICE & EXPERIENCE
(2023)
Article
Computer Science, Software Engineering
Zhi Quan Zhou, T. H. Tse, Matt Witheridge
Summary: The presence of hyphens in paper titles can distort citation counts and impact factor evaluations, challenging the common belief that they are reliable measures. This study reveals a strong negative correlation between journal impact factor and the percentage of papers with hyphenated titles, impacting the assessment of paper and journal impact.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2021)
Article
Computer Science, Software Engineering
Rongjie Yan, Siqi Wang, Yixuan Yan, Hongyu Gao, Jun Yan
Summary: The paper proposes a methodology to automatically evaluate the stability of text localization systems using metamorphic relations, with six metamorphic relations defined for stable systems and corresponding metrics for evaluation. By applying metamorphic testing techniques based on the defined relations, system stability can be evaluated and causes of inconsistency diagnosed effectively. Experimental results on academic and commercial text localization systems demonstrate the effectiveness of the proposed method in stability evaluation.
JOURNAL OF SYSTEMS AND SOFTWARE
(2021)
Article
Multidisciplinary Sciences
Abdulaziz Alhumam
Summary: Software Fault Forecasting (SFF) aims to identify sections in software projects that are prone to faults and result in significant development expenses. A proposed model uses a collective formulation of a fault localization model and model-specific metadata to build a global model for software fault forecasting.
Article
Computer Science, Information Systems
Weijun Shen, Yanhui Li, Yuanlei Han, Lin Chen, Di Wu, Yuming Zhou, Baowen Xu
Summary: The study introduces boundary sample selection (BSS) approach to select a smaller, sensitive, representative, and efficient subset of the test dataset for promoting mutation testing in DL models. The experimental results show that the subsets generated by BSS are smaller in size, superior in observing mutation effects, replaceable to a high degree in mutation score, and have better Mean Reciprocal Rank (MRR) values compared to the whole test sets. BSS can help reduce labeling cost, run mutation testing quickly, and identify killed mutants early.
INFORMATION AND SOFTWARE TECHNOLOGY
(2021)
Article
Computer Science, Software Engineering
Di Wu, Xiao-Yuan Jing, Hongyu Zhang, Bing Li, Yu Xie, Baowen Xu
Summary: API tutorials and Stack Overflow posts both contain API tags to help understand and navigate code snippets. ATTACK is a novel approach that uses deep neural network with attention mechanism to automatically generate API tags from Stack Overflow posts.
EMPIRICAL SOFTWARE ENGINEERING
(2021)
Article
Computer Science, Hardware & Architecture
Zhi Quan Zhou, Chen Liu, Tsong Yueh Chen, T. H. Tse, Willy Susilo
Summary: Existing test case prioritization techniques have limitations and may not always be more effective than random prioritization. This article introduces a new technique based on a dispersity metric that has been shown to be more effective. Empirical studies support the effectiveness of this new technique.
IEEE TRANSACTIONS ON RELIABILITY
(2021)
Article
Computer Science, Software Engineering
Yuying Li, Yang Feng, Rui Hao, Di Liu, Chunrong Fang, Zhenyu Chen, Baowen Xu
Summary: Crowdsourced testing is a popular method for testing mobile applications. It can simulate real usage scenarios and detect various bugs with a large workforce. However, the inspection and classification of crowdsourced test reports can be time-consuming. To address this issue, researchers have proposed techniques for automatically classifying test reports. In this study, we fuse features from text descriptions and screenshots to classify crowdsourced test reports and evaluate the effectiveness of our approach using six classification algorithms. The results show that SVM with fused features performs the best in classifying crowdsourced test reports, and image features improve the classification performance.
JOURNAL OF SYSTEMS AND SOFTWARE
(2022)
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)
Article
Computer Science, Hardware & Architecture
Chang-ai Sun, An Fu, Xinling Guo, Tsong Yueh Chen
Summary: Mutation testing is a fault-based software testing technique that measures fault detection effectiveness using simulated faults. The identification of redundant mutants, such as through the ReMuSSE technique, aims to improve testing efficiency by removing mutants with similar program execution state changes.
IEEE TRANSACTIONS ON RELIABILITY
(2022)
Article
Computer Science, Software Engineering
Kun Qiu, Zheng Zheng, Tsong Chen, Pak-Lok Poon
Summary: Research investigates using MR composition to reduce testing costs and improve fault detection capability in MT.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Software Engineering
Peng Zhang, Yanhui Li, Wanwangying Ma, Yibiao Yang, Lin Chen, Hongmin Lu, Yuming Zhou, Baowen Xu
Summary: This paper proposes a Coverage-Based Unsupervised Approach (CBUA) for evaluating the effectiveness of a test suite. Experimental results show that CBUA is competitive with the state-of-the-art supervised approaches and is effective in finding covered but not killed mutants.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Information Systems
Chang-ai Sun, Hepeng Dai, Guan Wang, Dave Towey, Tsong Yueh Chen, Kai-Yuan Cai
Summary: This article proposes a dynamic random testing (DRT) technique for web services and improves upon the random testing (RT) and partition testing (PT) approaches. Empirical studies show that DRT demonstrates higher fault-detection effectiveness and lower test case selection overhead compared to baseline techniques.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Computer Science, Information Systems
Haowen Chen, Xiao-Yuan Jing, Yuming Zhou, Bing Li, Baowen Xu
Summary: This study proposes a novel approach for heterogeneous defect prediction that reduces heterogeneity, deals with imbalanced data, and retains the meaningfulness of metric space. Experimental results demonstrate its superior performance across various indicators.
INFORMATION AND SOFTWARE TECHNOLOGY
(2022)
Article
Computer Science, Hardware & Architecture
Qiang Zhang, Lei Xu, Baowen Xu
Summary: Interpreters are widely used in programming language implementations and play a critical role in program performance. Register-based interpreters outperform stack-based interpreters in terms of execution efficiency and also excel in memory footprint, compilation cost, and implementation complexity.
ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION
(2022)
Article
Computer Science, Software Engineering
Di Wu, Xiao-Yuan Jing, Hongyu Zhang, Yuming Zhou, Baowen Xu
Summary: This paper introduces a new method called SO2RT for detecting relevant tutorial fragments of APIs using Stack Overflow (SO) posts. The method reduces the effort required for labeling the relevance between tutorial fragments and APIs, and its effectiveness is confirmed through experiments and user studies.
EMPIRICAL SOFTWARE ENGINEERING
(2023)
Article
Computer Science, Software Engineering
Yao Deng, Xi Zheng, Tianyi Zhang, Huai Liu, Guannan Lou, Miryung Kim, Tsong Yueh Chen
Summary: Autonomous driving has attracted much attention, but safety concerns arise due to fatal accidents caused by autonomous vehicles. This paper presents a rule-based metamorphic testing framework called RMT to detect potential issues in autonomous driving models.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2023)
Proceedings Paper
Computer Science, Software Engineering
Tsong Yueh Chen, T. H. Tse
Summary: Metamorphic testing (MT) has become a recognized and useful testing technique in both research and industry, with potential applications beyond software testing. Researchers have various visions for the future of MT, including integration with other methods and expansion into new domains.
PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21)
(2021)
Article
Computer Science, Artificial Intelligence
Rubing Huang, Weifeng Sun, Tsong Yueh Chen, Sebastian Ng, Jinfu Chen
Summary: The failure region, where failure-causing inputs reside, is crucial for enhancing testing effectiveness and supporting other processes. This paper introduces a new strategy, Search for Boundary (SB), to identify an approximate failure region of a numeric input domain by identifying additional failure-causing inputs close to the boundary.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2021)
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)