| 1. |
- Ebadi, Hamid, et al.
(författare)
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Efficient and Effective Generation of Test Cases for Pedestrian Detection - Search-based Software Testing of Baidu Apollo in SVL
- 2021
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Ingår i: 2021 IEEE International Conference on Artificial Intelligence Testing (AITest). - : IEEE. - 9781665434812 ; , s. 103-110
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Konferensbidrag (refereegranskat)abstract
- With the growing capabilities of autonomous vehicles, there is a higher demand for sophisticated and pragmatic quality assurance approaches for machine learning-enabled systems in the automotive AI context. The use of simulation-based prototyping platforms provides the possibility for early-stage testing, enabling inexpensive testing and the ability to capture critical corner-case test scenarios. Simulation-based testing properly complements conventional on-road testing. However, due to the large space of test input parameters in these systems, the efficient generation of effective test scenarios leading to the unveiling of failures is a challenge. This paper presents a study on testing pedestrian detection and emergency braking system of the Baidu Apollo autonomous driving platform within the SVL simulator. We propose an evolutionary automated test generation technique that generates failure-revealing scenarios for Apollo in the SVL environment. Our approach models the input space using a generic and flexible data structure and benefits a multi-criteria safety-based heuristic for the objective function targeted for optimization. This paper presents the results of our proposed test generation technique in the 2021 IEEE Autonomous Driving AI Test Challenge. In order to demonstrate the efficiency and effectiveness of our approach, we also report the results from a baseline random generation technique. Our evaluation shows that the proposed evolutionary test case generator is more effective at generating failure-revealing test cases and provides higher diversity between the generated failures than the random baseline.
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| 2. |
- Fontes, Afonso, 1987, et al.
(författare)
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Automated Support forUnit Test Generation
- 2023
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Ingår i: Natural Computing Series. - Singapore : Springer. - 1619-7127. ; , s. 179-219
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Bokkapitel (refereegranskat)abstract
- Unit testing is a stage of testing where the smallest segment of code that can be tested in isolation from the rest of the system—often a class—is tested. Unit tests are typically written as executable code, often in a format provided by a unit testing framework such as pytest for Python. Creating unit tests is a time and effort-intensive process with many repetitive, manual elements. To illustrate how AI can support unit testing, this chapter introduces the concept of search-based unit test generation. This technique frames the selection of test input as an optimization problem—we seek a set of test cases that meet some measurable goal of a tester—and unleashes powerful metaheuristic search algorithms to identify the best possible test cases within a restricted timeframe. This chapter introduces two algorithms that can generate pytest-formatted unit tests, tuned towards coverage of source code statements. The chapter concludes by discussing more advanced concepts and gives pointers to further reading for how artificial intelligence can support developers and testers when unit testing software.
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| 3. |
- Fontes, Afonso, 1987, et al.
(författare)
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The integration of machine learning into automated test generation: A systematic mapping study
- 2023
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Ingår i: Software Testing Verification and Reliability. - 0960-0833 .- 1099-1689. ; 33:4
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning (ML) may enable effective automated test generation. We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges in this intersection by performing. We perform a systematic mapping study on a sample of 124 publications. ML generates input for system, GUI, unit, performance, and combinatorial testing or improves the performance of existing generation methods. ML is also used to generate test verdicts, property-based, and expected output oracles. Supervised learning—often based on neural networks—and reinforcement learning—often based on Q-learning—are common, and some publications also employ unsupervised or semi-supervised learning. (Semi-/Un-)Supervised approaches are evaluated using both traditional testing metrics and ML-related metrics (e.g., accuracy), while reinforcement learning is often evaluated using testing metrics tied to the reward function. The work-to-date shows great promise, but there are open challenges regarding training data, retraining, scalability, evaluation complexity, ML algorithms employed—and how they are applied—benchmarks, and replicability. Our findings can serve as a roadmap and inspiration for researchers in this field.
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| 4. |
- Fontes, Afonso, 1987, et al.
(författare)
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Using Machine Learning to Generate Test Oracles: A Systematic Literature Review
- 2021
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Ingår i: TORACLE 2021 - Proceedings of the 1st International Workshop on Test Oracles, co-located with ESEC/FSE 2021. - New York, NY, USA : ACM. - 9781450386265 ; , s. 1-10
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Konferensbidrag (refereegranskat)abstract
- Machine learning may enable the automated generation of test oracles. We have characterized emerging research in this area through a systematic literature review examining oracle types, researcher goals, the ML techniques applied, how the generation process was assessed, and the open research challenges in this emerging field. Based on a sample of 22 relevant studies, we observed that ML algorithms generated test verdict, metamorphic relation, and - most commonly - expected output oracles. Almost all studies employ a supervised or semi-supervised approach, trained on labeled system executions or code metadata - including neural networks, support vector machines, adaptive boosting, and decision trees. Oracles are evaluated using the mutation score, correct classifications, accuracy, and ROC. Work-to-date show great promise, but there are significant open challenges regarding the requirements imposed on training data, the complexity of modeled functions, the ML algorithms employed - and how they are applied - the benchmarks used by researchers, and replicability of the studies. We hope that our findings will serve as a roadmap and inspiration for researchers in this field.
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| 5. |
- Fontes, Afonso, 1987
(författare)
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Industrial Internet of Things Security enhanced with Deep Learning Approaches for Smart Cities
- 2021
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Ingår i: IEEE Internet of Things Journal. - 2327-4662.
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Tidskriftsartikel (refereegranskat)abstract
- The significant evolution of the Internet of Things (IoT) enabled the development of numerous devices able to improve many aspects in various fields in the industry for smart cities where machines have replaced humans. With the reduction in manual work and the adoption of automation, cities are getting more efficient and smarter. However, this evolution also made data even more sensitive, especially in the industrial segment. The latter has caught the attention of many hackers targeting Industrial IoT (IIoT) devices or networks, hence the number of malicious software, i.e., malware, has increased as well. In this article, we present the IIoT concept and applications for smart cities, besides also presenting the security challenges faced by this emerging area. We survey currently available deep learning techniques for IIoT in smart cities, mainly Deep Reinforcement Learning, Recurrent Neural Networks, and Convolutional Neural Networks, and highlight the advantages and disadvantages of security-related methods. We also present insights, open issues, and future trends applying deep learning techniques to enhance IIoT security.
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| 6. |
- Han, T., et al.
(författare)
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Emerging Drone Trends for Blockchain-Based 5G Networks: Open Issues and Future Perspectives
- 2021
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Ingår i: Ieee Network. - : Institute of Electrical and Electronics Engineers (IEEE). - 0890-8044 .- 1558-156X. ; 35:1, s. 38-43
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Tidskriftsartikel (refereegranskat)abstract
- Unmanned aerial vehicles, commonly known as drones, are receiving growing research interest due to their ability to carry a multitude of sensors and to connect to mobile networks. They are also able to move freely across the air, which enables the creation of numerous applications that were until now considered impracticable. However, such applications may require high computational resources, reliable connection, and high data transmission rates to accomplish different tasks. Therefore, in this work, first, we discuss 5G communication networks and mobile edge computing (MEC) as promising technologies that can provide several benefits to drone-enabled environments and solve some of the presented issues. We also comment on 5G and MEC approaches, presenting the state of the art and seeking to solve each of the latter issues presented. Afterward, we introduce new security concerns of drone communication networks, given their recent popularity. These concerns are related to the possibility of malicious users taking advantage of this brand new technology, which has made many governments ban drones due to public safety. Next, blockchain technology is brought in as a novel solution to the security issues due to its decentralized nature, making it inherently safe. This article also surveys contributions that make use of each of the technologies mentioned to improve the emerging drone industry. Subsequently, we discuss open issues and future perspectives.
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