Testing is an inevitable part of any softwareengineering process to ensure quality and reliability. Model-based testing is a successful approach for the automated generation of test cases but requires a model of the ...
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Teaching empirical methods in softwareengineering education is a challenging task: at the end of the course, students should understand the main concepts of empirical research and be able to apply them in an industri...
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Increasing the semantic understanding and contextual awareness of machine learning models is important for improving robustness and reducing susceptibility to data shifts. In this work, we leverage contextual awarenes...
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ISBN:
(数字)9781665490429
ISBN:
(纸本)9781665490429
Increasing the semantic understanding and contextual awareness of machine learning models is important for improving robustness and reducing susceptibility to data shifts. In this work, we leverage contextual awareness for the anomaly detection problem. Although graphed-based anomaly detection has been widely studied, context-dependent anomaly detection is an open problem and without much current research. We develop a general framework for converting a context-dependent anomaly detection problem to a link prediction problem, allowing well-established techniques from this domain to be applied. We implement a system based on our framework that utilizes knowledge graph embedding models and demonstrates the ability to detect outliers using context provided by a semantic knowledge base. We show that our method can detect context-dependent anomalies with a high degree of accuracy and show that current object detectors can detect enough classes to provide the needed context to show good performance within our example domain.
Recently, system security represents a big challenge for many organizations, and it must be specifically handled when a system is intended to be deployed in a cloud environment. Cloud environments provide multiple sec...
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ISBN:
(纸本)9798400707728
Recently, system security represents a big challenge for many organizations, and it must be specifically handled when a system is intended to be deployed in a cloud environment. Cloud environments provide multiple security services that run over a Shared Responsibility Model that requires the participation of the cloud provider and the customer. thus, this paper proposes an architecture based on Artificial Intelligence to support the finding of system threats and errors in an early stage and on Security Chaos engineering methodology to reliably test the existence of such errors. this proposed architecture may help orientate better system designs and contribute to building holistic security. A particular use case is described to show how the proposal can be applied to a system that supports services for a military-related organization.
Background: Discussing and sharing information in development teams is part of any software project. therefore, software engineers spend significant time in meetings withtheir team. Communicating effectively and effi...
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ISBN:
(纸本)9798400704987
Background: Discussing and sharing information in development teams is part of any software project. therefore, software engineers spend significant time in meetings withtheir team. Communicating effectively and efficiently in those meetings is essential. However, software engineers often do not possess the right skills. On the other hand, training face-to-face meeting communication skills in university settings is resource- and time-consuming. Aims: Our goal is to develop and evaluate a method to support the training of face-to-face meeting communication skills. Method: We develop a method based on active video-watching. Active video-watching supports deep learning by systematically engaging students with video-based learning material. We also implement this method in an online platform for classroom use. Furthermore, we empirically develop a new measurement instrument to assess face-to-face meeting communication skills. To evaluate the training method, we used it in three instances of a second-year softwareengineering project course. To assess learning gain, we assessed (a) the conceptual knowledge about face-to-face meeting communication, and (b) skills based on our newly developed measurement instrument, both before and after the training. Results: Both conceptual knowledge as well as skill measurement scores based on our instrument increased. Increases are statistically significant. Conclusions: We show the effectiveness of active video-watching for training face-to-face meeting communication skills, one specific soft skill relevant for software engineers. the measurement instrument that we developed earl also be used as a stand-alone tool to assess skills of students and potentially practitioners.
In the decision system, the lower approximation set keeps expanding with adding features dynamically. But when enough features are added, the lower approximation set stabilizes. this provides a criterion for feature s...
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Withthe climate crisis looming, engineering sustainable software systems become crucial to optimize resource utilization, minimize environmental impact, and foster a greener, more resilient digital ecosystem. For dev...
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ISBN:
(纸本)9798350329964
Withthe climate crisis looming, engineering sustainable software systems become crucial to optimize resource utilization, minimize environmental impact, and foster a greener, more resilient digital ecosystem. For developers, getting access to automated tools that analyze code and suggest sustainability-related optimizations becomes extremely important from a learning and implementation perspective. However, there is currently a dearth of such tools due to the lack of standardized knowledge, which serves as the foundation of these tools. In this paper, we motivate the need for the development of a standard knowledge base of commonly occurring sustainability weaknesses in code, and propose an initial way of doing that. Furthermore, through preliminary experiments, we demonstrate why existing knowledge regarding software weaknesses cannot be re-tagged "as is" to sustainability without significant due diligence, thereby urging further explorations in this ecologically significant domain.
It is expected that in the near future, AI software development assistants will play an important role in the software industry. However, current software development assistants tend to be unreliable, often producing ...
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ISBN:
(纸本)9798400705007
It is expected that in the near future, AI software development assistants will play an important role in the software industry. However, current software development assistants tend to be unreliable, often producing incorrect, unsafe, or low-quality code. We seek to resolve these issues by introducing a holistic architecture for constructing, training, and using trustworthy AI software development assistants. In the center of the architecture, there is a foundational LLM trained on datasets representative of real-world coding scenarios and complex software architectures, and fine-tuned on code quality criteria beyond correctness. the LLM will make use of graph-based code representations for advanced semantic comprehension. We envision a knowledge graph integrated into the system to provide up-to-date background knowledge and to enable the assistant to provide appropriate explanations. Finally, a modular framework for constrained decoding will ensure that certain guarantees (e.g., for correctness and security) hold for the generated code.
this scientific article presents that the ideal job is crucial for undergraduate softwareengineering students because it is related to their mental and financial health. the proposal seeks the creation of a hybrid re...
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By far, the most effective knowledge assessment in college education is to give students exam and grade their answers then assess their level of understanding. However, exam grading can be time-consuming, tedious, cum...
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ISBN:
(纸本)9798350391961;9798350391954
By far, the most effective knowledge assessment in college education is to give students exam and grade their answers then assess their level of understanding. However, exam grading can be time-consuming, tedious, cumbersome, and sometimes the grading results are not consistent withthe rubric. Here, we propose an AI based exam grader that can not only ease educators' burden but also produce accurate, consistent, and precise grading results. We have used GPT-3.5, GPT-4.0, and Gemini-pro, respectively, as our grading engine. To verify the correctness, precision, and accuracy of our proposed grader, the results were compared withthe instructor's grading result and also with human grader such as teaching assistants. In our experiment, GPT-4.0 showed the most reliable and consistent results.
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