Since the introduction of generative artificial intelligence (GenAI), education in computerscience has prompted efforts to incorporate it into the educational curriculum. This innovative practice full paper presents ...
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ISBN:
(纸本)9798350351507
Since the introduction of generative artificial intelligence (GenAI), education in computerscience has prompted efforts to incorporate it into the educational curriculum. This innovative practice full paper presents a study into using GenAI to enhance student learning of softwareengineering. It outlines the initiatives to introduce GenAI into a graduate-level softwareengineering course in software Verification and Validation (SV&V). The paper presents the educational goals, methodologies and findings of these endeavors in this course. The primary education goal of this course is that students have a solid understanding of principles and practices of software quality assurance and seek to introduce students to diverse techniques employed for SV&V. The study presented in this paper centers on the practical application of GenAI within the domain of testing strategies. The paper introduces the findings of an exercise where GenAI was used to apply testing strategies for unit testing. The exercise consisted of the use of GenAI in the development of unit tests for an algorithm. Rigorous assessments were conducted to gauge the effectiveness of the unit tests developed for validating the accurate implementation of the algorithm. This exploration shed light on the tangible impact of GenAI on the precision and efficiency of unit testing procedures. The findings underscore the significance of encouraging students to actively explore emerging trends and methodologies in the realm of software verification and validation. By incorporating GenAI into the educational framework, students not only gain insights into the capabilities and limitations of this technology but also foster a mindset of continuous learning in software quality assurance. The paper demonstrates that it is not sufficient to use the test cases developed by GenAI for software validation since test cases recommended by GenAI do not cover corner cases which causes gaps in coverage in unit testing. The majority of
Although the dual second-order generalized integral adaptive filter can achieve phase locking when the power grid voltage is unbalanced, it still cannot accurately lock in the presence of DC components and high-order ...
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SMT solvers are often used in the back end of different softwareengineering tools-e.g., program verifiers, test generators, or program synthesizers. There are a plethora of algorithmic techniques for solving SMT quer...
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ISBN:
(纸本)9781665457019
SMT solvers are often used in the back end of different softwareengineering tools-e.g., program verifiers, test generators, or program synthesizers. There are a plethora of algorithmic techniques for solving SMT queries. Among the available SMT solvers, each employs its own combination of algorithmic techniques that are optimized for different fragments of logics and problem types. The most efficient solver can change with small changes in the SMT query, which makes it nontrivial to decide which solver to use. Consequently, designers of softwareengineering tools often select a single solver, based on familiarity or convenience, and tailor their tool towards it. Choosing an SMT solver at design time misses the opportunity to optimize query solve times and, for tools where SMT solving is a bottleneck, the performance loss can be significant. In this work, we present Sibyl, an automated SMT selector based on graph neural networks (GNNs). Sibyl creates a graph representation of a given SMT query and uses GNNs to predict how each solver in a suite of SMT solvers would perform on said query. Sibyl learns to predict based on features of SMT queries that are specific to the population on which it is trained - avoiding the need for manual feature engineering. Once trained, Sibyl makes fast and accurate predictions which can substantially reduce the time needed to solve a set of SMT queries. We evaluate Sibyl in four scenarios in which SMT solvers are used: in competition, in a symbolic execution engine, in a bounded model checker, and in a program synthesis tool. We find that Sibyl improves upon the state of the art in nearly every case and provide evidence that it generalizes better than existing techniques. Further, we evaluate Sibyl's overhead and demonstrate that it has the potential to speedup a variety of different softwareengineering tools.
A common practice in software development is to include linters, static analysis tools that warn developers about potential issues in the code, in the software quality assurance process. Actionable warnings generated ...
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Detecting diseases in rice leaves is vital for ensuring agricultural productivity and food security. This paper introduces a highly efficient deep learning model designed for the automated identification of rice leaf ...
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Volunteers in citizen science projects contribute their labour to the activities of science, becoming involved in the advancement of science. With the advent of digital technologies, the involvement of non-scientists ...
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Light plays a vital role in vision either human or machine vision, the perceived color is always based on the lighting conditions of the surroundings. Researchers are working to enhance the color detection techniques ...
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The layout of software to optimize community offerings can be a challenging venture. No c the blessings of extended bandwidth and stepped forward overall performance, there are numerous principal exchange-offs to reme...
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Traceability is the ability to trace the usage of artifacts during the software lifecycle process. Though the benefits of establishing a traceability software system have been widely recognized, it is difficult to be ...
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In recent years, audio-visual speech recognition (AVSR) assistance systems have gained increasing attention from researchers as an important part of human-computer interaction (HCI). The objective of this paper is to ...
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ISBN:
(纸本)9783031779602;9783031779619
In recent years, audio-visual speech recognition (AVSR) assistance systems have gained increasing attention from researchers as an important part of human-computer interaction (HCI). The objective of this paper is to further advance the development of assistive technologies in the AVSR field by introducing a multi-modal OpenAV dataset, intended for state-of-the-art neural network model training. The OpenAV is designed to train AVSR models for assistance to persons without hands or with disabilities of their hands or arms in HCI. The dataset could also be useful for ordinary users at hands-free contactless HCI. The dataset currently includes the recordings in two languages (English and Russian) of 15 speakers with a minimum of 10 recording sessions for each. Along with this we provide a detailed description of the dataset and its collection pipeline. In addition, we evaluate state-of-the-art audio-visual (AV) speech recognition approach and present a baseline recognition results. We also describe the recording methodology, release the recording software to public, as well as open the access to the dataset https://***/OpenAV-dataset/.
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