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pro vyhledávání: '"Aleti, A"'
Finetuning language models (LMs) is crucial for adapting the models to downstream data and tasks. However, full finetuning is usually costly. Existing work, such as parameter-efficient finetuning (PEFT), often focuses on \textit{how to finetune} but
Externí odkaz:
http://arxiv.org/abs/2406.11753
Autonomous Vehicles (AVs) are prone to revolutionise the transportation industry. However, they must be thoroughly tested to avoid safety violations. Simulation testing plays a crucial role in finding safety violations of Automated Driving Systems (A
Externí odkaz:
http://arxiv.org/abs/2405.03326
Context: Test-driven development (TDD) is a widely employed software development practice that involves developing test cases based on requirements prior to writing the code. Although various methods for automated test case generation have been propo
Externí odkaz:
http://arxiv.org/abs/2402.11910
Understanding the latent space of language models (LMs) is important for improving the performance and interpretability of LMs. Existing analyses often fail to provide insights that take advantage of the semantic properties of language models and oft
Externí odkaz:
http://arxiv.org/abs/2401.16184
Large Language Models (LLMs) have already gained widespread adoption in software engineering, particularly in code generation tasks. However, updating these models with new knowledge can be prohibitively expensive, yet it is essential to maximize the
Externí odkaz:
http://arxiv.org/abs/2312.05356
Publikováno v:
IEEE Transactions on Software Engineering, 49(4), 2642-2660 (2022)
Search-based software testing (SBST) is now a mature area, with numerous techniques developed to tackle the challenging task of software testing. SBST techniques have shown promising results and have been successfully applied in the industry to autom
Externí odkaz:
http://arxiv.org/abs/2312.02392
Autor:
Neelofar, Neelofar, Aleti, Aldeida
AI-powered systems have gained widespread popularity in various domains, including Autonomous Vehicles (AVs). However, ensuring their reliability and safety is challenging due to their complex nature. Conventional test adequacy metrics, designed to e
Externí odkaz:
http://arxiv.org/abs/2311.08049
Autor:
Aleti, Aldeida
Software Testing is a well-established area in software engineering, encompassing various techniques and methodologies to ensure the quality and reliability of software systems. However, with the advent of generative artificial intelligence (GenAI) s
Externí odkaz:
http://arxiv.org/abs/2309.03554
Software remodularization through clustering is a common practice to improve internal software quality. However, the true benefit of software clustering is only realized if developers follow through with the recommended refactoring suggestions, which
Externí odkaz:
http://arxiv.org/abs/2303.06283
Test case generation is an important activity, yet a time-consuming and laborious task. Recently, AthenaTest -- a deep learning approach for generating unit test cases -- is proposed. However, AthenaTest can generate less than one-fifth of the test c
Externí odkaz:
http://arxiv.org/abs/2302.10352