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pro vyhledávání: '"Majdinasab, Vahid"'
Machine learning models trained on code and related artifacts offer valuable support for software maintenance but suffer from interpretability issues due to their complex internal variables. These concerns are particularly significant in safety-criti
Externí odkaz:
http://arxiv.org/abs/2407.08890
Code auditing ensures that the developed code adheres to standards, regulations, and copyright protection by verifying that it does not contain code from protected sources. The recent advent of Large Language Models (LLMs) as coding assistants in the
Externí odkaz:
http://arxiv.org/abs/2402.09299
Autor:
Majdinasab, Vahid, Bishop, Michael Joshua, Rasheed, Shawn, Moradidakhel, Arghavan, Tahir, Amjed, Khomh, Foutse
AI-powered code generation models have been developing rapidly, allowing developers to expedite code generation and thus improve their productivity. These models are trained on large corpora of code (primarily sourced from public repositories), which
Externí odkaz:
http://arxiv.org/abs/2311.11177
Autor:
Dakhel, Arghavan Moradi, Nikanjam, Amin, Majdinasab, Vahid, Khomh, Foutse, Desmarais, Michel C.
One of the critical phases in software development is software testing. Testing helps with identifying potential bugs and reducing maintenance costs. The goal of automated test generation tools is to ease the development of tests by suggesting effici
Externí odkaz:
http://arxiv.org/abs/2308.16557
Autor:
Ho, Sharon Chee Yin, Majdinasab, Vahid, Islam, Mohayeminul, Costa, Diego Elias, Shihab, Emad, Khomh, Foutse, Nadi, Sarah, Raza, Muhammad
Software systems are increasingly relying on deep learning components, due to their remarkable capability of identifying complex data patterns and powering intelligent behaviour. A core enabler of this change in software development is the availabili
Externí odkaz:
http://arxiv.org/abs/2307.13777
Testing Deep Learning (DL) systems is a complex task as they do not behave like traditional systems would, notably because of their stochastic nature. Nonetheless, being able to adapt existing testing techniques such as Mutation Testing (MT) to DL se
Externí odkaz:
http://arxiv.org/abs/2301.05651
Autor:
Dakhel, Arghavan Moradi, Majdinasab, Vahid, Nikanjam, Amin, Khomh, Foutse, Desmarais, Michel C., Ming, Zhen, Jiang
Automatic program synthesis is a long-lasting dream in software engineering. Recently, a promising Deep Learning (DL) based solution, called Copilot, has been proposed by OpenAI and Microsoft as an industrial product. Although some studies evaluate t
Externí odkaz:
http://arxiv.org/abs/2206.15331
Autor:
Dakhel, Arghavan Moradi, Nikanjam, Amin, Majdinasab, Vahid, Khomh, Foutse, Desmarais, Michel C.
Publikováno v:
In Information and Software Technology July 2024 171
Autor:
Moradi Dakhel, Arghavan, Majdinasab, Vahid, Nikanjam, Amin, Khomh, Foutse, Desmarais, Michel C., Jiang, Zhen Ming (Jack)
Publikováno v:
In The Journal of Systems & Software September 2023 203
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