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of 634
pro vyhledávání: '"Hassan, Ahmed E."'
The proliferation of open Pre-trained Language Models (PTLMs) on model registry platforms like Hugging Face (HF) presents both opportunities and challenges for companies building products around them. Similar to traditional software dependencies, PTL
Externí odkaz:
http://arxiv.org/abs/2409.10472
Background: Data quality is vital in software analytics, particularly for machine learning (ML) applications like software defect prediction (SDP). Despite the widespread use of ML in software engineering, the effect of data quality antipatterns on t
Externí odkaz:
http://arxiv.org/abs/2408.12560
Foundation models (FM), such as large language models (LLMs), which are large-scale machine learning (ML) models, have demonstrated remarkable adaptability in various downstream software engineering (SE) tasks, such as code completion, code understan
Externí odkaz:
http://arxiv.org/abs/2407.04065
Predicting potential long-time contributors (LTCs) early allows project maintainers to effectively allocate resources and mentoring to enhance their development and retention. Mapping programming language expertise to developers and characterizing pr
Externí odkaz:
http://arxiv.org/abs/2405.13852
A long continuous integration (CI) build forces developers to wait for CI feedback before starting subsequent development activities, leading to time wasted. In addition to a variety of build scheduling and test selection heuristics studied in the pa
Externí odkaz:
http://arxiv.org/abs/2405.00796
Large Language Models (LLMs) have significantly advanced natural language processing (NLP) tasks but also pose ethical and societal risks due to their propensity to generate harmful content. To address this, various approaches have been developed to
Externí odkaz:
http://arxiv.org/abs/2404.19048
The advent of Foundation Models (FMs) and AI-powered copilots has transformed the landscape of software development, offering unprecedented code completion capabilities and enhancing developer productivity. However, the current task-driven nature of
Externí odkaz:
http://arxiv.org/abs/2404.10225
This paper investigates the complexities of integrating Large Language Models (LLMs) into software products, with a focus on the challenges encountered for determining their readiness for release. Our systematic review of grey literature identifies c
Externí odkaz:
http://arxiv.org/abs/2403.18958
Deciding what combination of operators to use across the Edge AI tiers to achieve specific latency and model performance requirements is an open question for MLOps engineers. This study aims to empirically assess the accuracy vs inference time trade-
Externí odkaz:
http://arxiv.org/abs/2403.17154
Autor:
Hao, Huizi, Hasan, Kazi Amit, Qin, Hong, Macedo, Marcos, Tian, Yuan, Ding, Steven H. H., Hassan, Ahmed E.
ChatGPT has significantly impacted software development practices, providing substantial assistance to developers in a variety of tasks, including coding, testing, and debugging. Despite its widespread adoption, the impact of ChatGPT as an assistant
Externí odkaz:
http://arxiv.org/abs/2403.10468