Zobrazeno 1 - 10
of 446
pro vyhledávání: '"FELDT, ROBERT"'
Autor:
Tavantzis, Theocharis, Feldt, Robert
As Artificial Intelligence (AI) becomes integral to software development, understanding the social and cooperative dynamics that affect AI-driven organizational change is important. Yet, despite AI's rapid progress and influence, the human and cooper
Externí odkaz:
http://arxiv.org/abs/2411.08693
Adaptive Random Testing (ART) has faced criticism, particularly for its computational inefficiency, as highlighted by Arcuri and Briand. Their analysis clarified how ART requires a quadratic number of distance computations as the number of test execu
Externí odkaz:
http://arxiv.org/abs/2410.17907
Autor:
Khoee, Arsham Gholamzadeh, Yu, Yinan, Feldt, Robert, Freimanis, Andris, Rhodin, Patrick Andersson, Parthasarathy, Dhasarathy
Traditional methods for making software deployment decisions in the automotive industry typically rely on manual analysis of tabular software test data. These methods often lead to higher costs and delays in the software release cycle due to their la
Externí odkaz:
http://arxiv.org/abs/2408.09785
Autor:
Gren, Lucas, Feldt, Robert
This position paper explores the intricate relationship between social psychology and secure software engineering, underscoring the vital role social psychology plays in the realm of engineering secure software systems. Beyond a mere technical endeav
Externí odkaz:
http://arxiv.org/abs/2407.00323
Publikováno v:
Proc. 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), pp. 66-69
Test case prioritisation (TCP) is a critical task in regression testing to ensure quality as software evolves. Machine learning has become a common way to achieve it. In particular, learning-to-rank (LTR) algorithms provide an effective method of ord
Externí odkaz:
http://arxiv.org/abs/2405.13786
Autor:
Feldt, Robert, Coppola, Riccardo
Large Language Models (LLMs) are becoming key in automating and assisting various software development tasks, including text-based tasks in requirements engineering but also in coding. Typically, these models are used to automate small portions of ex
Externí odkaz:
http://arxiv.org/abs/2405.04236
Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack performance when faced with out-of-distribution (OOD) data, a common scenario due to the inevitable domain shifts in real-world applications. This limitation stems
Externí odkaz:
http://arxiv.org/abs/2404.02785
GUI testing checks if a software system behaves as expected when users interact with its graphical interface, e.g., testing specific functionality or validating relevant use case scenarios. Currently, deciding what to test at this high level is a man
Externí odkaz:
http://arxiv.org/abs/2311.08649
Web-based test automation heavily relies on accurately finding web elements. Traditional methods compare attributes but don't grasp the context and meaning of elements and words. The emergence of Large Language Models (LLMs) like GPT-4, which can sho
Externí odkaz:
http://arxiv.org/abs/2310.02046
Publikováno v:
Empir. Softw. Eng. 21(6): 2324-2365 (2016)
Software Engineering activities are information intensive. Research proposes Information Retrieval (IR) techniques to support engineers in their daily tasks, such as establishing and maintaining traceability links, fault identification, and software
Externí odkaz:
http://arxiv.org/abs/2308.11750