Detecting multi-type self-admitted technical debt with generative adversarial network-based neural networks

Yu, Jiaojiao; Zhou, Xu; Liu, Xiao; Liu, Jin; Xie, Zheng; Zhao, Kunsong

doi:10.1016/j.infsof.2023.107190

Cited by 4 publications

(3 citation statements)

References 42 publications

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“…DL algorithms primarily employ supervised learning methods, particularly deep neural networks, enabling learning from unstructured data. Gen AI is a subset of DL that helps in emulating human interaction through sophisticated models like Generative Adversarial Networks (GANs) [40] and Generative Pre-trained Transformers (GPTs). GANs engage in a perpetual learning cycle between generator and discriminator models to enhance their capabilities until they become indistinguishable from authentic examples.…”

Section: Ai Algorithms' Statistics For Autonomous Vehiclesmentioning

confidence: 99%

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

Garikapati,

Shetiya

2024

BDCC

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The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of artificial intelligence (AI), propelling vehicles into realms of unprecedented autonomy. Commencing with an overview of the current industry landscape with respect to Operational Design Domain (ODD), this paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing various challenges such as safety, security, privacy, and ethical considerations in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI algorithms, and discussing the automation of key tasks and the software package size at each level. Overall, the paper provides a comprehensive analysis of the current industry landscape, focusing on several critical aspects.

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Section: Ai Algorithms' Statistics For Autonomous Vehiclesmentioning

confidence: 99%

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

Garikapati,

Shetiya

2024

BDCC

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“…Rules and regulations will address cybersecurity concerns and data privation, which will protect information or all persons thus mitigating cyberattacks. Clearly structured legal frameworks will determine liability in situations which involve self-driving vehicles and also take care of ethical decision-making (Yu, et al, 2023). As you can see, the future holds numerous potential for transforming transportation, eISSN1303-5150 www.neuroquantology.com 7 generating new jobs, and enhancing safety.…”

Section: Regulatorymentioning

confidence: 99%

Balancing Safety and Decision-Making Algorithms in AI-Driven Autonomous Vehicles

2024

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“…Neural Networks based on Generative Adversarial Networks are used in ref. [29] to detect different typologies of SATD and, in particular, to solve the imbalance of sampled data (enhancing the features containing few number data). In ref.…”

Section: Satd Detection Strategies and Techniquesmentioning

confidence: 99%

Investigation on Self-Admitted Technical Debt in Open-Source Blockchain Projects

et al. 2023

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Technical debt refers to decisions made during the design and development of software that postpone the resolution of technical problems or the enhancement of the software’s features to a later date. If not properly managed, technical debt can put long-term software quality and maintainability at risk. Self-admitted technical debt is defined as the addition of specific comments to source code as a result of conscious and deliberate decisions to accumulate technical debt. In this paper, we will look at the presence of self-admitted technical debt in open-source blockchain projects, which are characterized by the use of a relatively novel technology and the need to generate trust. The self-admitted technical debt was analyzed using NLP techniques for the classification of comments extracted from the source code of ten projects chosen based on capitalization and popularity. The analysis of self-admitted technical debt in blockchain projects was compared with the results of previous non-blockchain open-source project analyses. The findings show that self-admitted design technical debt outnumbers requirement technical debt in blockchain projects. The analysis discovered that some projects had a low percentage of self-admitted technical debt in the comments but a high percentage of source code files with debt. In addition, self-admitted technical debt is on average more prevalent in blockchain projects and more equally distributed than in reference Java projects.If not managed, the relatively high presence of detected technical debt in blockchain projects could represent a threat to the needed trust between the blockchain system and the users. Blockchain projects development teams could benefit from self-admitted technical debt detection for targeted technical debt management.

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Detecting multi-type self-admitted technical debt with generative adversarial network-based neural networks

Cited by 4 publications

References 42 publications

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

Balancing Safety and Decision-Making Algorithms in AI-Driven Autonomous Vehicles

Investigation on Self-Admitted Technical Debt in Open-Source Blockchain Projects

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