An Exploration of Agile Methods in the Automotive Industry: Benefits, Challenges and Opportunities
September 19, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mehrnoosh Askarpour, Sahar Kokaly, Ramesh S
arXiv ID
2409.12676
Category
cs.SE: Software Engineering
Citations
1
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Agile methodologies have gained significant traction in the software development industry, promising increased flexibility and responsiveness to changing requirements. However, their applicability to safety-critical systems, particularly in the automotive sector, remains a topic of debate. This paper examines the benefits and challenges of implementing agile methods in the automotive industry through a comprehensive review of relevant literature and case studies. Our findings highlight the potential advantages of agile approaches, such as improved collaboration and faster time-to-market, as well as the inherent challenges, including safety compliance and cultural resistance. By synthesizing existing research and practical insights, this paper aims to provide an understanding of the role of agile methods in shaping the future of automotive software development.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted