How do Agile Software Startups deal with uncertainties by Covid-19 pandemic?
June 24, 2020 Β· Declared Dead Β· π International Journal of Software Engineering & Applications
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Rafael da Camara, Marcelo Marinho, Suzana Sampaio, Saulo Cadete
arXiv ID
2006.13715
Category
cs.SE: Software Engineering
Citations
42
Venue
International Journal of Software Engineering & Applications
Last Checked
4 months ago
Abstract
The dissipation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already taken on pandemic proportions, affecting over 100 countries in a couple of weeks. The evolution of the disease and its economic impact is highly uncertain, which brings challenges for newly created software companies. Software startups are companies that create innovative software products and services in a dynamic and fast-growing market. Agile Software Methods aims to enable startups in responding to uncertainty caused by Covid-19. This paper investigates the impact of Covid-19 in a real software startup context to understand how they have reacted against uncertainties caused by Covid-19. As a research methodology, action research within Di2Win, a Brazilian software startup, has been applied. The study was carried out throughout six sprints, during the quarantine. Practices employed to mitigate threats while simultaneously allowing teams to remain open to opportunities and challenges are detailed. This paper shares lessons learned that could help agile software startups improve their way of work in an uncertain environment caused by the Covid-19 pandemic.
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