Olimar Borges, Valentina Lenarduzzi, and Rafael Prikladnicki. 2022. Preliminary Insights to enable automation of the Software Development Process in Software StartUps: An Investigation Study from the use of Artificial Intelligence and Machine Learning. In 1st Conference on AI Engineering - Software Engineering for AI (CAIN’22), May 16–24, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3522664.3528610
Preliminary insights to enable automation of the software development process in software StartUps : an investigation study from the use of artificial intelligence and machine learning
|Author:||Borges, Olimar1; Lenarduzzi, Valentina2; Prikladnicki, Rafael1|
1PUCRS University, Porto Alegre, Brazil
2University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023032733304
Association for Computing Machinery,
|Publish Date:|| 2023-03-27
Artificial Intelligence (AI) and Machine Learning (ML) tools and techniques have increasingly effectively supported Software Engineering (SE) tasks, whether for requirements classification, software refactoring, defect prediction, and many others. In the context of software StartUps, where innovative and scalable software products are developed, dealing with the pressure of fast delivery of a working solution becomes a challenging factor. We aim to investigate AI and ML techniques used by SE practitioners and entrepreneurs to support their Software Development Processes (SDP) and thus enable their use by software StartUps. We seek to identify this information through the application of an online Survey instrument, mainly disseminated in Brazil and Finland. This preliminary study provides insights that can support improving the SDP in StartUps.
|Pages:||37 - 38|
CAIN '22: Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI
Conference on AI Engineering : Software Engineering for AI
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
This study was financed in part by the Coordenação de Aperfeiçoa-mento de Pessoal de Nivel Superior – Brasil (CAPES) – Code 001and partially funded by FAPERGS (17/2551-0001/205-4) and CNPq.
© ACM 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CAIN '22: Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI, http://dx.doi.org/10.1145/3522664.3528610.