Projects per year
Abstract
The software engineering community is rapidly adopting machine learning for transitioning modern-day software towards highly intelligent and self-learning systems. However, the software engineering community is still discovering new ways how machine learning can offer help for various software development life cycle stages. In this article, we present a study on the use of machine learning across various software development life cycle stages. The overall aim of this article is to investigate the relationship between software development life cycle stages, and machine learning tools, techniques, and types. We attempt a holistic investigation in part to answer the question of whether machine learning favors certain stages and/or certain techniques.
| Original language | English |
|---|---|
| Pages (from-to) | 140896-140920 |
| Number of pages | 24 |
| Journal | IEEE Access |
| Volume | 9 |
| DOIs | |
| Publication status | Published - Sept 2021 |
Fields of science
- 102 Computer Sciences
- 102022 Software development
JKU Focus areas
- Digital Transformation
Projects
- 2 Finished
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CROSS-CHANGE - How Do Engineers Coordinate and Execute Crossdisciplinary Changes in Software-Intensive Mechatronical Systems?
Ashraf, U. (Researcher) & Mayr-Dorn, C. (PI)
15.08.2020 → 14.08.2022
Project: Funded research › Federal / regional / local authorities
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Pro2Future - Products and Production Systems of the Future
Egyed, A. (Researcher), Küng, J. (Researcher), Miethlinger, J. (Researcher), Müller, A. (Researcher), Schlacher, K. (Researcher), Streit, M. (Researcher) & Ferscha, A. (PI)
01.04.2017 → 31.03.2025
Project: Funded research › FFG - Austrian Research Promotion Agency