Towards machine learning guided by best practices
- URL: http://arxiv.org/abs/2305.00233v2
- Date: Sat, 6 May 2023 13:04:35 GMT
- Title: Towards machine learning guided by best practices
- Authors: Anamaria Mojica-Hanke
- Abstract summary: Machine learning (ML) is being used in software systems with multiple application fields, from medicine to software engineering (SE)
This thesis aims to answer research questions that help to understand the practices used and discussed by practitioners and researchers in the SE community.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Nowadays, machine learning (ML) is being used in software systems with
multiple application fields, from medicine to software engineering (SE). On the
one hand, the popularity of ML in the industry can be seen in the statistics
showing its growth and adoption. On the other hand, its popularity can also be
seen in research, particularly in SE, where not only have multiple studies been
published in SE conferences and journals but also in the multiple workshops and
co-located conferences in software engineering conferences. At the same time,
researchers and practitioners have shown that machine learning has some
particular challenges and pitfalls. In particular, research has shown that
ML-enabled systems have a different development process than traditional SE,
which also describes some of the challenges of ML applications. In order to
mitigate some of the identified challenges and pitfalls, white and gray
literature has proposed a set of recommendations based on their own experiences
and focused on their domain (e.g., biomechanics), but for the best of our
knowledge, there is no guideline focused on the SE community. This thesis aims
to reduce this gap by answering research questions that help to understand the
practices used and discussed by practitioners and researchers in the SE
community by analyzing possible sources of practices such as question and
answer communities and also previous research studies to present a set of
practices with an SE perspective.
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