This repository contains a aplication of Text Mining technique in DevOps Challenges and Recommendations to teach related in the paper Challenges and Recommendations in DevOps Education: A Systematic Literature Review.
In previous work, It was realized a systematic literature review where DevOps challenges and recommendations was found. Also, these challenges and recommendations was manually thematized in 7 themes: assessment, curriculum, tools, classes preparation, devops concepts, environment setup and pedagogy. Therefore, as work propose, I wait to realize a thematization of theses challenges and recommendations using some models of classification.
| Theme | Description |
|---|---|
| Curriculum | The Curriculum theme is related to the content, hourly load and the number of subjects needed to use DevOps. The relationship between the DevOps discipline and the prerequisite disciplines of this subject is also considered, as well as the interaction with the others of a software engineering course. |
| Tools | The Tool theme includes software used to operationalize DevOps practices. They can be those widely used in the industry or those created with a teaching standard. |
| Assessment | The Assessment theme includes necessary and permanent didactic tasks in the teacher's work with the objective of diagnosing the learning situation of each student, in relation to the curriculum. |
| Classes preparation | The topic Classes preparation refers to the planning of the course, including, for example, a research of reference material and preparation of classes. |
| Devops concepts | The DevOps Concepts are related to its main foundations, techniques and mentality (culture). |
| Environment setup | The Environment setup theme refers to the preparatory activities of the environments used in the practices of students' exercises and projects. |
| Pedagogy | References to the subject of Pedagogy are related to a set of techniques, principles, methods, and strategy for education and teaching. |
There are 2 dataset file (in Weka format ): challenges_dataset.arff and recommendations_dataset.arff. In the challenges_dataset.arff, there are 73 instances and 2 atributtes: challenge and theme. In the recommendations_dataset.arff, there are 85 instances and 2 atributtes: recommendation and theme.