Bachelor and Master Theses

Title: Quality property ontology population through text/data mining
Level: Basic or Advanced (contact supervisor)
Description: Today, software is everywhere. Such a prevalence calls for the ability to develop high quality software at a faster pace. An important step in this direction requires that software engineers better comprehend which quality properties should be evaluated and how. However, the current body of knowledge on quality properties is huge, which makes it challenging for software engineers to be aware of the most suitable methods to evaluate a given property in their current context of work. To help organizing the knowledge on quality properties, an ontology of property models was proposed by Sentilles et al[1]. However, the ontology still needs to be populated with appropriate and accurate information. The goal of this thesis is to investigate how text mining, data mining and deep learning can be used to retrieve relevant data for the ontology.

The thesis is suitable for 1 or 2 students. The scope of the work will be limited to a predefined subset of properties in line with the student's profile.

The thesis work will on a high level consist of:
- State-of-the-art and on text mining, data mining, deep learning, and quality properties to identify the most suitable technique to apply in the context of this work
- Definition of a protocol on how the selected technique will be used (e.g., manual training, tagging, syntactic parsing, disambiguation, sources of information, features…)
- Evaluation of the quality of the retrieved data
- Classification of the retrieved data according to the property model ontology

Proposed: 2017-10-24
Prerequisites: To be successful in this thesis work the candidate(s) would need the following: (1)Good English proficiency and interest in scientific literature; (2) Knowledge on software quality and quality assurance/V&V. Additionally, previous experience on data mining, text mining, deep learning, natural language processing is an advantage.
IDT supervisor: Séverine Sentilles, +46-21-10 70 38

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