Issue Resolution Time Prediction Using Deep Learning Techniques
Name
Atakan Arikan
Abstract
Issue resolution time prediction has a large importance in software projects since planning of these projects are typically hard. Especially in the agile practices, such as sprint planning, to be able to predict correctly how long it would take to resolve an issue, holds the power to plan correctly.
This thesis focuses on the state of the art approaches to this problem and study their performances. On top of that we discuss how can one structure and implement a deep learning algorithm to solve issue resolution time prediction problem. Afterwards, we compare and discuss the results of the applied deep learning technique with the current state of the art.
The data used for this study contains around 700,000 issues. This data is gathered collectively from the previous studies in this field. By using the already existing data, we plan to validate the existing results and build on top of the current baseline.
This thesis focuses on the state of the art approaches to this problem and study their performances. On top of that we discuss how can one structure and implement a deep learning algorithm to solve issue resolution time prediction problem. Afterwards, we compare and discuss the results of the applied deep learning technique with the current state of the art.
The data used for this study contains around 700,000 issues. This data is gathered collectively from the previous studies in this field. By using the already existing data, we plan to validate the existing results and build on top of the current baseline.
Graduation Thesis language
English
Graduation Thesis type
Master - Software Engineering
Supervisor(s)
Dietmar Pfahl
Defence year
2019