Bug-fix time prediction with machine learning
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Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
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Bug-fix time is an essential part of any bug report. Accurate bug-fix time predictions
help in many project management related issues. This thesis examines, whether
it is possibly to find out a suitable machine learning model for making accurate
bug fixing time predictions based on bug report data. Project is implemented for
ATR Soft Oy and the needed data is fetched from ATR Soft work hour registration
system and open-source bug tracking system.
Thesis begins with fundamentals of machine learning, while also covering
popularly used machine learning algorithms. Similarly, natural language processing
basic concepts are introduced. Literature review is conducted for finding important
machine learning characteristics from previous bug-fix time estimation papers.
These found characteristics are then utilized in the machine learning process for
creating a machine learning model, which predicts bug fixing times. Suitable
categories for predictions are formed and data preprocessing is performed. After
the initial results, machine learning model is tuned for final results of the project.
Based on the evaluation metrics, performance of the machine learning model
is not good enough to be put into real use. However, model prediction capability
could be improved with some modifications concerning the data, preprocessing
phase and changing the predicted categories.