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Predicting the time-to-deliver of software changes

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thesis
posted on 24.05.2021, 06:53 by Sokratis Tsakiltsidis
In this thesis we examine the application of survival analysis on time-to-deliver data. Successful prediction of the time necessary to deliver a new feature or fix a reported defect can assist in various phases and aspects of software development. We identify and try to overcome limitations when dealing with time-to-event data. Our proposed methodological framework includes use of survival analysis, utilization of incomplete information that might be available as censored data, and incorporation of random-effects through mixed-effects models for identification of hierarchical/clustered data within our dataset. We explore and experiment with a dataset from a large scale commercial software over a twelve year period of time. We show that we can successfully implement survival analysis, and that incorporation of random-effects provides a considerable advantage, however, incorporation of censored information is not proven to be advantageous in this case.

History

Language

eng

Degree

Master of Science

Program

Computer Science

Granting Institution

Ryerson University

LAC Thesis Type

Thesis

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