Date of Award

2021

Document Type

Dissertation

Degree Name

Ph.D.

Department

Computer Science

First Advisor

Anneliese Amschler Andrews

Second Advisor

Scott Leutenegger

Third Advisor

Catherine Durso

Fourth Advisor

Krystyna Matusiak

Keywords

Change request, Effort, Estimation, Evolution, Legacy system, Prediction

Abstract

Prediction of software defects has been the focus of many researchers in empirical software engineering and software maintenance because of its significance in providing quality estimates from the project management perspective for an evolving legacy system. Software Reliability Growth Models (SRGM) have been used to predict future defects in a software release. Modern software engineering databases contain Change Requests (CR), which include both defects and other maintenance requests. Our goal is to use defect prediction methods to help predict CRs in an evolving legacy system.

Limited research has been done in defect prediction using curve-fitting methods evolving software systems, with one or more change-points. Curve-fitting approaches have been successfully used to select a fitted reliability model among candidate models for defect prediction. This work demonstrates the use of curve-fitting defect prediction methods to predict CRs. It focuses on providing a curve-fit solution that deals with evolutionary software changes but yet considers long-term prediction of data in the full release. We compare three curve-fit solutions in terms of their ability to predict CRs. Our data show that the Time Transformation approach (TT) provides more accurate CR predictions and fewer under-predicted Change Requests than the other curve-fitting methods.

In addition to CR prediction, we investigated the possibility of estimating effort as well. We found Lines of Code (added, deleted, modified, and auto-generated) associated with CRs do not necessarily predict the actual effort spent on CR resolution.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Provenance

Received from ProQuest

Rights holder

Lamees Abdullah Alhazzaa

File size

272 pgs

File format

application/pdf

Language

en

Discipline

Computer science

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