dc.contributor.advisor | Kagdi, Huzefa Hatimbhai | |
dc.contributor.author | Zanjani, Motahareh Bahrami | |
dc.date.accessioned | 2018-01-30T17:25:33Z | |
dc.date.available | 2018-01-30T17:25:33Z | |
dc.date.issued | 2017-05 | |
dc.identifier.other | d17021 | |
dc.identifier.uri | http://hdl.handle.net/10057/14512 | |
dc.description | Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science | |
dc.description.abstract | The conducted research is within the realm of software maintenance and evolution. Human
reliance and dominance are ubiquitous in sustaining a high-quality large software system. Automatically
assigning the right solution providers to the maintenance task at hand is arguably as
important as providing the right tool support for it, especially in the far too commonly found state
of inadequate or obsolete documentation of large-scale software systems. Several maintenance
tasks related to assignment and assistance to software developers and reviewers are addressed, and
multiple solutions are presented. The key insight behind the presented solutions is the analysis
and use of micro-levels of human-to-code and human-to-human interactions. The formulated methodology
consists of the restrained use of machine learning techniques, lightweight source code
analysis, and mathematical quantification of different markers of developer and reviewer expertise
from these micro interactions.
In this dissertation, we first present the automated solutions for Software Change Impact
Analysis based on interaction and code review activities of developers. Then we provide explanation
for two separate developer expertise models which use the micro-levels of human-to-code and
human-to-human interactions from the previous code review and interaction activities of developers.
Next, we present a reviewer expertise model based on code review activities of developers
and show how this expertise model can be used for Code Reviewer Recommendation. At the end
we examine the influential features that characterize the acceptance probability of a submitted patch
(implemented code change) by developers. We present a predictive model that classifies whether a
patch will be accepted or not as soon as it is submitted for code review in order to assist developers
and reviewers in prioritizing and focusing their efforts.
A rigorous empirical validation on large open source and commercial systems shows that
the solutions based on the presented methodology outperform several existing solutions. The quantitative
gains of our solutions across a spectrum of evaluation metrics along with their statistical
significance are reported. | |
dc.format.extent | xv, 199 pages | |
dc.language.iso | en_US | |
dc.publisher | Wichita State University | |
dc.rights | Copyright 2017 by Motahareh Bahrami Zanjani
All Rights Reserved | |
dc.subject.lcsh | Electronic dissertations | |
dc.title | Effective assignment and assistance to software developers and reviewers | |
dc.type | Dissertation | |