Using Machine Learning to Detect Architectural Integrity Violations Associated with Bugs

Recent years have seen a surge of research into the impact architectural relations among files have on software maintainability and file bug-proneness. In particular, a set of rules for determining recurring design flaws associated with bugs has been proposed. In the present thesis we have investigated if machine learning can be used to advance the research on software architecture analysis and, specifically, on pinpointing architectural issues which may be the root causes of elevated bug- and change-proneness. In the case study of the Tiki open source project, we have been able to replicate three of the six known types of such architectural integrity violations and discover one new type, the Reverse Unstable Interface pattern. We have also demonstrated that one has to consider a mixture of local and global relationships in architectural bug detection and, contrary to common practice, should not disregard occasional co-changing of the two files as noise.

Name thesis:degree_name

Master of Applied Science (M.App.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Technology Innovation Management

Grantor dc:publisher

Carleton University

Year dc:date.issued

2021

Author dc:creator

• Zakurdaeva, Alla Vitalevna

Statement dc:rights

• Copyright © 2021 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2021-14643

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/42561