Work Description

Title: A Security-Aware Refactoring Approach Open Access Deposited

h
Attribute Value
Methodology
  • We investigated the possible impact of improving different quality attributes (e.g. reusability, extendibility, etc.), from the QMOOD model, effectiveness on a set of 8 security metrics defined in the literature related to the data access. We also studied the impact of different refactorings on these static security metrics. Then, we proposed a multi-objective refactoring recommendation approach to find a balance between quality attributes and security based on the correlation results to guide the search. We evaluated our tool on 30 open source projects. We also collected the practitioner perceptions on the refactorings recommended by our tool in terms of the possible impact on both security and other quality attributes. Our results confirm that developers need to make trade-offs between security and other qualities when refactoring software systems due to the negative correlations between them.
Description
  • Data about the evaluation of the refactorings impact on security.
Creator
Depositor
  • marouane@umich.edu
Contact information
Discipline
Date coverage
  • 2019-11-01
Resource type
Last modified
  • 03/26/2020
Published
  • 03/26/2020
DOI
  • https://doi.org/10.7302/0bgn-vt27
License
To Cite this Work:
Abid, C., Kessentini, M., Alizadeh, V., Dhaouadi, M., Kazman, R. (2020). A Security-Aware Refactoring Approach [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/0bgn-vt27

Relationships

This work is not a member of any user collections.

Files (Count: 2; Size: 20.5 MB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
Refactoring_for_Security_Data.rar 2020-03-24 2020-03-24 20.5 MB Open Access
Participant_consent_form_templat....docx 2020-03-26 2020-03-26 17.4 KB Open Access

Download All Files (To download individual files, select them in the “Files” panel above)

Best for data sets < 3 GB. Downloads all files plus metadata into a zip file.

Files are ready   Download Data from Globus
Best for data sets > 3 GB. Globus is the platform Deep Blue Data uses to make large data sets available.   More about Globus

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.