Degree Type

Thesis

Date of Award

2019

Degree Name

Master of Science

Department

Computer Science

Major

Computer Science

First Advisor

Robyn R. Lutz

Abstract

As a software product line evolves, it typically introduces new features and includes new products over time. A feature is a unit of functionality. Unwanted feature interactions, wherein one feature hinders another feature’s operation, are a significant problem, especially as large software product lines evolve. This can lead to failures, performance degradation, and hazardous states in a new product. Software product line developers currently identify new, unwanted feature interactions primarily in the testing of each new product. This incurs significant costs, comes late in development, and does not exploit the knowledge of prior feature interactions within a product line. The contribution of this thesis is to leverage knowledge of prior feature interactions in a software product line, together with similarity measures between the features in known feature interactions and the new features, in order to detect similar feature interactions in a new product much earlier in the development process. Results obtained from application of our approach to three small software product lines from the literature showed an accuracy of 69% to 73% and coverage of 71% to 82% in detecting feature interactions. This indicates that the use of similarity measures between features in a software product line can help detect potential feature interactions in the design phase of a newly added product.

Copyright Owner

Seyedehzahra Khoshmanesh

Language

en

File Format

application/pdf

File Size

43 pages

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