Hierons, R.M. orcid.org/0000-0002-4771-1446, Li, M., Liu, X. et al. (2 more authors) (2016) SIP: Optimal Product Selection from Feature Models Using Many-Objective Evolutionary Optimization. ACM Transactions on Software Engineering and Methodology, 25 (2). 17. ISSN 1049-331X
Abstract
A feature model specifies the sets of features that define valid products in a software product line. Recent work has considered the problem of choosing optimal products from a feature model based on a set of user preferences, with this being represented as a many-objective optimization problem. This problem has been found to be difficult for a purely search-based approach, leading to classical many-objective optimization algorithms being enhanced either by adding in a valid product as a seed or by introducing additional mutation and replacement operators that use an SAT solver. In this article, we instead enhance the search in two ways: by providing a novel representation and by optimizing first on the number of constraints that hold and only then on the other objectives. In the evaluation, we also used feature models with realistic attributes, in contrast to previous work that used randomly generated attribute values. The results of experiments were promising, with the proposed (SIP) method returning valid products with six published feature models and a randomly generated feature model with 10,000 features. For the model with 10,000 features, the search took only a few minutes.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2016 ACM. This is an author-produced version of a paper subsequently published in ACM Transactions on Software Engineering and Methodology. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Jun 2019 15:08 |
Last Modified: | 19 Jun 2019 15:08 |
Status: | Published |
Publisher: | ACM |
Refereed: | Yes |
Identification Number: | 10.1145/2897760 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:147601 |