Breathing ontological knowledge into feature model synthesis: an empirical study.
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| Title: | Breathing ontological knowledge into feature model synthesis: an empirical study. |
|---|---|
| Authors: | Bécan, Guillaume1 guillaume.becan@inria.fr, Acher, Mathieu1 mathieu.acher@inria.fr, Baudry, Benoit1 benoit.baudry@inria.fr, Nasr, Sana1 sana.ben-nasr@inria.fr |
| Source: | Empirical Software Engineering. Aug2016, Vol. 21 Issue 4, p1794-1841. 48p. |
| Subjects: | Software product line engineering, Reverse engineering, Software refactoring, Ontology, Reasoning |
| Abstract: | Feature Models (FMs) are a popular formalism for modeling and reasoning about the configurations of a software product line. As the manual construction of an FM is time-consuming and error-prone, management operations have been developed for reverse engineering, merging, slicing, or refactoring FMs from a set of configurations/dependencies. Yet the synthesis of meaningless ontological relations in the FM - as defined by its feature hierarchy and feature groups - may arise and cause severe difficulties when reading, maintaining or exploiting it. Numerous synthesis techniques and tools have been proposed, but only a few consider both configuration and ontological semantics of an FM. There are also few empirical studies investigating ontological aspects when synthesizing FMs. In this article, we define a generic, ontologic-aware synthesis procedure that computes the likely siblings or parent candidates for a given feature. We develop six heuristics for clustering and weighting the logical, syntactical and semantical relationships between feature names. We then perform an empirical evaluation on hundreds of FMs, coming from the SPLOT repository and Wikipedia. We provide evidence that a fully automated synthesis (i.e., without any user intervention) is likely to produce FMs far from the ground truths. As the role of the user is crucial, we empirically analyze the strengths and weaknesses of heuristics for computing ranking lists and different kinds of clusters. We show that a hybrid approach mixing logical and ontological techniques outperforms state-of-the-art solutions. We believe our approach, environment, and empirical results support researchers and practitioners working on reverse engineering and management of FMs. [ABSTRACT FROM AUTHOR] |
| Copyright of Empirical Software Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Breathing ontological knowledge into feature model synthesis: an empirical study. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bécan%2C+Guillaume%22">Bécan, Guillaume</searchLink><relatesTo>1</relatesTo><i> guillaume.becan@inria.fr</i><br /><searchLink fieldCode="AR" term="%22Acher%2C+Mathieu%22">Acher, Mathieu</searchLink><relatesTo>1</relatesTo><i> mathieu.acher@inria.fr</i><br /><searchLink fieldCode="AR" term="%22Baudry%2C+Benoit%22">Baudry, Benoit</searchLink><relatesTo>1</relatesTo><i> benoit.baudry@inria.fr</i><br /><searchLink fieldCode="AR" term="%22Nasr%2C+Sana%22">Nasr, Sana</searchLink><relatesTo>1</relatesTo><i> sana.ben-nasr@inria.fr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Empirical+Software+Engineering%22">Empirical Software Engineering</searchLink>. Aug2016, Vol. 21 Issue 4, p1794-1841. 48p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Software+product+line+engineering%22">Software product line engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Reverse+engineering%22">Reverse engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Software+refactoring%22">Software refactoring</searchLink><br /><searchLink fieldCode="DE" term="%22Ontology%22">Ontology</searchLink><br /><searchLink fieldCode="DE" term="%22Reasoning%22">Reasoning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Feature Models (FMs) are a popular formalism for modeling and reasoning about the configurations of a software product line. As the manual construction of an FM is time-consuming and error-prone, management operations have been developed for reverse engineering, merging, slicing, or refactoring FMs from a set of configurations/dependencies. Yet the synthesis of meaningless ontological relations in the FM - as defined by its feature hierarchy and feature groups - may arise and cause severe difficulties when reading, maintaining or exploiting it. Numerous synthesis techniques and tools have been proposed, but only a few consider both configuration and ontological semantics of an FM. There are also few empirical studies investigating ontological aspects when synthesizing FMs. In this article, we define a generic, ontologic-aware synthesis procedure that computes the likely siblings or parent candidates for a given feature. We develop six heuristics for clustering and weighting the logical, syntactical and semantical relationships between feature names. We then perform an empirical evaluation on hundreds of FMs, coming from the SPLOT repository and Wikipedia. We provide evidence that a fully automated synthesis (i.e., without any user intervention) is likely to produce FMs far from the ground truths. As the role of the user is crucial, we empirically analyze the strengths and weaknesses of heuristics for computing ranking lists and different kinds of clusters. We show that a hybrid approach mixing logical and ontological techniques outperforms state-of-the-art solutions. We believe our approach, environment, and empirical results support researchers and practitioners working on reverse engineering and management of FMs. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Empirical Software Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10664-014-9357-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 48 StartPage: 1794 Subjects: – SubjectFull: Software product line engineering Type: general – SubjectFull: Reverse engineering Type: general – SubjectFull: Software refactoring Type: general – SubjectFull: Ontology Type: general – SubjectFull: Reasoning Type: general Titles: – TitleFull: Breathing ontological knowledge into feature model synthesis: an empirical study. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bécan, Guillaume – PersonEntity: Name: NameFull: Acher, Mathieu – PersonEntity: Name: NameFull: Baudry, Benoit – PersonEntity: Name: NameFull: Nasr, Sana IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 13823256 Numbering: – Type: volume Value: 21 – Type: issue Value: 4 Titles: – TitleFull: Empirical Software Engineering Type: main |
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