Distance-Based Sampling of Software Configuration Spaces.
Saved in:
| Title: | Distance-Based Sampling of Software Configuration Spaces. |
|---|---|
| Authors: | Kaltenecker, Christian1, Grebhahn, Alexander1, Siegmund, Norbert2, Jianmei Guo3, Apel, Sven1 |
| Source: | ICSE: International Conference on Software Engineering. 5/25/2019, p1084-1094. 11p. |
| Subjects: | Software configuration management, Computer science, Software engineering, Artificial intelligence, Probability theory |
| Abstract: | Configurable software systems provide a multitude of configuration options to adjust and optimize their functional and non-functional properties. For instance, to find the fastest configuration for a given setting, a brute-force strategy measures the performance of all configurations, which is typically intractable. Addressing this challenge, state-of-the-art strategies rely on machine learning, analyzing only a few configurations (i.e., a sample set) to predict the performance of other configurations. However, to obtain accurate performance predictions, a representative sample set of configurations is required. Addressing this task, different sampling strategies have been proposed, which come with different advantages (e.g., covering the configuration space systematically) and disadvantages (e.g., the need to enumerate all configurations). In our experiments, we found that most sampling strategies do not achieve a good coverage of the configuration space with respect to covering relevant performance values. That is, they miss important configurations with distinct performance behavior. Based on this observation, we devise a new sampling strategy, called distance-based sampling, that is based on a distance metric and a probability distribution to spread the configurations of the sample set according to a given probability distribution across the configuration space. This way, we cover different kinds of interactions among configuration options in the sample set. To demonstrate the merits of distance-based sampling, we compare it to state-of-the-art sampling strategies, such as t-wise sampling, on 10 real-world configurable software systems. Our results show that distance-based sampling leads to more accurate performance models for medium to large sample sets. [ABSTRACT FROM AUTHOR] |
| Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery 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 | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 155540195 AccessLevel: 6 PubType: Conference PubTypeId: conference PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Distance-Based Sampling of Software Configuration Spaces. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kaltenecker%2C+Christian%22">Kaltenecker, Christian</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Grebhahn%2C+Alexander%22">Grebhahn, Alexander</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Siegmund%2C+Norbert%22">Siegmund, Norbert</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Jianmei+Guo%22">Jianmei Guo</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Apel%2C+Sven%22">Apel, Sven</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. 5/25/2019, p1084-1094. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Software+configuration+management%22">Software configuration management</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+science%22">Computer science</searchLink><br /><searchLink fieldCode="DE" term="%22Software+engineering%22">Software engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Configurable software systems provide a multitude of configuration options to adjust and optimize their functional and non-functional properties. For instance, to find the fastest configuration for a given setting, a brute-force strategy measures the performance of all configurations, which is typically intractable. Addressing this challenge, state-of-the-art strategies rely on machine learning, analyzing only a few configurations (i.e., a sample set) to predict the performance of other configurations. However, to obtain accurate performance predictions, a representative sample set of configurations is required. Addressing this task, different sampling strategies have been proposed, which come with different advantages (e.g., covering the configuration space systematically) and disadvantages (e.g., the need to enumerate all configurations). In our experiments, we found that most sampling strategies do not achieve a good coverage of the configuration space with respect to covering relevant performance values. That is, they miss important configurations with distinct performance behavior. Based on this observation, we devise a new sampling strategy, called distance-based sampling, that is based on a distance metric and a probability distribution to spread the configurations of the sample set according to a given probability distribution across the configuration space. This way, we cover different kinds of interactions among configuration options in the sample set. To demonstrate the merits of distance-based sampling, we compare it to state-of-the-art sampling strategies, such as t-wise sampling, on 10 real-world configurable software systems. Our results show that distance-based sampling leads to more accurate performance models for medium to large sample sets. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=155540195 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/ICSE.2019.00112 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1084 Subjects: – SubjectFull: Software configuration management Type: general – SubjectFull: Computer science Type: general – SubjectFull: Software engineering Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Probability theory Type: general Titles: – TitleFull: Distance-Based Sampling of Software Configuration Spaces. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kaltenecker, Christian – PersonEntity: Name: NameFull: Grebhahn, Alexander – PersonEntity: Name: NameFull: Siegmund, Norbert – PersonEntity: Name: NameFull: Jianmei Guo – PersonEntity: Name: NameFull: Apel, Sven IsPartOfRelationships: – BibEntity: Dates: – D: 25 M: 05 Text: 5/25/2019 Type: published Y: 2019 Titles: – TitleFull: ICSE: International Conference on Software Engineering Type: main |
| ResultId | 1 |
