Faster bug detection for software product lines with incomplete feature models

Souto, Sabrina; Gopinath, Divya; d’Amorim, Marcelo; Marinov, Darko; Khurshid, Sarfraz; Batory, Don

doi:10.1145/2791060.2791093

Cited by 3 publications

(3 citation statements)

References 61 publications

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“…The goal is to reduce the combinatorial number of variants (e.g., Java programs) to examine [23]. SPLif aims to detect bugs of software product lines with incomplete feature models [36]. It helps to prioritize failing tests and configurations.…”

Section: Testing and Configurable Systemsmentioning

confidence: 99%

Towards Learning-Aided Configuration in 3D Printing

Amand

Cordy

Heymans

et al. 2019

Proceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems

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Configurators rely on logical constraints over parameters to aid users and determine the validity of a configuration. However, for some domains, capturing such configuration knowledge is hard, if not infeasible. This is the case in the 3D printing industry, where parametric 3D object models contain the list of parameters and their value domains, but no explicit constraints. This calls for a complementary approach that learns what configurations are valid based on previous experiences. In this paper, we report on preliminary experiments showing the capability of state-of-the-art classification algorithms to assist the configuration process. While machine learning holds its promises when it comes to evaluation scores, an in-depth analysis reveals the opportunity to combine the classifiers with constraint solvers.

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Section: Testing and Configurable Systemsmentioning

confidence: 99%

Towards Learning-Aided Configuration in 3D Printing

Amand

Cordy

Heymans

et al. 2019

Proceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems

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“…Another research direction focuses on the identification of relevant products to test [10,20,21,31,32]. Kim et al [20] applied static program analysis techniques to find irrelevant features for a test.…”

Section: Related Workmentioning

confidence: 99%

“…The goal is to reduce the combinatorial number of variants (e.g., Java programs) to examine. SPLif aims to detect bugs of software product lines with incomplete feature models [31]. Our method to enforce the feature models with constraints can benefit to SPLif.…”

Section: Related Workmentioning

confidence: 99%

Using machine learning to infer constraints for product lines

Temple

Galindo

Acher

et al. 2016

Proceedings of the 20th International Systems and Software Product Line Conference

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Variability intensive systems may include several thousand features allowing for an enormous number of possible configurations, including wrong ones (e.g. the derived product does not compile). For years, engineers have been using constraints to a priori restrict the space of possible configurations, i.e. to exclude configurations that would violate these constraints. The challenge is to find the set of constraints that would be both precise (allow all correct configurations) and complete (never allow a wrong configuration with respect to some oracle). In this paper, we propose the use of a machine learning approach to infer such product-line constraints from an oracle that is able to assess whether a given product is correct. We propose to randomly generate products from the product line, keeping for each of them its resolution model. Then we classify these products accord-ing to the oracle, and use their resolution models to infer crosstree constraints over the product-line. We validate our approach on a product-line video generator, using a simple computer vision algorithm as an oracle. We show that an interesting set of cross-tree constraint can be generated, with reasonable precision and recall.

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