Scalable prediction of non-functional properties in software product lines: Footprint and memory consumption

Siegmund, Norbert; Rosenmüller, Marko; Kästner, Christian; Giarrusso, Paolo G.; Apel, Sven; Kolesnikov, Sergiy S.

doi:10.1016/j.infsof.2012.07.020

Cited by 77 publications

(80 citation statements)

References 14 publications

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“…The experimental corpus of our evaluation is composed by a benchmark of 118 feature models, whose number of products ranges from 16 to 640 products, that are publicly available from the SPL Conqueror [16] and the SPLOT [17] repositories. The objectives to optimize are the 3 Available at URL: http://minisat.se/MiniSat+.html Algorithm 1 Algorithm for obtaining the optimal Pareto set.…”

Section: Methodsmentioning

confidence: 99%

Multi-objective Optimal Test Suite Computation for Software Product Line Pairwise Testing

Lopez-Herrejon

Chicano

Ferrer

et al. 2013

2013 IEEE International Conference on Software Maintenance

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Abstract-Software Product Lines (SPLs) are families of related software products, which usually provide a large number of feature combinations, a fact that poses a unique set of challenges for software testing. Recently, many SPL testing approaches have been proposed, among them pairwise combinatorial techniques that aim at selecting products to test based on the pairs of feature combinations such products provide. These approaches regard SPL testing as an optimization problem where either coverage (maximize) or test suite size (minimize) are considered as the main optimization objective. Instead, we take a multi-objective view where the two objectives are equally important. In this exploratory paper we propose a zero-one mathematical linear program for solving the multi-objective problem and present an algorithm to compute the true Pareto front, hence an optimal solution, from the feature model of a SPL. The evaluation with 118 feature models revealed an interesting trade-off between reducing the number of constraints in the linear program and the runtime which opens up several venues for future research.

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Section: Methodsmentioning

confidence: 99%

Multi-objective Optimal Test Suite Computation for Software Product Line Pairwise Testing

Lopez-Herrejon

Chicano

Ferrer

et al. 2013

2013 IEEE International Conference on Software Maintenance

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“…The prediction of the performance of individual variants is subject to intensive research. Approaches usually handle a small sample of measured variants and seek to understand the correlation between configurations and performance [15,27,30,34,37]. The e↵ectiveness of statistical learning techniques and regression methods have been empirically studied.…”

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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“…Increasing the robustness and reliability of measurements would mean to multiply the times by a factor of 10 or higher. Even feature-wise measurement (as explained next) requires more measurements for our subject programs, but with a higher error rate, as it does not consider feature interactions at all [34]. For illustration, we depict in Table 2 the times needed for familybased performance measurement in relation to the times needed to measure all variants (in brackets).…”

Section: Ajstats 15s (53d)mentioning

confidence: 99%

“…Does family-based performance measurement outperform state-of-the-art sampling approaches: (a) feature-wise and (b) pair-wise measurement [34]? Feature-wise measurement samples the customizable program to quantify the influence of each feature on performance.…”

Section: Ajstats 15s (53d)mentioning

confidence: 99%

See 1 more Smart Citation

Family-based performance measurement

Siegmund

Rhein

Apel

2013

Proceedings of the 12th International Conference on Generative Programming: Concepts &Amp; Experiences

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Most contemporary programs are customizable. They provide many features that give rise to millions of program variants. Determining which feature selection yields an optimal performance is challenging, because of the exponential number of variants. Predicting the performance of a variant based on previous measurements proved successful, but induces a trade-off between the measurement effort and prediction accuracy. We propose the alternative approach of family-based performance measurement, to reduce the number of measurements required for identifying feature interactions and for obtaining accurate predictions. The key idea is to create a variant simulator (by translating compile-time variability to run-time variability) that can simulate the behavior of all program variants. We use it to measure performance of individual methods, trace methods to features, and infer feature interactions based on the call graph. We evaluate our approach by means of five feature-oriented programs. On average, we achieve accuracy of 98 %, with only a single measurement per customizable program. Observations show that our approach opens avenues of future research in different domains, such an feature-interaction detection and testing.

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Scalable prediction of non-functional properties in software product lines: Footprint and memory consumption

Cited by 77 publications

References 14 publications

Multi-objective Optimal Test Suite Computation for Software Product Line Pairwise Testing

Multi-objective Optimal Test Suite Computation for Software Product Line Pairwise Testing

Using machine learning to infer constraints for product lines

Family-based performance measurement

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