Exploring variability-aware execution for testing plugin-based web applications

Nguyen, Hung Viet; Kästner, Christian; Nguyen, Tien N.

doi:10.1145/2568225.2568300

Cited by 88 publications

(97 citation statements)

References 41 publications

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“…In future work, although challenging to scale, we plan to investigate additional analysis approaches that track load-time and runtime variability (e.g., from commandline parameters). Data-flow analysis, symbolic execution, and testing tailored to variability [15], [32], [38], [44] are interesting starting points.…”

Section: Related Workmentioning

confidence: 99%

Where Do Configuration Constraints Stem From? An Extraction Approach and an Empirical Study

Nadi

Berger

Kästner

et al. 2015

IIEEE Trans. Software Eng.

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Abstract-Highly configurable systems allow users to tailor software to specific needs. Valid combinations of configuration options are often restricted by intricate constraints. Describing options and constraints in a variability model allows reasoning about the supported configurations. To automate creating and verifying such models, we need to identify the origin of such constraints. We propose a static analysis approach, based on two rules, to extract configuration constraints from code. We apply it on four highly configurable systems to evaluate the accuracy of our approach and to determine which constraints are recoverable from the code. We find that our approach is highly accurate (93 % and 77 % respectively) and that we can recover 28 % of existing constraints. We complement our approach with a qualitative study to identify constraint sources, triangulating results from our automatic extraction, manual inspections, and interviews with 27 developers. We find that, apart from low-level implementation dependencies, configuration constraints enforce correct runtime behavior, improve users' configuration experience, and prevent corner cases. While the majority of constraints is extractable from code, our results indicate that creating a complete model requires further substantial domain knowledge and testing. Our results aim at supporting researchers and practitioners working on variability model engineering, evolution, and verification techniques.

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Section: Related Workmentioning

confidence: 99%

Where Do Configuration Constraints Stem From? An Extraction Approach and an Empirical Study

Nadi

Berger

Kästner

et al. 2015

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

show abstract

“…In future work, although challenging to scale, we plan to investigate additional analysis approaches that track load-time and runtime variability (e.g., from command-line parameters). Data-flow analysis, symbolic execution, and testing tailored to variability [16,30,34,39] are interesting starting points.…”

Section: Related Workmentioning

confidence: 99%

Mining configuration constraints: static analyses and empirical results

Nadi

Berger

Kästner

et al. 2014

Proceedings of the 36th International Conference on Software Engineering

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112

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Highly-configurable systems allow users to tailor the software to their specific needs. Not all combinations of configuration options are valid though, and constraints arise for technical or non-technical reasons. Explicitly describing these constraints in a variability model allows reasoning about the supported configurations. To automate creating variability models, we need to identify the origin of such configuration constraints. We propose an approach which uses buildtime errors and a novel feature-effect heuristic to automatically extract configuration constraints from C code. We conduct an empirical study on four highly-configurable open-source systems with existing variability models having three objectives in mind: evaluate the accuracy of our approach, determine the recoverability of existing variability-model constraints using our analysis, and classify the sources of variability-model constraints. We find that both our extraction heuristics are highly accurate (93 % and 77 % respectively), and that we can recover 19 % of the existing variability-models using our approach. However, we find that many of the remaining constraints require expert knowledge or more expensive analyses. We argue that our approach, tooling, and experimental results support researchers and practitioners working on variability model re-engineering, evolution, and consistency-checking techniques.

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“…Testing configurable systems and product lines are an active area of research. Prior work has considered the problem of optimizing the execution of tests for a set of related products [19,25]. Our method can benefit from test execution optimization since computations with the oracle can be done quickly.…”

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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Exploring variability-aware execution for testing plugin-based web applications

Cited by 88 publications

References 41 publications

Where Do Configuration Constraints Stem From? An Extraction Approach and an Empirical Study

Where Do Configuration Constraints Stem From? An Extraction Approach and an Empirical Study

Mining configuration constraints: static analyses and empirical results

Using machine learning to infer constraints for product lines

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