Validation of Constraints Among Configuration Parameters Using Search-Based Combinatorial Interaction Testing

Gargantini, Angelo; Petke, Justyna; Radavelli, Marco; Vavassori, P.

doi:10.1007/978-3-319-47106-8_4

Cited by 16 publications

(23 citation statements)

References 24 publications

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“…For big models, there are cases in which we are unable to improve the constraints, but on average we can still remove around 35% of the faults with CuCV and ValC. As already observed in [10], the CC policy (that generates only valid tests) detects fewer conformance faults, and for this reason has the lowest FID. The average FID over all the mutants is around 37%.…”

Section: )supporting

confidence: 51%

“…In our previous work [10] we proposed to use combinatorial interaction testing techniques to verify the validity of CIT models, which is the approach we use in this work. Fig.…”

Section: B Combinatorial Testing Policiesmentioning

confidence: 99%

“…Two basic policies can be employed in combinatorial testing: UC (Unconstrained CIT) consists in generating the tests ignoring the constraints, and CC (Constrained CIT) is the classical testing policy that generates only tests satisfying the constraints. Aside from UC and CC, we consider three other policies, introduced in our previous work [10]:…”

Section: B Combinatorial Testing Policiesmentioning

confidence: 99%

“…In this section we present the proposed process for constraint repair in CIT models. In our previous work [10] we devised a way of finding conformance faults. We use these techniques to find faults and extend them by proposing an automated way of fixing the faults found.…”

Section: The Constraint Repair Processmentioning

confidence: 99%

“…We use combinatorial interaction testing policies introduced in our previous work [10] to find such faults and fix them by repairing constraints in the original CIT model.…”

Section: Introductionmentioning

confidence: 99%

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Combinatorial Interaction Testing for Automated Constraint Repair

Gargantini

Petke

Radavelli

2017

2017 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)

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Highly-configurable software systems can be easily adapted to address user's needs. Modelling parameter configurations and their relationships can facilitate software reuse. Combinatorial Interaction Testing (CIT) methods are already often used to drive systematic testing of software system configurations. However, a model of the system's configurations not conforming with respect to its software implementation, must be repaired in order to restore conformance. In this paper we extend CIT by devising a new search-based technique able to repair a model composed of a set of constraints among the various software system's parameters. Our technique can be used to detect and fix faults both in the model and in the real software system. Experiments for five real-world systems show that our approach can repair on average 37% of conformance faults. Moreover, we also show it can infer parameter constraints in a large real-world software system, hence it can be used for automated creation of CIT models.

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Section: )supporting

confidence: 51%

“…In our previous work [10] we proposed to use combinatorial interaction testing techniques to verify the validity of CIT models, which is the approach we use in this work. Fig.…”

Section: B Combinatorial Testing Policiesmentioning

confidence: 99%

Section: B Combinatorial Testing Policiesmentioning

confidence: 99%

Section: The Constraint Repair Processmentioning

confidence: 99%

“…We use combinatorial interaction testing policies introduced in our previous work [10] to find such faults and fix them by repairing constraints in the original CIT model.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Combinatorial Interaction Testing for Automated Constraint Repair

Gargantini

Petke

Radavelli

2017

2017 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)

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Cost-effective learning-based strategies for test case prioritization in continuous integration of highly-configurable software

et al. 2022

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Evolving software system families in space and time with feature revisions

et al. 2022

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Software companies commonly develop and maintain variants of systems, with different feature combinations for different customers. Thus, they must cope with variability in space. Software companies further must cope with variability in time, when updating system variants by revising existing software features. Inevitably, variants evolve orthogonally along these two dimensions, resulting in challenges for software maintenance. Our work addresses this challenge with ECSEST (Extraction and Composition for Systems Evolving in Space and Time), an approach for locating feature revisions and composing variants with different feature revisions. We evaluated ECSEST using feature revisions and variants from six highly configurable open source systems. To assess the correctness of our approach, we compared the artifacts of input variants with the artifacts from the corresponding composed variants based on the implementation of the extracted features. The extracted traces allowed composing variants with 99-100% precision, as well as with 97-99% average recall. Regarding the composition of variants with new configurations, our approach can combine different feature revisions with 99% precision and recall on average. Additionally, our approach retrieves hints when composing new configurations, which are useful to find artifacts that may have to be added or removed for completing a product. The hints help to understand possible feature interactions or dependencies. The average time to locate feature revisions ranged from 25 to 250 seconds, whereas the average time for composing a variant was 18 seconds. Therefore, our experiments demonstrate that ECSEST is feasible and effective.

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Validation of Constraints Among Configuration Parameters Using Search-Based Combinatorial Interaction Testing

Cited by 16 publications

References 24 publications

Combinatorial Interaction Testing for Automated Constraint Repair

Combinatorial Interaction Testing for Automated Constraint Repair

Cost-effective learning-based strategies for test case prioritization in continuous integration of highly-configurable software

Evolving software system families in space and time with feature revisions

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