Applications of Model Reuse When Using Estimation of Distribution Algorithms to Test Concurrent Software

Staunton, Jan; Clark, John A.

doi:10.1007/978-3-642-23716-4_12

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…They proposed integration with data mining techniques to predict the performance of a test data generator. In the context of testing for concurrent software, detecting faults can be improved by exploiting information discovered in EDA exploration that can save future test efforts [24]. EDA has also been employed to improve software reliability prediction [25].…”

Section: Related Workmentioning

confidence: 99%

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Marginal Distribution Algorithm for Feature Model Test Configuration Generation

Sahid,

Saringat,

Hamzah

et al. 2023

IJACSA

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Generating test configuration for Software Product Line (SPL) is difficult, due to the exponential effect of feature combination. Pairwise testing can generate test input for a single software product that deviates from exhaustive testing, nevertheless proven to be effective. In the context of SPL testing, to generate minimal test configuration that maximizes pairwise coverage is not trivial, especially when dealing with a huge number of features and when constraints must be satisfied, which is the case in most SPL systems. In this paper, we propose an estimation of distribution algorithm, based on pairwise testing, to alleviate this problem. Comparisons are made against a greedybased and a constraint handling based approach. The experiments demonstrate the feasibility of the proposed algorithm, such that it achieves better test configurations dissimilarity and at the same time maintain the test configuration size and pairwise coverage. This is supported by analysis using descriptive statistics.

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Section: Related Workmentioning

confidence: 99%

“…Pair of features, pf = { (-3,20), (-3,21), (5,19), (-5,20), (5,22), (-5,23), (-6,18), (8,9), (8,19), (8,21), (-8,23), (-9,19), (9,23), (-10,20), (-10,23), (11,19), (11,22), (-11,23), (12,19), (12,22), (-12,23), (-16,23), (19,23), (20,22), (21,24), ( 21 Step 2. We calculate the univariate distribution for each feature.…”

Section: Definition 2 (Joint Probability)mentioning

confidence: 99%

Marginal Distribution Algorithm for Feature Model Test Configuration Generation

Sahid,

Saringat,

Hamzah

et al. 2023

IJACSA

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“…EDAs have been adopted to solve optimization problems in software engineering. We noticed that EDAs have been utilized in optimizing test data generation and test suites generation [17], [18], [19] and fault detection [20]. EDA has also been employed to improve software reliability prediction [21].…”

Section: Related Workmentioning

confidence: 99%

A Univariate Marginal Approach for Pairwise Testing of Software Product Lines

Sahid¹,

Sultan²,

Ghani³

et al. 2017

IJCA

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Software Product Line (SPL) is a software engineering paradigm that is inspired by the concept of reusability of common features, formulated for different software products. Complete testing of all software products in SPL is known to be unfeasible. This is due to the very large number of possible products that can be produced or configured using a combination of features in the SPL. Pairwise Testing is a type of Combinatorial Testing, influenced by the perception that two factors (or features in the context of SPL testing) stimulate most faults. The effectiveness of SPL testing can be measured using the pairwise coverage of test configuration. However, to generate minimal test configuration that maximizes the pairwise coverage is not trivial, especially when dealing with a huge number of features and when constraints have to be satisfied, which is the case in most SPL scenarios. Therefore, it is the motivation of this work to investigate the feasibility of an Estimation of Distribution Algorithm (EDA), specifically the Univariate Marginal Distribution Algorithm (UMDA), in generating minimal test configuration for pairwise testing of SPL. The experimental results show that in certain problem instances, UMDA is able to compete with existing greedy and search-based algorithms.

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“…Models (normally formal models) specify the program behavior and testers use them to generate test sequences [75]. Model checking is the most common model-based testing for concurrent software [75,76,44,77,78,19,79,80,81,82,83,84]. The drawback to model checking is that it can lead to a state-space explosion problem.…”

Section: Model Based Testingmentioning

confidence: 99%

“…• Estimation of Distribution Algorithms (EDA) [83] based model checking saving computational and manual e↵ort when building and debugging concurrent systems. This is achieved by reusing information, specifically information from the models constructed, from an earlier execution to aid the search in a future execution.…”

Section: Model Based Testingmentioning

confidence: 99%

Um framework para avaliação sistemática de técnicas de teste no contexto de programação concorrente

Melo¹

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Applications of Model Reuse When Using Estimation of Distribution Algorithms to Test Concurrent Software

Cited by 5 publications

References 7 publications

Marginal Distribution Algorithm for Feature Model Test Configuration Generation

Marginal Distribution Algorithm for Feature Model Test Configuration Generation

A Univariate Marginal Approach for Pairwise Testing of Software Product Lines

Um framework para avaliação sistemática de técnicas de teste no contexto de programação concorrente

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