Fault sTypes of Adaptive and Context-Aware Systems and Their Relationship with Fault-based Testing Approaches

Siqueira, Bento R.; Ferrari, Fabiano Cutigi; Souza, Kathiani E.; Santibanez, Daniel S. M.; Camargo, Valter Vieira de

doi:10.1109/icstw50294.2020.00054

Cited by 3 publications

(4 citation statements)

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“…Identifying testing approaches for adaptive systems (ASs) or context-aware systems (CASs); characterizing challenges to test these types of systems; and discussing research directions and recommendations for the area. Note: Previous initial results have already been reported in our prior studies [1,12,41].…”

Section: Descriptionmentioning

confidence: 59%

“…Finally, in recent prior research [41], we characterized specific faults for ASs and CASs based on a subset of 11 studies [19,29,[42][43][44][45][46][47][48][49][50] selected for the SLR we report in this article. As a result, we concluded that more attention in some characteristics (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…Only studies that are peer reviewed, published in either a conference or a scientific journal, and written in English were analysed in the light of the inclusion criteria. As mentioned in Section 3, the dataset produced in this SLR was already used in the analysis of fault types of ASs and CASs [41]; therefore, in order to keep the dataset consistent across our publications, we also show criterion (iii) throughout this paper. In this work, we define round as a procedure of performing all phases of a systematic literature review in order to synthesize answers to the research questions.…”

Section: Inclusion Criteriamentioning

confidence: 99%

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Testing of adaptive and context‐aware systems: approaches and challenges

Siqueira

Ferrari

Souza

et al. 2021

Software Testing Verif & Rel

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Adaptive systems (ASs) and context-aware systems (CASs) are able to evaluate their own behaviour and to adapt it when the system fails to accomplish its goals or when better functionality or performance is possible. Ensuring the reliability of ASs and CASs is demanding because failures might have undesirable consequences. Testing ASs and CASs effectively is not trivial because of the inherent characteristics of these systems. The literature lacks a comprehensive review that provides a broad picture of the area; current reviews are outdated and incomplete. The objectives of this study are characterizing the state of the art in AS and CAS testing and discussing approaches, challenges, observed trends, and research limitations and directions. We performed a systematic literature review (SLR) and a thematic analysis of studies, reporting up-to-date, refined and extended results when compared with existing reviews. Based on 102 selected studies, we (i) characterized testing approaches by grouping techniques for ASs and CASs; (ii) updated and refined a characterization of testing challenges for ASs and CASs; and (iii) analysed and discussed research trends and implications for AS and CAS testing. There are recurring research concerns regarding AS and CAS testing. Examples are the generation of test cases and built-in tests. Moreover, we also identified recurring testing challenges such as context monitoring and runtime decisions. Moreover, there are some trends such as model-based testing and hybrid techniques and some little investigated issues like uncertainty and prediction of changes. All in all, our results may provide guidance for developers and researchers with respect to the practice and the future research on AS and CAS testing.

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Section: Descriptionmentioning

confidence: 59%

Section: Related Workmentioning

confidence: 99%

Section: Inclusion Criteriamentioning

confidence: 99%

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Testing of adaptive and context‐aware systems: approaches and challenges

Siqueira

Ferrari

Souza

et al. 2021

Software Testing Verif & Rel

Self Cite

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“…The minor variance of the original program is known as mutant. 4 The key concept behind mutation testing is to verify whether the test suite can manifest the mutant fault. Hence, test suite is applied for the different versions of the source program.…”

Section: Introductionmentioning

confidence: 99%

Equivalent mutant identification using hybrid wavelet convolutional rain optimization

Jammalamadaka

Parveen

2021

Softw Pract Exp

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Mutation testing is a significant software testing approach that identifies the faults present in the source codes and the potentiality of the test cases in detecting the mutated codes. In this testing approach, equivalent mutant detection is complicated as the test cases will not identify the mutated codes from the source program. To overcome this problem, the article proposes a novel hybrid strategy known as the hybrid wavelet convolutional rain optimization (HWCRO) to classify the equivalent mutants present in the source codes accurately. The proposed technique considers three different classes of equivalent mutants based on the RIPR model and exactly identifies the mutated code. Initially, the features such as the semantic similarity and the information entropy are extracted, and these features are given as the input to the wavelet convolutional neural network (wCNN) classifier. The dimensions of the features are reduced in the convolutional layers using the wavelet function, which enhances the classifier's performance. To improve the classification accuracy, the loss function is minimized with an adaptive rain optimization algorithm (ROA) that iteratively tunes the parameter of wCNN. The proposed approach is compared with the existing classification techniques based on the parameters such as precision, recall, f1-score, and accuracy, and the simulation results yielded 85.17% accuracy value for the proposed approach.

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Self-Adaptive Testing in the Field

Silva,

Pelliccione,

Bertolino

2024

ACM Trans. Auton. Adapt. Syst.

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We are increasingly surrounded by systems connecting us with the digital world and facilitating our life by supporting our work, leisure, activities at home, health, etc. These systems are pressed by two forces. On the one side, they operate in environments that are increasingly challenging due to uncertainty and uncontrollability. On the other side, they need to evolve, often in a continuous fashion, to meet changing needs, to offer new functionalities, or also to fix emerging failures. To make the picture even more complex, these systems rarely work in isolation and often need to collaborate with other systems, as well as humans. All such facets call for moving their validation during operation, as offered by approaches called testing in the field. In this paper, we observe that even the field-based testing approaches should change over time to follow and adapt to the changes and evolution of collaborating systems or environments or users’ behaviors. We provide a taxonomy of this new category of testing that we call self-adaptive testing in the field (SATF), together with a reference architecture for SATF approaches. To achieve this objective, we surveyed the literature and collected feedback and contributions from experts in the domain via a questionnaire and interviews.

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Fault sTypes of Adaptive and Context-Aware Systems and Their Relationship with Fault-based Testing Approaches

Cited by 3 publications

References 41 publications

Testing of adaptive and context‐aware systems: approaches and challenges

Testing of adaptive and context‐aware systems: approaches and challenges

Equivalent mutant identification using hybrid wavelet convolutional rain optimization

Self-Adaptive Testing in the Field

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