Test suite reduction for self-organizing systems

Reichstaller, André; Eberhardinger, Benedikt; Ponsar, Hella; Knapp, Alexander; Reif, Wolfgang

doi:10.1145/3194733.3194739

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…• Usage of formal specification and fault model (4-d): Püschel et al [109] focused on applying a formal fault model, error and failure by using MAPE-K, deriving 10 distinct failure scenarios that may occur in the process of adaptation. • Test minimization (4-e): Reichstaller et al [136] proposed a fault-based approach on test suite reduction by taking into account effects of communication faults during the reconfiguration process of a system.…”

Section: Studies That Either Proposed or Applied Fault-based Testing Approachesmentioning

confidence: 99%

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 The literature lacks a comprehensive review that provides a broad picture of the area; current reviews are outdated and incomplete.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: Studies That Either Proposed or Applied Fault-based Testing Approachesmentioning

confidence: 99%

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

Siqueira

Ferrari

Souza

et al. 2021

Software Testing Verif & Rel

View full text Add to dashboard Cite

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“…Reichstaller et al [59] evaluate two clustering techniques i.e. dissimilarity-based sparse subset selection and affinity propagation, for reduction of test cases.…”

Section: Clustering Algorithmmentioning

confidence: 99%

“…Experimental results imply that effective clusters of test cases are achieved with the proposed method. From Table 10, it is analyzed that out of 5 clustering based research studies, 2 of them focus on statement coverage [57], [58], other three studies deal with fault detection [60], mutation score [59], and code coverage [56] respectively.…”

Section: Clustering Algorithmmentioning

confidence: 99%

“…This method features a higher coverage rate with lower complexity under the streamlined test suite of the same quantity [56]. Two existing clustering techniques, Affinity Propagation and Dissimilarity-based Sparse Subset Selection can also be used to drastically reduce the original test suite while retaining a good mutation score [59]. In several applications, hierarchical clustering is preferred because it produces deterministic output and leads to a greater reduction in test suite size at the expense of a small loss in fault-detection capability.…”

Section: G Comparison Of Categoriesmentioning

confidence: 99%

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A Comprehensive Investigation of Modern Test Suite Optimization Trends, Tools and Techniques

et al. 2019

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Software testing is an important but expensive activity of software development life cycle, as it accounts for more than 52% of entire development cost. Testing requires the execution of all possible test cases in order to find the defects in the software. Therefore, different test suite optimization approaches like the genetic algorithm and the greedy algorithm, etc., are widely used to select the representative test suite without compromising the effectiveness. Test suite optimization is frequently researched to enhance its competences but there is no study published until now that analyzes the latest developments from 2016 to 2019. Hence, in this article, we systematically examine the state-of-the-art optimizations' approaches, tools, and supporting platforms. Principally, we conducted a systematic literature review (SLR) to inspect and examine 58 selected studies that are published during 2016-2019. Subsequently, the selected researches are grouped into five main categories, i.e., greedy algorithm (seven studies), meta-heuristic (28 studies), hybrid (six studies), clustering (five studies), and general (12 studies). Finally, 32 leading tools have been presented, i.e., existing tools (25 tools) and proposed/developed tools (seven tools) along 14 platform supports. Furthermore, it is noted that several approaches aim at solving the single-objective optimization problem. Therefore, researchers should focus on dealing with the multi-objective problem, as multi-objective versions outperform the single-objective ones. Moreover, less attention has been given to clustering-based techniques. Thus, we recommend exploring the machine learning and artificial intelligencebased optimization approaches in the future. A broad exploration of tools and techniques, in this article, will help researchers, practitioners, and developers to opt for adequate techniques, tools, or platforms as per requirements.

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