Combinatorial Test Generation for Software Product Lines Using Minimum Invalid Tuples

Yu, Linbin; Duan, Feng; Lei, Yu; Kacker, Raghu N.; Kuhn, D. Richard

doi:10.1109/hase.2014.18

Cited by 24 publications

(22 citation statements)

References 26 publications

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“…The proposed strategy was compared with the existing tway strategy to evaluate the performance of SPL-HS strategy during the t-way testing. The results were obtained from a previous study that a test generation research tool called LOOKUP performs better than the existing test generation tools in term of test size and execution time [6]. Table 6 demonstrates that SPL-HS has produced superior results in most of the cases.…”

Section: B Experimental Results On T-waymentioning

confidence: 98%

See 1 more Smart Citation

Software Product Line Test List Generation based on Harmony Search Algorithm with Constraints Support

Alsewari¹,

Kabir²,

Zamli³

et al. 2019

ijacsa

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In software product line (SPL), selecting product's features to be tested is an essential issue to enable the manufactories to release new products earlier than others. Practically, it is impossible to test all the products' features (i.e. exhaustive testing). Evidence has shown that several SPL strategies have been proposed to generate the test list for testing purpose. Nevertheless, all the existing strategies failed to produce an optimum test list for all cases. Thus, the current study is aimed to develop a new SPL test list generation strategy based on Harmony Search (HS) algorithm, namely SPL-HS. SPL-HS generates a minimum number of test cases that cover all of the features that are required to be tested based on the required interaction degree (t). The results demonstrate that the performance of SPL-HS is able to compete with the existing SPL strategies for generating test list size.

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Section: B Experimental Results On T-waymentioning

confidence: 98%

“…PICT uses random selection to generate a test suite. As an alternate, LOOKUP [6] uses In Parameter Oreder Generation (IPOG) approach combined with Minimum Invalid Tuples (MIT) for testing suite generation. Although these strategies are able to generate test suit, but are not well optimized.…”

Section: Introductionmentioning

confidence: 99%

Software Product Line Test List Generation based on Harmony Search Algorithm with Constraints Support

Alsewari¹,

Kabir²,

Zamli³

et al. 2019

ijacsa

View full text Add to dashboard Cite

show abstract

“…Sl, S2, S3, S4, S5, S6, S8, S9 SIO, Sll, S12, S13, S14, S15, S16, S17, S18, S19, S20, S21,S22,S23,S24,S25,S26,S27,S28,S29,S30,S31,S32,S33,S34,S35,S36,S37,S38,S39,S40,S41,S42,S43,S44,S45,S46 How Sl, S3, S7, S9, S13, S14, S18, S22, S31, S45, S47 technique was GE (GEneric) with 6 publications, followed by SA (Simulated Annealing), GA (Genetic Algorithm), EA (Evolutionary Algorithm), MOEA (Multi-Objective Evolutionary Algorithm) and STA (Static Analysis). The remaining references were spread out among the remaining 5 techniques.…”

Section: Whatmentioning

confidence: 99%

A first systematic mapping study on combinatorial interaction testing for software product lines

Lopez-Herrejon

Fischer

Ramler³

et al. 2015

2015 IEEE Eighth International Conference on Software Testing, Verification and Validation Workshops (ICSTW)

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Software Product Lines (SPLs) are families of related software systems distinguished by the set of features each one provides. Over the past decades SPLs have been the subject of extensive research and application both in academia and industry. SPLs practices have proven benefits such as better product customization and reduced time to market. Testing SPLs pose additional challenges stemming from the typically large number of product variants which make it infeasible to test every single one of them. In recent years, there has been an extensive research on applying Combinatorial Interaction Testing (CIT) for SPL testing. In this paper we present the first systematic mapping study on this subject. Our research questions aim to gather information regarding the techniques that have been applied, the nature of the case studies used for their evaluation, and what phases of CIT have been addressed. Our goal is to identify common trends, gaps, and opportunities for further research and application.

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“…The t-way test suite generation (where t indicates the interaction strength), involves finding an optimized set of test cases that covers the t-way interaction strength. Many reported test results indicate that t-way test suite is as good as exhaustive testing [8,9]. Over the last 10 years, many new meta-heuristic algorithms have been developed, often, disguised by some new inspirations and mathematical formulation.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Opposition-Based Learning Selection: The Case for Jaya Algorithm

et al. 2021

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Over the years, opposition-based Learning (OBL) technique has been proven to effectively enhance the convergence of meta-heuristic algorithms. The fact that OBL is able to give alternative candidate solutions in one or more opposite directions ensures good exploration and exploitation of the search space. In the last decade, many OBL techniques have been established in the literature including the Standard-OBL, General-OBL, Quasi Reflection-OBL, Centre-OBL and Optimal-OBL. Although proven useful, much existing adoption of OBL into meta-heuristic algorithms has been based on a single technique. If the search space contains many peaks with potentially many local optima, relying on a single OBL technique may not be sufficiently effective. In fact, if the peaks are close together, relying on a single OBL technique may not be able to prevent entrapment in local optima. Addressing this issue, assembling a sequence of OBL techniques into meta-heuristic algorithm can be useful to enhance the overall search performance. Based on a simple penalized and reward mechanism, the best performing OBL is rewarded to continue its execution in the next cycle, whilst poor performing one will miss cease its current turn. This paper presents a new adaptive approach of integrating more than one OBL techniques into Jaya Algorithm, termed OBL-JA. Unlike other adoptions of OBL which use one type of OBL, OBL-JA uses several OBLs and their selections will be based on each individual performance. Experimental results using the combinatorial testing problems as case study demonstrate that OBL-JA shows very competitive results against the existing works in term of the test suite size. The results also show that OBL-JA performs better than standard Jaya Algorithm in most of the tested cases due to its ability to adapt its behaviour based on the current performance feedback of the search process.

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Combinatorial Test Generation for Software Product Lines Using Minimum Invalid Tuples

Cited by 24 publications

References 26 publications

Software Product Line Test List Generation based on Harmony Search Algorithm with Constraints Support

Software Product Line Test List Generation based on Harmony Search Algorithm with Constraints Support

A first systematic mapping study on combinatorial interaction testing for software product lines

An Adaptive Opposition-Based Learning Selection: The Case for Jaya Algorithm

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