A Survey of Constrained Combinatorial Testing

Combinatorial Testing (CT) is a potentially powerful testing technique, whereas its failure revealing ability might be dramatically reduced if it fails to handle constraints in an adequate and efficient manner. To ensure the wider applicability of CT in the presence of constrained problem domains, large and diverse efforts have been invested towards the techniques and applications of constrained combinatorial testing. In this paper, we provide a comprehensive survey of representations, influences, and techniques that pertain to constraints in CT, covering 129 papers published between 1987 and 2018. This survey not only categorises the various constraint handling techniques, but also reviews comparatively less well-studied, yet potentially important, constraint identification and maintenance techniques. Since real-world programs are usually constrained, this survey can be of interest to researchers and practitioners who are looking to use and study constrained combinatorial testing techniques

Test suite prioritization by switching cost

Test suite generation and prioritization are two main research fields to improve testing efficiency. Combinatorial testing has been proven as an effective method to generate test suite for highly configurable software systems, while test suites are often prioritized by interaction coverage to detect faults as early as possible. However, for some cases, there exists reasonable cost of reconfiguring parameter settings when switching test cases in different orders. Surprisingly, only few studies paid attention to it. In this paper, by proposing greedy algorithms and graph-based algorithms, we aim to prioritize a given test suite to minimize its total switching cost. We also compare two different prioritization strategies by a series of experiments, and discuss the advantages of our prioritization strategy and the selection of prioritization techniques. The results show that prioritization by switching cost can improve testing efficiency and our prioritization strategy can produce a small test suite with a reasonably low switching cost. This prioritization can be used widely and help locate fault causing interactions. The results also suggest that when testing highly configurable software systems and no knowledge of fault detection can be used, prioritization by switching cost is a good choice to detect faults earlier

A discrete particle swarm optimization for covering array generation

Software behavior depends on many factors. Combinatorial testing aims to generate small sets of test cases to uncover defects caused by those factors and their interactions. Covering array generation, a discrete optimization problem, is the most popular research area in the field of combinatorial testing. Particle swarm optimization (PSO), an evolutionary search based heuristic technique, has succeeded in generating covering arrays that are competitive in size. However, current PSO methods for covering array generation simply round the particle’s position to an integer to handle the discrete search space. Moreover no guidelines are available to set PSO’s parameters for this problem effectively. In this paper, we extend the set-based PSO, an existing discrete PSO method, to covering array generation. Two auxiliary strategies (particle reinitialization and additional evaluation of gbest) are proposed to improve performance, and thus a novel discrete PSO (DPSO) for covering array generation is developed. Guidelines for parameter settings both for conventional PSO and for DPSO are developed systematically here. Discrete extensions of four existing PSO variants are developed, in order to further investigate the effectiveness of DPSO for covering array generation. Experiments show that conventional PSO can produce better results using the guidelines for parameter settings, and that DPSO can generate smaller covering arrays than conventional PSO and other existing evolutionary algorithms. DPSO is a promising improvement on particle swarm optimization for covering array generation

Search based combinatorial testing

Search techniques can dramatically change our ability to solve a host of problems in applied science and engineering, many search techniques have been developed and applied successfully in many fields, including search based software engineering (SBSE). As a key problem of combinatorial testing, covering array generation has been widely studied and many search techniques have been applied which can be named as search based combinatorial testing (SBCT). SBCT is a branch of search based software testing (SBST) within SBSE. In this paper, to explore the applicability and effectiveness of SBCT, we design six variants from existing search algorithms: Genetic Algorithm, Particle Swarm Optimization and Ant Colony Algorithm by reversing and randomizing their mechanisms. We study their effectiveness in terms of generating a covering array and compare their performance. Experiments show that these search techniques can work well with distinct performance in covering array generation. We believe that these search techniques can be further improved by fine-tuning their configuration and used in broad ranges of area

Combinatorial testing, random testing, and adaptive random testing for detecting interaction triggered failures

Context: Software behavior depends on many factors, and some failures occur only when certain factors interact. This is known as an interaction triggered failure, and the corresponding selection of factor values can be modeled as a Minimal Failure-causing Schema (MFS). (An MFS involving m factors is an m-MFS.) Combinatorial Testing (CT) has been developed to exercise ("hit") all MFS with few tests. Adaptive Random Resting (ART) endeavors to make tests as different as possible, ensuring that testing of MFS is not unnecessarily repeated. Random Testing (RT) chooses tests at random without regard to the MFS already treated. Cl' might be expected to improve on RT for finding interaction triggered faults, and yet some studies report no significant difference. CT can also be expected to be better than ART, and yet other studies report that ART can be much better than RT. In light of these, the relative merits of CT, ART, and RT for finding interaction triggered faults are unclear. Objective: To investigate the relationships among CT, ART, and RT, we conduct the first complete and systematic comparison for the purpose of hitting MFS. Method: A systematic review of six aspects of CT, RT and ART is conducted first. Then two kinds of experiments are used to compare them under four metrics. Results: ART improves upon RT, but t-way CT is better than both. In hitting t'-MFS the advantage is typically in the range from 10% to 30% when t = t', but becomes much smaller when t' < t, and there may be no advantage when t' > t. The latter case may explain the studies reporting no significant difference between RT and CT. Conclusion: RT is easily implemented. However, depending on its implementation, ART can improve upon RT. Cl' does as well as ART whether or nott' = t, but provides a valuable improvement in the cases when t' = t

ncRNA-mediated ceRNA regulatory network: Transcriptomic insights into breast cancer progression and treatment strategies

With the rapid development of next-generation sequencing technology, several studies have shown that ncRNAs can act as competitive endogenous RNAs (ceRNAs) and are involved in various biological processes, such as proliferation, differentiation, apoptosis, and migration of breast cancer (BC) cells, and plays an important role in BC progression as a molecular target for its diagnosis, treatment, prognosis, and differentiation of subtypes and age groups of BC patients. Based on the description of ceRNA-related biological functions, this study screened and sorted the sequencing analysis and experimental verification conclusions of BC-related ceRNAs and found that the ncRNAs mediated ceRNA networks can promote the development of BC by promoting the expression of genes related to BC proliferation, drug resistance, and apoptosis, inducing the production of epithelial-mesenchymal transition (EMT) to promote metastasis and activating cancer-related signaling pathways

A review of the biological activity and pharmacology of cryptotanshinone, an important active constituent in Danshen

Cryptotanshinone (IUPAC name: (R)-1,2,6,7,8,9-hexahydro-1,6,6-trimethyl-phenanthro(1,2-b)furan-10,11-dione), a biologically active constituent extracted from the roots and rhizomes of the plant Salvia miltiorrhiza, has been studied in depth as a medicinally active compound and shown to have efficacy in the treatment of numerous diseases and disorders. In this review, we describe in detail the current status of cryptotanshinone research, including findings relating to the structure, pharmacokinetics, pharmacological activity, and derivatives of this compound. Cryptotanshinoneh as a diverse range of pharmacological effects, including anti-cancer, anti-inflammatory, immune regulatory, neuroprotective, and anti-fibrosis activities. Studies on the molecular mechanisms underlying the activities of cryptotanshinone have established that the JAK2/STAT3, PI3K/AKT, NF-κB, AMPK, and cell cycle pathways are involved in the inhibitory and pro-apoptotic effects of cryptotanshinone on different tumor cell lines, these molecular pathways interact in a coordinated manner to inhibit cell proliferation, migration and invasion,and induce transformation, autophagy, necrosis, and cellular immunity. The anti-inflammatory mechanisms of cryptotanshinone have been found to be associated with the TLR4-MyD88/PI3K/Nrf2 and TLR4-MyD88/NF-κB/MAPK pathways, whereasthe Hedgehog, NF-κB, and Nrf-2/HO-1 pathways are regulated by cryptotanshinone to reduce organ fibrosis, and its inhibitory effects on the PI3K/AKT-eNOS pathway have been linked to neuroprotective effects. Given the potential medicinal utility of cryptotanshinone, further research is needed to verify the efficacy and safety of this compound in clinical use, evaluate its pharmacological activity, and identify molecular targets

Construction and Analysis of Competing Endogenous RNA Networks for Breast Cancer Based on TCGA Dataset

Background. Long noncoding RNAs (lncRNAs) act as competing endogenous RNAs for microRNAs in cancer metastasis. However, the roles of lncRNA-mediated competing endogenous RNA (ceRNA) networks for breast cancer (BC) are still unclear. Material and Methods. The expression profiles of mRNAs, lncRNAs, and miRNAs with BC were extracted from The Cancer Genome Atlas database. Weighted gene coexpression network analysis was conducted to extract differentially expressed mRNAs (DEmRNAs) that might be core genes. Through miRWalk, TargetScan, and miRDB to predict the target genes, an abnormal lncRNA-miRNA-mRNA ceRNA network with BC was constructed. The survival possibilities of mRNAs, miRNAs, and lncRNAs for patients with BC were determined by Kaplan-Meier survival curves and Oncomine. Results. We identified 2134 DEmRNAs, 1059 differentially expressed lncRNAs (DElncRNAs), and 86 differentially expressed miRNAs (DEmiRNAs). We then compose a ceRNA network for BC, including 72 DElncRNAs, 8 DEmiRNAs, and 12 DEmRNAs. After verification, 2 lncRNAs (LINC00466, LINC00460), 1 miRNA (Hsa-mir-204), and 5 mRNAs (TGFBR2, CDH2, CHRDL1, FGF2, and CHL1) were meaningful as prognostic biomarkers for BC patients. In the ceRNA network, we found that three axes were present in 10 RNAs related to the prognosis of BC, namely, LINC00466-Hsa-mir-204-TGFBR2, LINC00466-Hsa-mir-204-CDH2, and LINC00466-Hsa-mir-204-CHRDL1. Conclusion. This study highlighted lncRNA-miRNA-mRNA ceRNA related to the pathogenesis of BC, which might be used for latent diagnostic biomarkers and therapeutic targets for BC