An EFSM-Based Test Data Generation Approach in Model-Based Testing

Testing is an integral part of software development. Current fast-paced system developments have rendered traditional testing techniques obsolete. Therefore, automated testing techniques are needed to adapt to such system developments speed. Model-based testing (MBT) is a technique that uses system models to generate and execute test cases automatically. It was identified that the test data generation (TDG) in many existing model-based test case generation (MB-TCG) approaches were still manual. An automatic and effective TDG can further reduce testing cost while detecting more faults. This study proposes an automated TDG approach in MB-TCG using the extended finite state machine model (EFSM). The proposed approach integrates MBT with combinatorial testing. The information available in an EFSM model and the boundary value analysis strategy are used to automate the domain input classifications which were done manually by the existing approach. The results showed that the proposed approach was able to detect 6.62 percent more faults than the conventional MB-TCG but at the same time generated 43 more tests. The proposed approach effectively detects faults, but a further treatment to the generated tests such as test case prioritization should be done to increase the effectiveness and efficiency of testing.     

Accurate retrieval of region of interest for estimating point spread function and image deblurring

Extraction of an optimal region of interest (ROI) is crucial in many image processing applications, such as estimation of the point spread function (PSF) and blind deconvolution (BD). Although the amount of publications on PSF and BD is quite extensive; however, the work on ROI estimation has not received much attention. Existing methods which used heuristic models are not only time-consuming but also computationally expensive. In this paper, we proposed a new ROI retrieval scheme based on image partitioning and entropy measurement feedback. This method has low computation cost since it contains no matrix operations. Comprehensive experiments on real and synthetic datasets revealed that the proposed method is competitive when compared with existing search techniques, averaging at 26.1?dB, 0.46 and 1.44 on peak signal-to-noise ratio, universal image quality index and error ratio scales, respectively. On average, the proposed method takes less than 10?s to retrieve the ROI which is significantly faster compared to established solution.     

Tyrosinase inhibition by water and ethanol extracts of a far eastern sea cucumber, Stichopus japonicus

BACKGROUND: Tyrosinase plays a key role in hyperpigmentaion and enzymatic browning. The present study was aimed at investigating the inhibitory effects of water and 70% aqueous ethanol extracts of Stichopus japonicus, a sea cucumber long consumed as a tonic food and traditional medicine, on the diphenolase activity of tyrosinase. RESULTS: In the tyrosinase inhibition study, high‐performance liquid chromatography completely separated L ‐3,4‐dihydroxyphenylalanine and dopachrome from other compounds present in the extracts, and provided more reliable results than the commonly used spectrophotometry. The ethanol extract (IC₅₀ = 0.49–0.61 mg mL^?1) showed higher inhibitory activity than the water extract (IC₅₀ = 1.80–1.99 mg mL^?1). Enzyme inhibition by the extracts was reversible and of mixed type. For both extracts, the dissociation constants for binding to free enzyme were significantly smaller than those for binding to enzyme–substrate complex. Ethyl‐α‐D ‐glucopyranoside (IC₅₀ = 0.19 mg mL^?1), isolated for the first time from sea cucumber, and adenosine (IC₅₀ = 0.13 mg mL^?1), were identified as key tyrosinase inhibitors. CONCLUSION: The sea cucumber extracts were demonstrated to possess considerable inhibitory potency against the diphenolase activity of tyrosinase, suggesting that the sea cucumber may be a good source of safe and effective tyrosinase inhibitors. Copyright © 2011 Society of Chemical Industry