9,908 research outputs found

    SafeSkin: A blockchain system of products’ lifespan development to help the user find safer products

    Get PDF
    Due to the escalating demand for cosmetics skincare products and a growing number of beauty products putting people’s health at risk by counterfeits or using chemical ingredients in order to give a better skincare result, people start to have more concerns about the quality of cosmetics and skincare products. Today, many companies strive to make the transparency of the ingredients by labeling on the packaging and knowledge their customers about the benefits of the ingredients they are using. However, the consumers unable to find out how does the product being produced by the original company, and being transferred to the department store or other retailer. This study aims to use data and analysis from Environmental Working Group (EWG) and Get it Beauty Program, to provide comprehensive information on a skincare product from Production, Quality Inspection, Transportation to Marketing to assist consumers to determine whether the product is safe or not, also gathering consumers’ skin related information and skincare concerns to evaluate the product in particular for their skin type and provide recommendations. This paper investigates why it is essential to show a product from choosing raw material to the production process, shipment and when they are buying from the (online) store with after-sales support. To achieve this, a blockchain system of products’ lifespan development that built-in many online websites to the spurious with the genuine and help users find safer products was discussed and evaluated based on the 52 users with different skin conditions background

    Are Smell-Based Metrics Actually Useful in Effort-Aware Structural Change-Proneness Prediction? An Empirical Study

    Get PDF
    Bad code smells (also named as code smells) are symptoms of poor design choices in implementation. Existing studies empirically confirmed that the presence of code smells increases the likelihood of subsequent changes (i.e., change-proness). However, to the best of our knowledge, no prior studies have leveraged smell-based metrics to predict particular change type (i.e., structural changes). Moreover, when evaluating the effectiveness of smell-based metrics in structural change-proneness prediction, none of existing studies take into account of the effort inspecting those change-prone source code. In this paper, we consider five smell-based metrics for effort-aware structural change-proneness prediction and compare these metrics with a baseline of well-known CK metrics in predicting particular categories of change types. Specifically, we first employ univariate logistic regression to analyze the correlation between each smellbased metric and structural change-proneness. Then, we build multivariate prediction models to examine the effectiveness of smell-based metrics in effort-aware structural change-proneness prediction when used alone and used together with the baseline metrics, respectively. Our experiments are conducted on six Java open-source projects with up to 60 versions and results indicate that: (1) all smell-based metrics are significantly related to structural change-proneness, except metric ANS in hive and SCM in camel after removing confounding effect of file size; (2) in most cases, smell-based metrics outperform the baseline metrics in predicting structural change-proneness; and (3) when used together with the baseline metrics, the smell-based metrics are more effective to predict change-prone files with being aware of inspection effort
    • …
    corecore