Data Quality Based Applications Testing

Executive Summary/Abstract: In an effort to improve the data quality levels of applications developed by McKesson Specialty Patient Services, the Quality Assurance team has implemented a data quality approach to system testing. The QA team uses system testing activities to identify and resolve data quality defects. Data quality findings reports are developed to form the basis for long term solutions to data quality issues. The details of the current process as well as plans for process improvement are provided in this presentation

Multiple Comparisons using Composite Likelihood in Clustered Data

We study the problem of multiple hypothesis testing for multidimensional data when inter-correlations are present. The problem of multiple comparisons is common in many applications. When the data is multivariate and correlated, existing multiple comparisons procedures based on maximum likelihood estimation could be prohibitively computationally intensive. We propose to construct multiple comparisons procedures based on composite likelihood statistics. We focus on data arising in three ubiquitous cases: multivariate Gaussian, probit, and quadratic exponential models. To help practitioners assess the quality of our proposed methods, we assess their empirical performance via Monte Carlo simulations. It is shown that composite likelihood based procedures maintain good control of the familywise type I error rate in the presence of intra-cluster correlation, whereas ignoring the correlation leads to erratic performance. Using data arising from a diabetic nephropathy study, we show how our composite likelihood approach makes an otherwise intractable analysis possible

Underpinning Quality Assurance: Identifying Core Testing Strategies for Multiple Layers of Internet-of-Things-Based Applications

The Internet of Things (IoT) constitutes a digitally integrated network of intelligent devices equipped with sensors, software, and communication capabilities, facilitating data exchange among a multitude of digital systems via the Internet. Despite its pivotal role in the software development life-cycle (SDLC) for ensuring software quality in terms of both functional and non-functional aspects, testing within this intricate software–hardware ecosystem has been somewhat overlooked. To address this, various testing techniques are applied for real-time minimization of failure rates in IoT applications. However, the execution of a comprehensive test suite for specific IoT software remains a complex undertaking. This paper proposes a holistic framework aimed at aiding quality assurance engineers in delineating essential testing methods across different testing levels within the IoT. This delineation is crucial for effective quality assurance, ultimately reducing failure rates in real-time scenarios. Furthermore, the paper offers a mapping of these identified tests to each layer within the layered framework of the IoT. This comprehensive approach seeks to enhance the reliability and performance of IoT-based applications

Towards quality programming in the automated testing of distributed applications

PhD ThesisSoftware testing is a very time-consuming and tedious activity and accounts for over 25% of the cost of software development. In addition to its high cost, manual testing is unpopular and often inconsistently executed. Software Testing Environments (STEs) overcome the deficiencies of manual testing through automating the test process and integrating testing tools to support a wide range of test capabilities. Most prior work on testing is in single-thread applications. This thesis is a contribution to testing of distributed applications, which has not been well explored. To address two crucial issues in testing, when to stop testing and how good the software is after testing, a statistics-based integrated test environment which is an extension of the testing concept in Quality Programming for distributed applications is presented. It provides automatic support for test execution by the Test Driver, test development by the SMAD Tree Editor and the Test Data Generator, test failure analysis by the Test Results Validator and the Test Paths Tracer, test measurement by the Quality Analyst, test management by the Test Manager and test planning by the Modeller. These tools are integrated around a public, shared data model describing the data entities and relationships which are manipulable by these tools. It enables early entry of the test process into the life cycle due to the definition of the quality planning and message-flow routings in the modelling. After well-prepared modelling and requirements specification are undertaken, the test process and the software design and implementation can proceed concurrently. A simple banking application written using Java Remote Method Invocation (RMI) and Java DataBase Connectivity (JDBC) shows the testing process of fitting it into the integrated test environment. The concept of the automated test execution through mobile agents across multiple platforms is also illustrated on this 3-tier client/server application.The National Science Council, Taiwan: The Ministry of National Defense, Taiwan

Towards a Framework for Realizing Healthcare Management Benefits Through the Integration of Patient\u27s Information

Business Intelligence (BI) applications, including customer relationship management systems, decision support systems, analytical processing systems, and data mining systems, have captured the attention of practitioners and researchers for the last few years. Health care organizations, which are data driven and in which quality and integration of data is of paramount importance, have adopted BI applications to help and assist healthcare managers in improving the quality of the information input to the decision process. Based on preliminary data collection results, it is found that high quality data is essential to successful BI performance and that technological support for data acquisition, analysis and deployment are not widespread. Yet, business organizations are not investing in improving data quality and data integration. In this paper the authors propose a framework for evaluating the quality and integration of patient’s data for BI applications in healthcare organizations. In doing so, a range of potential benefits is highlighted. Even though this framework is in an early stage of development, it intends to present existing solutions for evaluating the above issues. The authors conclude that further research needs to be carried out to refine this framework, through model testing and case studies evaluation

A Comparison of Grading Models for Neighborhood Level of Family Housing Units

More recently Turkey has witnessed fast housing development and real estate sector growth because of the mortgage preparations. With this development, property location quality has been considered important for selecting and paying them. This study uses a data set of new single family housing units in Kocaeli University Campus Area. By using 4 location quality criteria, 27 single family housing units are graded at the neighborhood level. It is aimed to examine the applications of grading property at the neighborhood level based on property location quality by testing with three methods. Traditional method and fuzzy logic method were discussed in our antecedent studies. In this study, an easy used numerical calculation method; Neural Networks (NN), is introduced. Its grading performance is compared with the previous methods. NN method is found to be more accurate and realistic than traditional grading approach where its designing stage is more practical and faster than fuzzy logic approach.

NERFBK: A HOLISTIC DATASET FOR BENCHMARKING NERF-BASED 3D RECONSTRUCTION

Neural Radiance Field methods are innovative solutions to derive 3D data from a set of oriented images. This paper introduces new real and synthetic image datasets - called NeRFBK - specifically designed for testing and comparing NeRF-based 3D reconstruction algorithms. More and more reconstruction algorithms and techniques are available nowadays, raising the need to evaluate and compare the quality of derived 3D products currently used in various domains and applications. However, gathering diverse data with precise ground truth is challenging and may not encompass all relevant applications. The NeRFBK dataset addresses this issue by providing multi-scale, indoor and outdoor datasets with high-resolution images and videos and camera parameters for testing and comparing NeRF-based algorithms. This paper presents the design and creation of the NeRFBK set of data, various examples and application scenarios, and highlights its potential for advancing the field of 3D reconstruction

Scientific and Regulatory Policy Committee (SRPC) Review*: Interpretation and Use of Cell Proliferation Data in Cancer Risk Assessment

Increased cell proliferation is a central key event in the mode of action for many non-genotoxic carcinogens, and quantitative cell proliferation data play an important role in the cancer risk assessment of many pharmaceutical and environmental compounds. Currently, there is limited unified information on assay standards, reference values, targeted applications, study design issues, and quality control considerations for proliferation data. Here, we review issues in measuring cell proliferation indices, considerations for targeted studies, and applications within current risk assessment frameworks. As the regulatory environment moves toward more prospective evaluations based on quantitative pathway-based models, standardiza- tion of proliferation assays will become an increasingly important part of cancer risk assessment. To help address this development, we also discuss the potential role for proliferation data as a component of alternative carcinogenicity testing models. This information should improve consistency of cell proliferation methods and increase efficiency of targeted testing strategies