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Software testing is the act of examining the artifacts and the behavior of the software under test by validation and verification. Software testing can also provide an objective, independent view of the software to allow the business to appreciate and understand the risks of software implementation. Test techniques include, but are not necessarily limited to:
Verify or verification may refer to:
In the context of hardware and software systems, formal verification is the act of proving or disproving the correctness of intended algorithms underlying a system with respect to a certain formal specification or property, using formal methods of mathematics.
Software verification is a discipline of software engineering whose goal is to assure that software fully satisfies all the expected requirements.
In software project management, software testing, and software engineering, verification and validation (V&V) is the process of checking that a software system meets specifications and requirements so that it fulfills its intended purpose. It may also be referred to as software quality control. It is normally the responsibility of software testers as part of the software development lifecycle. In simple terms, software verification is: "Assuming we should build X, does our software achieve its goals without any bugs or gaps?" On the other hand, software validation is: "Was X what we should have built? Does X meet the high-level requirements?"
In software testing, test automation is the use of software separate from the software being tested to control the execution of tests and the comparison of actual outcomes with predicted outcomes. Test automation can automate some repetitive but necessary tasks in a formalized testing process already in place, or perform additional testing that would be difficult to do manually. Test automation is critical for continuous delivery and continuous testing.
Data migration is the process of selecting, preparing, extracting, and transforming data and permanently transferring it from one computer storage system to another. Additionally, the validation of migrated data for completeness and the decommissioning of legacy data storage are considered part of the entire data migration process. Data migration is a key consideration for any system implementation, upgrade, or consolidation, and it is typically performed in such a way as to be as automated as possible, freeing up human resources from tedious tasks. Data migration occurs for a variety of reasons, including server or storage equipment replacements, maintenance or upgrades, application migration, website consolidation, disaster recovery, and data center relocation.
In computer science, data validation is the process of ensuring data has undergone data cleansing to ensure they have data quality, that is, that they are both correct and useful. It uses routines, often called "validation rules", "validation constraints", or "check routines", that check for correctness, meaningfulness, and security of data that are input to the system. The rules may be implemented through the automated facilities of a data dictionary, or by the inclusion of explicit application program validation logic of the computer and its application.
A source document is a document in which data collected for a clinical trial is first recorded. This data is usually later entered in the case report form. The International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH-GCP) guidelines define source documents as "original documents, data, and records." Source documents contain source data, which is defined as "all information in original records and certified copies of original records of clinical findings, observations, or other activities in a clinical trial necessary for the reconstruction and evaluation of the trial."
In the experimental (non-clinical) research arena, good laboratory practice or GLP is a quality system of management controls for research laboratories and organizations to ensure the uniformity, consistency, reliability, reproducibility, quality, and integrity of products in development for human or animal health through non-clinical safety tests; from physio-chemical properties through acute to chronic toxicity tests.
An electronic data capture (EDC) system is a computerized system designed for the collection of clinical data in electronic format for use mainly in human clinical trials. EDC replaces the traditional paper-based data collection methodology to streamline data collection and expedite the time to market for drugs and medical devices. EDC solutions are widely adopted by pharmaceutical companies and contract research organizations (CRO).
A transcription error is a specific type of data entry error that is commonly made by human operators or by optical character recognition (OCR) programs. Human transcription errors are commonly the result of typographical mistakes; putting one’s fingers in the wrong place while touch typing is the easiest way to make this error. Electronic transcription errors occur when the scan of some printed matter is compromised or in an unusual font – for example, if the paper is crumpled, or the ink is smudged, the OCR may make transcription errors when reading.
A clinical data management system or CDMS is a tool used in clinical research to manage the data of a clinical trial. The clinical trial data gathered at the investigator site in the case report form are stored in the CDMS. To reduce the possibility of errors due to human entry, the systems employ various means to verify the data. Systems for clinical data management can be self-contained or part of the functionality of a CTMS. A CTMS with clinical data management functionality can help with the validation of clinical data as well as helps the site employ for other important activities like building patient registries and assist in patient recruitment efforts.
Post-silicon validation and debug is the last step in the development of a semiconductor integrated circuit.
Verification and validation are independent procedures that are used together for checking that a product, service, or system meets requirements and specifications and that it fulfills its intended purpose. These are critical components of a quality management system such as ISO 9000. The words "verification" and "validation" are sometimes preceded with "independent", indicating that the verification and validation is to be performed by a disinterested third party. "Integration verification and validation" can be abbreviated as "IV&V".
Laboratory quality control is designed to detect, reduce, and correct deficiencies in a laboratory's internal analytical process prior to the release of patient results, in order to improve the quality of the results reported by the laboratory. Quality control (QC) is a measure of precision, or how well the measurement system reproduces the same result over time and under varying operating conditions. Laboratory quality control material is usually run at the beginning of each shift, after an instrument is serviced, when reagent lots are changed, after equipment calibration, and whenever patient results seem inappropriate. Quality control material should approximate the same matrix as patient specimens, taking into account properties such as viscosity, turbidity, composition, and color. It should be simple to use, with minimal vial-to-vial variability, because variability could be misinterpreted as systematic error in the method or instrument. It should be stable for long periods of time, and available in large enough quantities for a single batch to last at least one year. Liquid controls are more convenient than lyophilized (freeze-dried) controls because they do not have to be reconstituted, minimizing pipetting error. Dried Tube Specimen (DTS) is slightly cumbersome as a QC material but it is very low-cost, stable over long periods and efficient, especially useful for resource-restricted settings in under-developed and developing countries. DTS can be manufactured in-house by a laboratory or Blood Bank for its use.
GIMIAS is a workflow-oriented environment focused on biomedical image computing and simulation. The open-source framework is extensible through plug-ins and is focused on building research and clinical software prototypes. Gimias has been used to develop clinical prototypes in the fields of cardiac imaging and simulation, angiography imaging and simulation, and neurology
Clinical data management (CDM) is a critical process in clinical research, which leads to generation of high-quality, reliable, and statistically sound data from clinical trials. Clinical data management ensures collection, integration and availability of data at appropriate quality and cost. It also supports the conduct, management and analysis of studies across the spectrum of clinical research as defined by the National Institutes of Health (NIH). The ultimate goal of CDM is to ensure that conclusions drawn from research are well supported by the data. Achieving this goal protects public health and increases confidence in marketed therapeutics.
An electronic trial master file (eTMF) is a trial master file in electronic format. It is a type of content management system for the pharmaceutical industry, providing a formalized means of organizing and storing documents, images, and other digital content for pharmaceutical clinical trials that may be required for compliance with government regulatory agencies. The term eTMF encompasses strategies, methods and tools used throughout the lifecycle of the clinical trial regulated content. An eTMF system consists of software and hardware that facilitates the management of regulated clinical trial content. Regulatory agencies have outlined the required components of eTMF systems that use electronic means to store the content of a clinical trial, requiring that they include: Digital content archiving, security and access control, change controls, audit trails, and system validation.
Business Process Validation (BPV) is the act of verifying that a set of end-to-end business processes function as intended. If there are problems in one or more business applications that support a business process, or in the integration or configuration of those systems, then the consequences of disruption to the business can be serious. A company might be unable to take orders or ship product – which can directly impact company revenue, reputation, and customer satisfaction. It can also drive additional expenses, as defects in production are much more expensive to fix than if identified earlier. For this reason, a key aim of Business Process Validation is to identify defects early, before new enterprise software is deployed in production so that there is no business impact and the cost of repairing defects is kept to a minimum.
References
- ↑ "What is Enterprise Content Management".
- ↑ Andersen JR, Byrjalsen I, Bihlet A, Kalakou F, Hoeck HC, Hansen G; et al. (2015). "Impact of source data verification on data quality in clinical trials: an empirical post hoc analysis of three phase 3 randomized clinical trials". Br J Clin Pharmacol. 79 (4): 660–8. doi:10.1111/bcp.12531. PMC 4386950 . PMID 25327707.
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