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This paper discusses the limitations of modern web application frameworks in providing static verification and consistency checking in web applications developed with domain-specific languages. It proposes a solution through the design of WebDSL, a domain-specific language that integrates various sublanguages and enables upfront static verification, ensuring that inconsistencies are detected early with clear, domain-specific error messages.
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01
Identify the requirements for the data consistency check.
02
Gather the data that requires consistency checking.
03
Define the rules and criteria for static consistency.
04
Implement the necessary tools or scripts to automate the consistency check.
05
Run the static consistency checker on the collected data.
06
Review the results for any inconsistencies or violations of the defined rules.
07
Document the findings and take necessary corrective actions if needed.

Who needs static consistency checking of?

01
Software engineers and developers working on codebases that require consistency.
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Data analysts who need to ensure the accuracy and reliability of datasets.
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Quality assurance teams responsible for maintaining standards in software applications.
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Project managers who oversee projects involving data integrity.
05
Organizations implementing compliance checks for regulatory requirements.
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Static consistency checking is a process that ensures the data and information in a system adhere to predetermined rules and standards, ensuring accuracy and compliance.
Entities or organizations that are subject to regulatory or compliance requirements must file static consistency checking as mandated by relevant authorities.
To fill out static consistency checking, follow the prescribed format provided by the relevant authority, ensuring all required fields are completed accurately.
The purpose of static consistency checking is to validate the integrity of data, ensure compliance with regulations, and identify any discrepancies or errors in records.
Information reported must typically include entity details, data metrics, compliance status, and any identified inconsistencies or errors.
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