Search Results for "python user interface"
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Showing 5 open source projects for "python user interface"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Securden Privileged Account ManagerDiscover and manage administrator, service, and web app passwords, keys, and identities. Automate management with approval workflows. Centrally control, audit, monitor, and record all access to critical IT assets.
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SerialToFS
Program handling file operations when interfacing embedded system.
Program handling file operations when interfacing embedded system by serial port. Main intention for creation of this program was getting files with code coverage out of the embedded system memory. -
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The Automatic JUnit Creation Tool analyzes java classes to map all possible test branches. The tool then guides users through the generation of a JUnit test.
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Covered is a Verilog code coverage utility using VCD/LXT/FST dumpfiles (or VPI interface) and the design to generate line, toggle, memory, combinational logic, FSM state/arc and assertion coverage report metrics viewable via GUI or ASCII format. This project is ported to github and can be found at: https://github.com/chiphackers/covered
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Javelina is an advanced code coverage tool that uses dynamic instrumentation to minimize the overhead for collecting coverage data. Additionally, Javelina allows the application of logical operations to code coverage data.
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SoftCo: Enterprise Invoice and P2P Automation SoftwareSoftCo Accounts Payable Automation processes all PO and non-PO supplier invoices electronically from capture and matching through to invoice approval and query management. SoftCoAP delivers unparalleled touchless automation by embedding AI across matching, coding, routing, and exception handling to minimize the number of supplier invoices requiring manual intervention. The result is 89% processing savings, supported by a context-aware AI Assistant that helps users understand exceptions, answer questions, and take the right action faster.
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A set of scripts, based around doctest, for performing regression testing & code coverage analysis on Python modules.
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