Oracle Forms: create debug triggers

Nov 21, 2015Download as ppt, pdf

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Triggers in Form Builder are used to perform actions when events occur. This document discusses how to create triggers, the different types of triggers like When-Button-Pressed and When-Window-Closed, and how to debug triggers using the PL/SQL debugger. Form Builder variables and built-in subprograms can be used within triggers. Debugging tools like breakpoints allow stepping through trigger code line-by-line to identify errors.

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Objectives
• How to Create Triggers?
• Writing trigger code
• Describe Built-in sub-programs used in Forms
• When-Button-Pressed Trigger
• When-Window-Closed Trigger
• Debugging Triggers

Trigger PL/SQL Block
• DECLARE
------- Declarative statements (OPTIONAL)
• BEGIN
------- Executable statements (MANDATORY)
• EXCEPTION
--------- Exception Handling. (OPTIONAL)
• END;

Variables in Form Builder
• PL/SQL variables must be declared in triggers or must be defined in a
package
• Form Builder variables not required to be declared
– Requires a Colon Prefix

Form Builder Variables
• Items
For presentation and user interaction :block_name.item_name
• Global variables
Session-wide character variable :GLOBAL.var_name
• System variables
Form status and control :SYSTEM.var_name
• Parameters
Passing values in and out of module :PARAMENTER.parameter

Form Builder Built-in Subprograms
Built-ins belong to either:
• The Standard Extensions package where no prefix is required
• Another Form Builder package where a prefix is required
Built-in
Restricted
Built-ins
Unrestricted
Built-ins
Allowed only
In certain
Triggers
Allowed In Any
Trigger or
subprogram

Built-in Subprograms
• EDIT_TEXTITEM
• ENTER_QUERY
• EXECUTE_QUERY
• EXIT_FORM
• GO_BLOCK
• GO_ ITEM
• NEXT_RECORD
• PREVIOUS_RECORD
• MESSAGE
• SHOW_ALERT
• SHOW_ EDITOR
• SHOW_ LOV
• SHOW_VIEW
• HIDE_VIEW
• GET_ITEM_PROPERTY
• SET_ITEM_PROPERTY

When-Button-Pressed Trigger
• Fires when the operator clicks a button.
• Accepts restricted and unrestricted
built-ins.
• Used for existing form, Display LOVs, Editors etc.
Example
GO_BLOCK(‘EMP’);
EXECUTE_QUERY;

When-Window-Closed Trigger
• Fires when the operator closes a window by clicking button.
• Accepts restricted and unrestricted built-ins.
• Used to programmatically close a window when the operator issues a
window manager-specific close command. You can close a window by
using built-ins.
When-Window-Closed
Trigger at Form Level.
EXIT_FORM;

Debugging Triggers
• Monitor and Debug Triggers by:
– Compiling and rectifying errors in the PL/SQL Editor
– Displaying debug messages at run time
– Invoking the PL/SQL Debugger

Debugging Triggers -Tips
• Connect to Database before compiling SQL statements.
• Define triggers always at the correct level
• Terminate statements with semicolon where ever necessary.
• Watch for missing quotation marks
• Place the triggers where the event will happen.

Running a Form In Debug Mode
Forms
Debugger
Window
Run Form Debug

Debug Triggers and Breakpoint
Every
statement
Debugger
Program Unit
Statement
Statement
Statement
Debug
trigger
Breakpoint
trigger
Fire
Fire
Fire
Breakpoint

PL/SQL Debugger
Step Into
Step Over
Step Out
Go Reset
Debug Commands

Summary
• Creating Trigger
• From Built-ins
• Restricted and Unrestricted built-ins
• When-Button-Pressed trigger
• When-Window-Closed Trigger

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Humans vs AI Call Agents - Qcall.ai's Special ReportUdit Goenka

Humans vs Agentic AI Cost Comparison: Cut 85% Support Costs TL;DR: Agentic AI crushes human costs in L1/L2 support with 85% savings, 300% efficiency gains, and 90%+ success rates. Your CFO just asked: "Why spend $2.3M annually on L1 support when competitors automated 80% for under $200K?" The humans vs agentic AI cost debate isn't theoretical - it's happening now. Real Cost Breakdown: - India L1/L2 Agent: ₹6,64,379 ($8,000) annually (full cost) - US L1/L2 Agent: $70,000 annually (loaded cost) - Qcall.ai Agentic AI: ₹6/min ($0.07/min) = 85% savings Game-Changing Results: - 47 human agents → 8 agents + AI system - Operating costs: $2.8M → $420K (85% reduction) - Customer satisfaction: 78% → 91% - First call resolution: 72% → 94% - Languages: 2-3 → 15+ fluently Why Agentic AI Wins: - Human Limitations: 8hrs/day, 1-2 languages, 30-45% attrition, mood-dependent, 6-12 weeks training - Qcall.ai Advantages: 24/7 availability, 97% humanized voice, <60 second response, intelligent qualification, seamless handoff Industry Impact: - E-commerce: 82% cost reduction - Healthcare: 90% appointment automation - SaaS: 23% satisfaction improvement - Financial: 95% inquiry automation Implementation: - Setup: 2-5 days vs 6-12 weeks hiring - ROI: Break-even in 3 months - Scaling: Instant global expansion Competitive Gap: - Early Adopters: 85% cost advantage, superior metrics, faster expansion Late Adopters: Higher costs, limited scalability, compounding disadvantage Similar automation patterns emerge across industries. Marketing teams use autoposting.ai for social media automation - same human-AI collaboration model driving L1/L2 transformation. "We reduced support costs by 78% while improving satisfaction by 31%. ROI was immediate and keeps compounding." - COO, Global SaaS Company Getting Started: Calculate current L1/L2 costs Assess repetitive tasks Request Qcall.ai demo Plan 20% pilot Bottom Line: Companies avoiding this shift hemorrhage millions while competitors scale globally with fraction of costs. The question isn't whether agentic AI will replace routine tasks - it's whether you'll implement before competitors gain insurmountable advantage. Ready to transform operations? This reveals complete cost analysis, implementation strategy, and competitive advantages.

Azure AI Foundry: The AI app and agent factoryMaxim Salnikov

Discover how Azure AI Foundry is transforming the way organizations innovate and operate. This session explores the pivotal role of AI in reshaping business processes, enhancing employee experiences, and redefining customer engagement. Attendees will gain insights into the strategic value of AI investments, including the measurable returns and the growing momentum behind generative AI experimentation. The talk introduces the emerging paradigm of agentic AI - intelligent agents that are revolutionizing how we build, manage, and interact with software. Through real-world examples, the session demonstrates how Azure AI Foundry empowers teams to design, customize, and scale AI applications and agents, unlocking new levels of efficiency and creativity across industries.

Test Case Design Techniques – Practical Examples & Best Practices in Software...Muhammad Fahad Bashir

This presentation was part of the SQA & PM Bootcamp, where I served as a trainer. It focuses on effective test case design techniques, blending theoretical knowledge with practical application, especially useful for manual testers and QA beginners. Video Lecture Recording : https://www.facebook.com/share/v/1Z44DiXN5v/ What’s Covered: 🧾 Definition and Purpose of Test Case Design 🚀 Importance of Structured Test Design 🧠 Test Case Design Techniques Overview: ✅ Black Box Techniques ✅ White Box Techniques ✅ Experience-Based Techniques 📝 Manual Test Case Design in Detail 🔐 Real-World Example: Login Page Test Case 🧩 Black Box Methods Explained with Examples: Equivalence Partitioning (EP) Boundary Value Analysis (BVA) Difference Between EP & BVA 📊 Decision Table Testing 💡 Experience-Based Testing: Error Guessing, Exploratory Testing 📂 White Box Techniques (Introductory Overview) ➕ Much More to Help You Build Strong Manual Testing Skills This resource is especially helpful for students, aspiring QA professionals, and junior testers looking to master test design fundamentals with clarity and practical insight.

OpenChain Webinar - AboutCode - Practical Compliance in One Stack – Licensing...Shane Coughlan

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Building Geospatial Data Warehouse for GIS by GIS with FMESafe Software

Data warehouses are often considered the backbone of data-driven decision-making. However, traditional implementations can struggle to meet the iterative and dynamic demands of GIS operations, particularly for Iterative Cross-Division Data Integration (ICDI) processes. At WVDOT, we faced this challenge head-on by designing a Geospatial Data Warehouse (GDW) tailored to the needs of GIS. In this talk, we’ll share how WVDOT developed a metadata-driven, SESSI-based (Simplicity, Expandability, Spatiotemporality, Scalability, Inclusivity) GDW that transforms how we manage and leverage spatial data. Key highlights include: - Simplifying data management with a lean schema structure. - Supporting ad hoc source data variations using JSON for flexibility. - Scaling for performance with RDBMS partitioning and materialized views. - Empowering users with FME Flow-based self-service metadata tools.We’ll showcase real-world use cases, such as pavement condition data and LRS-based reporting, and discuss how this approach is elevating GIS as an integrative framework across divisions. Attendees will walk away with actionable insights on designing GIS-centric data systems that combine the best of GIS and data warehouse methodologies.

Modern Platform Engineering with Choreo - The AI-Native Internal Developer Pl...WSO2

Building and operating internal platforms has become increasingly complex — sprawling toolchains, rising costs, and fragmented developer experiences are slowing teams down. It’s time for a new approach. This slide deck explores modern platform engineering with Choreo, WSO2’s AI-native Internal Developer Platform as a Service. Discover how you can streamline platform operations, empower your developers, and drive faster innovation — all while reducing complexity and cost. Learn more: https://wso2.com/choreo/platform-engineering/

Threat Modeling a Batch Job Framework - Teri Radichel - AWS re:Inforce 20252nd Sight Lab

This presentation is similar to another presentation I gave earlier in the year at the AWS Community Security Day, except that I had less time to present and incorporated some AI slides into the presentation. In this presentation I explain what batch jobs are, how to use them, and that AI Agents are essentially batch jobs. All the same security controls that apply to batch jobs also apply to AI agents. In addition, we have more concerns with AI agents because AI is based on a statistical model. That means that depending on where and how AI is used we lose some of the reliability that we would get from a traditional batch job. That needs to be taken into consideration when selecting how and when to use AI Agents as batch jobs and whether we should use AI to trigger an Agent. As I mention on one side - I don't want you to predict what my bank statement should look like. I want it to be right! By looking at various data breaches we can determine how those attacks worked and whether the system we are building is susceptible to a similar attack or not.

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From Data Preparation to Inference: How Alluxio Speeds Up AIAlluxio, Inc.

Alluxio Webinar June 17, 2025 For more Alluxio Events: https://www.alluxio.io/events/ Speaker: Jingwen Ouyang (Sr. Product Manager @ Alluxio) In this talk, Jingwen Ouyang, Senior Product Manager at Alluxio, will share how Alluxio make it easy to share and manage data from any storage to any compute engine in any environment with high performance and low cost for your model training, model inference, and model distribution workload.

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University Campus Navigation for All - Peak of Data & AISafe Software

Navigating around the 1000 acres of UBC Vancouver’s campus may look like a walk in a park, but when you’re a student in a wheelchair trying to find a powered door to the class you’re late for, it is not all that scenic. UBC’s GIS team will share the journey from the original 2003 static map application to the current web application built using FME and the ArcGIS JavaScript API. There were two main goals with this new application: 1. Enable the UBC community to easily interact with and navigate the campus by walking, cycling and accessible means (which include mobility constraints such as steep slopes, stairs, and doors that are not powered). 2. Provide high quality geospatial data that is authoritative and updated using FME to reflect existing conditions on campus including road, sidewalk or building closures for construction. We will cover the unique challenges in creating a navigation application which offers features that Google Maps cannot at the scale of a University campus. Door to door navigation, avoiding stairs and steep slopes, navigating to accessible entrances and up to date construction barriers are some of the features we will discuss.

Why Every Growing Business Needs a Staff Augmentation Company IN USA.pdfmary rojas

U.S. staff augmentation companies excel at providing IT professionals with expertise in development, cybersecurity, AI, and more. Whether you need to fill a short-term gap or enhance your team’s capabilities long-term, these firms deliver pre-vetted talent tailored to your tech stack and industry. With an understanding of both global technologies and local compliance, U.S.-based IT staff augmentation bridges the gap between talent shortage and project demand efficiently.

Automated Testing and Safety Analysis of Deep Neural NetworksLionel Briand

CodeCleaner: Mitigating Data Contamination for LLM Benchmarkingarabelatso

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A Guide to Telemedicine Software Development.pdfOlivero Bozzelli