Dynamic input tables lwc vs aura vs. visualforce

Mar 18, 20191 like364 views

This document summarizes and compares different approaches to implementing dynamic input tables in Lightning Web Components (LWC), Aura, and Visualforce. It begins with an introduction and user story example of needing to bulk input data. It then discusses the basic implementation of a reusable dynamic input table component. The document compares LWC, Aura, and Visualforce implementations, noting differences in attributes, iterators, manipulation, binding, and Apex method importing. It also briefly discusses testing LWC components with LWC-Jest. It concludes with an overview of setting up and deploying the demo application to a scratch org.

Dynamic Input Tables:
LWC vs Aura vs Visualforce
Sean Cuevo
@squizzleflip
socalledprogrammer.com

Agenda
● Introductions
● A Common User Story: Bulk Input
● Dynamic Input Tables
● Comparison of LWC, Aura, and Visualforce
implementations
● Testing your components with LWC-Jest
● Setup and deploying to a Scratch Org
● Questions

Sean Cuevo
Developer with 7 years of
experience
Twitter: @squizzleflip
Blog: socalledprogrammer.com

A Common User Story
As a User, I want to input information about multiple users
at once so that I can quickly capture data (not necessarily
sObject fields) without having to save each user’s data
individually.

Usually End Up Building Something Like This

Basic Implementation
● A table component that accepts a list of desired columns and generates
a table
● An input component that allows you to create dynamic inputs
○ Exists because computed keys are not supported in both aura and
LWC
■ This is not allowed:
<lightning-input value={record[column]}/>
● Top level application that uses table components and aggregates
inputted values

● For front-end behavior,
Visualforce version is basically
front-end JavaScript with a
Remote Action
● Not as “componentized” as the
LWC and Aura versions
● More of a plugin rather than a
component
A Quick Look at Visualforce Implementation

LWC vs. Aura
● Attributes defined in JS file
● Iterators require a unique ID
● Easier manipulation of
attributes with @track
● String API parameters need
to be escaped
● One-Way Binding
● Explicit Apex method
importing
LWC
● Attributes defined in markup
● Query selectors are limited
● A LOT of boilerplate files
● Weird state issues with
defaults
● Two-Way Binding
● Implicit Apex method
importing
Aura

Blog Post: Lightning Web Components - First Impressions
Blog Post: Lightning Web Components - Testing
Run Jest against LWC components in SFDX workspace environment
Demo Application: Dynamic Fields LWC
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As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.

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What Do Contribution Guidelines Say About Software Testing? (MSR 2025)Andre Hora

Software testing plays a crucial role in the contribution process of open-source projects. For example, contributions introducing new features are expected to include tests, and contributions with tests are more likely to be accepted. Although most real-world projects require contributors to write tests, the specific testing practices communicated to contributors remain unclear. In this paper, we present an empirical study to understand better how software testing is approached in contribution guidelines. We analyze the guidelines of 200 Python and JavaScript open-source software projects. We find that 78% of the projects include some form of test documentation for contributors. Test documentation is located in multiple sources, including CONTRIBUTING files (58%), external documentation (24%), and README files (8%). Furthermore, test documentation commonly explains how to run tests (83.5%), but less often provides guidance on how to write tests (37%). It frequently covers unit tests (71%), but rarely addresses integration (20.5%) and end-to-end tests (15.5%). Other key testing aspects are also less frequently discussed: test coverage (25.5%) and mocking (9.5%). We conclude by discussing implications and future research.

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