Cross CMS plugin development using AWF

Oct 26, 2015Download as PPTX, PDF2 likes2,486 views

Presented at WordPress Sydney User Group, Tim Plummer talks about his experiences using Akeeba Web Framework (AWF). AWF is a web application framework for single source standalone web applications, Joomla! components and WordPress plugins.

Cross-CMS Plugin Development
using AWF
Presented by Tim Plummer
26/10/2015

My Holy Grail
• A framework for building applications that are
cross-CMS compatible

Why?
• Single codebase to maintain
• Less code = less bugs
• Platform independent – let the web developer
choose the best CMS for that particular
project.
• Larger potential customer base

How
• Akeeba Web Framework (AWF)
A web application framework for single source
standalone web applications, Joomla! components
and WordPress plugins.
• WordPress 3.8.0 or greater
• GPL
• Requires PHP 5.3+

Who wrote AWF?
• Written by Nicholas Dionysopoulos from
akeebabackup.com who also has a WordPress
blog http://www.dionysopoulos.me/
• September 2014 – public release of AWF

What can AWF do for WordPress?
• Object Oriented programming
• MVC
• ACL
• Namespaces
• ORM
(This is something a
lot of WP developers
in the community
dream of)

Does it stack up?
• Not quite holy grail, but the closest I’ve seen.
• AWF is good for building back end application,
doesn’t help much with front end.
• AWF works well as stand alone PHP or in
WordPress, but ironically the Joomla
integration doesn’t work well (I recommend
still using FOF for Joomla apps).

What I like
• Code is very similar to FOF
• MVC
• Separation of content from presentation, with
overrides for views and media files
• JSON support

What I think is missing
• JForm equivalent – Lot more code required to
create form fields etc.
• CSV export
• Front end routing

How does it work in WordPress?
• App folder contains AWF,
and files for plugin
• Helper files provide
wrapper to bootstrap the
application

Where do I get AWF?
• http://akeebabackup.com/download.html
• https://github.com/akeeba/awf

Where do I get AWF?
• http://akeebabackup.com/download.html
• https://github.com/akeeba/awf
Demo time…

Can you do me a favour?
• I’m looking for people to help me beta test BF
Survey for WordPress before I release it. I
need your feedback, for example what
features should be in free version vs paid
version.
• Register via bfsurvey.com

Where can I get AWF?
• https://www.akeebabackup.com/download.ht
ml
• https://github.com/akeeba/awf
Tim Plummer
www.timplummer.com.au
@bfsurvey

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Join Ajay Sarpal and Miray Vu to learn about key Marketo Engage enhancements. Discover improved in-app Salesforce CRM connector statistics for easy monitoring of sync health and throughput. Explore new Salesforce CRM Synch Dashboards providing up-to-date insights into weekly activity usage, thresholds, and limits with drill-down capabilities. Learn about proactive notifications for both Salesforce CRM sync and product usage overages. Get an update on improved Salesforce CRM synch scale and reliability coming in Q2 2025. Key Takeaways: Improved Salesforce CRM User Experience: Learn how self-service visibility enhances satisfaction. Utilize Salesforce CRM Synch Dashboards: Explore real-time weekly activity data. Monitor Performance Against Limits: See threshold limits for each product level. Get Usage Over-Limit Alerts: Receive notifications for exceeding thresholds. Learn About Improved Salesforce CRM Scale: Understand upcoming cloud-based incremental sync.

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Tests, especially unit tests, are the developers’ superheroes. They allow us to mess around with our code and keep us safe. We often trust them with the safety of our codebase, but how do we know that we should? How do we know that this trust is well-deserved? Enter mutation testing – by intentionally injecting harmful mutations into our code and seeing if they are caught by the tests, we can evaluate the quality of the safety net they provide. By watching the watchmen, we can make sure our tests really protect us, and we aren’t just green-washing our IDEs to a false sense of security. Talk from SciFiDevCon 2025 https://www.scifidevcon.com/courses/2025-scifidevcon/contents/680efa43ae4f5

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Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi

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Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak

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.

Exceptional Behaviors: How Frequently Are They Tested? (AST 2025)Andre Hora

Exceptions allow developers to handle error cases expected to occur infrequently. Ideally, good test suites should test both normal and exceptional behaviors to catch more bugs and avoid regressions. While current research analyzes exceptions that propagate to tests, it does not explore other exceptions that do not reach the tests. In this paper, we provide an empirical study to explore how frequently exceptional behaviors are tested in real-world systems. We consider both exceptions that propagate to tests and the ones that do not reach the tests. For this purpose, we run an instrumented version of test suites, monitor their execution, and collect information about the exceptions raised at runtime. We analyze the test suites of 25 Python systems, covering 5,372 executed methods, 17.9M calls, and 1.4M raised exceptions. We find that 21.4% of the executed methods do raise exceptions at runtime. In methods that raise exceptions, on the median, 1 in 10 calls exercise exceptional behaviors. Close to 80% of the methods that raise exceptions do so infrequently, but about 20% raise exceptions more frequently. Finally, we provide implications for researchers and practitioners. We suggest developing novel tools to support exercising exceptional behaviors and refactoring expensive try/except blocks. We also call attention to the fact that exception-raising behaviors are not necessarily “abnormal” or rare.

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