Navigating Complex Systems: Lessons from the Digital Infrastructure for the Environmental Planning Act and the Role of AI

The digital system for the Environmental Planning Act consists of a large number of interconnected systems. The realization of this system took many years. On January 1st of this year, the law came into effect, and the system was put into operation. Until the very end, there were concerns, including from the Senate, about whether this was a responsible decision. These concerns were difficult to fully alleviate, despite a learning approach and enormous testing efforts. I would like to share our experiences from this interesting process and reflect on how things can be improved and what role AI can play in this.

Panel member

An AI Experiment: building a Test Automation Framework using Generative AI

Like many testers, I was initially skeptical about AI’s role in our field. Could it really enhance our work, or is it just another overhyped technology that disrupts our jobs and compromises the quality we strive to maintain? To confront these doubts, I conducted an experiment to see if AI truly delivers on its promises or if it’s a threat to our profession.

In this presentation, I will walk you through the steps I took to build a test automation framework using ChatGPT, sharing not only the insights and challenges encountered along the way, but also the failures and the problem-solving approaches.
The framework is intended for UI testing and it is based on Java, Selenide and Gradle.
My experiment didn’t just test AI’s capabilities; it exposed the gains and debts we face when using AI in testing.
Did the experiment succeed in proving that AI can help, or did it confirm the fears that AI is more of a hindrance than a help? If it did succeed, under what conditions? If it didn’t, what were the reasons, and what does this mean for the future of testing? I will expose the results during the presentation.

Transforming the Tester Role

In this presentation, we explore the impact of Artificial Intelligence (AI) and Large Language Models (LLM) on the field of testing. Based on our own experiences and personal journeys, we will share how AI can assist testers in various areas such as requirements review, test case creation, test automation code, and bug ticket creation.

Combining practical examples with theoretical insights, we provide an overview of the possibilities and limitations of AI in software testing. Additionally, we talk about best practices and techniques such as prompt engineering, essential for effectively utilizing AI tools in the testing process.

We will include a 15-minute discussion based on open questions such as:

• What are the main challenges in integrating AI into testing processes?
• How can testers adapt their skills to effectively use AI tools?
• What ethical considerations should be taken into account when using AI in testing?
• What are your thoughts on how the tester role will transform due to AI in the near future?

Applying Behaviour-Driven Development for Game Creation

How to test computer games? In computer games, a player interacts with advanced (AI) agents, and deals with extensive game worlds. While computer games can be immensely complex, and bugs show up in well-known games, testing has not been picked up as much in the game software engineering community, as it has in traditional software engineering. In this talk I will show how Behavior-Driven Development, which is a popular technique for specification and testing in traditional software engineering, can be applied in game software engineering as well. Specifically, I will present the highlights of (i) a framework to help express game behaviors as BDD scenarios, (ii) a method to apply BDD in game development, and (iii) tooling to apply BDD in Unity 3D, a major game development platform.

A Novel Assessment Tool: Enhancing Test Automation Frameworks with Twelve-Factor App Principles

In this digital era, software application is delivered as web applications or Software-as-a-Service (SaaS). This confirms the fact that the priority of software development should be on scalability, portability, and agile development. The Twelve-Factor App methodology gives a standard guideline for the development of such applications that are in line with these objectives. This presentation sheds light on how these principles can be extended beyond application development and used to build and maintain effective test automation frameworks.

For the first time, this tool introduces the use of the Twelve-Factor principles in quality engineering practices. I developed an assessment tool that helps you to evaluate the level of your current test automation framework’s compliance with the Twelve-Factor App methodology across various environments. This assessment, combined with a Large Language Model (LLM) analysis of the assessment answers, will reveal your framework’s strengths and growth areas. Furthermore, the tool will also have recommendations and action steps for implementing the principles of the Twelve-Factor “applicable practices” in test automation.
The analysis results provided by the LLM offer deep insights to optimize test automation strategies. This optimization translates into significant improvement to the overall test automation framework. The identified strengths and growth areas will be translated into clear recommendations and action items, which guide teams on how to implement these improvements in their test environments.

This solution is generic and can be used by any team that is developing or maintaining a test automation framework. It is mainly relevant for software development and quality engineering teams that are looking to improve their test automation strategies and guarantee that their frameworks are prepared for the needs of modern application development. The adoption of this new assessment will improve the scalability, portability, and agility of the test automation frameworks. Thus, they will be in line with the highest standards of software delivery.

Explore the Power of Generative AI

This presentation explores the transformative advancements in
generative AI technology. It introduces large language models (LLMs)
like GPT, Gemini, and Claude, explaining their training processes and
the powerful Transformer architecture enabling near-human performance on
various tasks. 

The presentation traces the evolution of agents from
human-operated systems to sophisticated AI agents, highlighting AI’s
growing role in traditionally human tasks. It examines emerging
AI-enabled interfaces that revolutionize human-computer interaction,
making technology more intuitive and accessible. Practical applications
of AI, including robots trained via video recognition and advanced
self-driving car systems, are showcased to emphasize AI’s tangible
benefits in real-world scenarios. Participants will gain valuable
insights and practical knowledge applicable across various fields and
industries.