In the era when manual testing was the standard, software companies often employed dedicated Quality Assurance (QA) teams to create comprehensive “test plans.” These plans were crucial for ensuring that the features of software projects performed as expected. The QA team would manually execute checklists each time an update was introduced, providing the engineering team with test plan results to address any issues that arose.
This manual testing process proved to be slow, costly, and prone to errors. Particularly challenging was the testing of web routes essential to businesses, where identifying routes likely to fail or those most frequently used by end users presented difficulties.
The advent of automated testing has transformed the landscape of software development, offering a more efficient approach to Quality Assurance and Quality Control (QC) teams. Automated testing guarantees quality at every stage of development, ensuring that software functions correctly before deployment and that updates do not introduce bugs. This streamlined process enables QA and QC teams to concentrate on more critical tasks, enhancing overall efficiency.
In pursuit of efficiency
At Amaris Consulting, we continuously evolve our working methods to achieve optimal results for our clients and partners. In this pursuit of efficiency, our quality assurance team conceived AI4QA, an advanced AI system designed to revolutionize the automated testing process.
Following the successful completion of a complex banking and finance project, the quality assurance team sought to enhance the automated testing process, aiming for simplicity and speed. The objective was to create a system capable of automated code generation and analysis, reducing project duration and allowing automation engineers more time for critical decision-making. Thus, AI4QA was born—a system that not only analyzes but also semantically understands web data.
The magic behind the curtain
The innovative approach taken by AI4QA involves sophisticated data extraction techniques, mimicking user web browsing behavior. By leveraging the latest advancements in machine learning classification, the system can not only analyze but also understand web data on a semantic level. This autonomous problem-solving capability sets AI4QA apart, promising a more intelligent and adaptable automated testing experience.
AI4QA is set to transform how Quality Assurance engineers conduct application testing. While users remain in control of the testing process, the role of QA team members undergoes a radical shift—from coding builders to decision-makers evaluating machine-generated code. This simplifies the process, significantly reducing the time needed to build automated test cases from months to mere weeks. Automation engineers can now focus their efforts on making informed decisions rather than spending extensive time coding.
A continuos evolution in quality assurance
In conclusion, the journey from manual testing to the era of automated testing, exemplified by AI4QA, signifies a paradigm shift in software development. The amalgamation of machine intelligence and human decision-making not only accelerates the testing process but also elevates the role of Quality Assurance engineers.
As AI4QA paves the way for a more efficient future in application testing, it underscores the importance of continuous evolution and adaptation in the ever-changing landscape of software development. Amaris Consulting remains dedicated to providing cutting-edge solutions to engineering challenges. Our focus on innovation ensures that our clients and partners benefit from the latest advancements in technology.
As technology continues to advance, Amaris Consulting stands at the forefront, offering solutions that redefine efficiency and excellence in engineering. Explore the possibilities with us, and experience the transformative power of our engineering solutions.
|Project title (original)
|Desarrollo de sistema basado en Inteligencia Artificial como potenciador de diseño de pruebas de software
|Project title (in English)
|Development of an AI-based system for enhanced design of software testing cases
|Research and Development
|IVACE – Valencian Institute of Business Competitiveness
|I+D EN COOPERACIÓN (PIDCOP-CV) 2021