In today's IT landscape, delivery speed and software quality are critical factors for business success. However, maintaining high-quality standards while containing development costs and timelines remains a constant challenge. Innovaway, leveraging its extensive experience, is driving the evolution of software testing. We achieve this by strategically integrating Artificial Intelligence (AI) to transform client challenges into tangible opportunities for operational excellence.
Navigating the New Challenges of the Software Development Life Cycle (SDLC)
Our clients routinely face the need to optimize their software's lifecycle. Traditional approaches, often characterized by increased effort to boost quality or, conversely, reduced quality due to decreased effort, are no longer sustainable. The objective is to achieve high software quality with reduced time and costs, thereby maximizing efficiency.
The Innovaway Approach: Beyond Manual Testing and Automation
Innovaway has mapped out a clear path for the evolution of testing. Starting from manual testing, which, while ensuring quality, involves high overall costs and lengthy timelines, we transitioned to test automation to make the process more efficient. Now, we are taking the next step, integrating Artificial Intelligence to optimize the entire testing process, overcoming the inherent limitations of manual testing and enhancing automation.
The limitations of manual testing are well-known: human errors, low scalability, difficulty in repeatability and traceability, limited coverage, cognitive biases, prolonged timelines, and challenging bug identification. While automation has already introduced significant advantages, such as rapid execution and the possibility of integration with shared repositories, AI amplifies these benefits exponentially.
Innovaway's Distinctive Testing Expertise
Innovaway boasts comprehensive methodologies and practices dedicated to Quality Assurance (QA), forming the foundation of our delivery excellence:
The Added Value of AI in Testing: A Real-World Case
Innovaway conducted a pilot project in the banking sector to evaluate the impact of an AI-based solution in a User Acceptance Testing (UAT) project. The objective was to analyse the impact on delivery times, costs, and efficiency compared to traditional solutions.
The project compared three models: Manual Testing, Automated Testing, and AI-based Testing. We observed significant results:
Current Challenges and Next Steps
It is crucial to recognise that AI applied to testing is still in an "evolving” domain. The decision-making mechanisms of the AI engine are not always precise, sometimes requiring manual corrections to the generated outputs. Furthermore, the quality of results heavily depends on the quality of input data ("Garbage in, garbage out"). Additionally, the study was conducted in a controlled environment, and the results depend on the specific context and the effectiveness of the SDLC processes in which AI is integrated.
Innovaway is already looking ahead, aiming to continuously improve our AI-based testing solutions and define a clear strategy for AI application in the Software Testing sector, anticipating a future where AI will drive both software development and testing.
Artificial Intelligence is profoundly transforming software testing; it's not just a technological trend. Innovaway is at the forefront of this transformation. With our expertise and innovative approach, we are the ideal partner for companies looking to optimize their software lifecycle and achieve unprecedented levels of efficiency and quality.
