Rethinking AI’s Impact on Software Development and Testing

Adoption of artificial intelligence isn’t just about learning from customer data or supporting line workers. It’s already making an impact in software development processes.

Jason Goth, Chief Technology Officer and Partner

February 23, 2024

4 Min Read
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Stoycho Stoychev via Alamy Stock

Artificial intelligence has rapidly evolved from a buzzword to a crucial tool in software development and testing, marking a transformative shift in the industry. As someone closely involved in this field, I have observed AI’s role in enhancing various development processes.

AI now significantly empowers coding practices. It aids developers by generating code snippets, providing coding assistance, and optimizing existing code. This advanced support boosts developer productivity by enabling them to focus on the broader aspects of their projects, rather than being entangled in minute details.

Today’s AI tools enhance testing and quality assurance in software development as well. By automating intricate testing procedures, AI introduces a level of systematic precision previously unattainable. This automation enables testers to quickly find and fix bugs, which is crucial for preventing potential issues in the development process.

AI can even supercharge personalized user experiences. While it’s feasible to create software with personalized experiences without AI, the challenge lies in curating sufficient and appropriate content, as well as analyzing user data to determine the most fitting experience for everyone.

Artificial intelligence stands out in its ability to efficiently process vast amounts of user data. It’s not just about managing the sheer volume; AI excels in intelligently discerning patterns and preferences from this data. This advanced analysis enables AI to recommend user experiences that are not only suitably tailored but also richly populated with content that aligns with individual user preferences.

Integrating AI Into Your Software Development

Integrating AI solutions into software development presents a significant challenge for businesses. To justify the investment in AI tools, these solutions must be thoroughly tested, safe, and practically viable. Navigating this challenge requires a well-considered approach, especially in understanding how to effectively implement AI.

Let’s look at three key ideas to consider regarding software development and AI. 

1. Get serious about testing. Testing is essential in software development, especially when using AI-based tools. Artificial intelligence is a relatively new addition to this field, making thorough testing at every step even more crucial. Integrate continuous testing throughout the development lifecycle, rather than treating it as an afterthought. This means implementing testing processes at every stage of development to ensure the quality and effectiveness of the software.

To effectively implement AI in software testing, adopt a comprehensive approach from the very beginning. This includes unit testing, integration testing, and system testing concurrently with the development of the code. The good news is that the necessary tools for systematic testing are readily available.

Continuous integration tools, for instance, can automatically execute tests each time new code is committed. This reduces the reliance on manual testing and accelerates the development timeline. Additionally, it’s crucial to ensure that your automated testing encompasses a broad spectrum, covering functional, performance and security aspects to guarantee the software’s overall integrity and efficiency.

2. Embrace collaboration. Contrary to what one might expect, AI is set to make the future of software development more centered around human collaboration, not less. Emphasizing partnership with the right collaborators will become increasingly vital in the development process. Specifically, it’s crucial to actively seek input from team members at each stage of development, focusing on evaluating outcomes and refining solutions. This collaborative approach should include developers and business owners, but it also should extend to subject matter experts and legal/compliance personnel, ensuring a well-rounded and comprehensive development process.

Collaboration through cross-functional teams is key in software development, especially with AI integration. The true strength of these teams lies in their diversity, with varied perspectives contributing to shaping the software at every project phase. To make this collaboration effective, establish continuous feedback loops. Such mechanisms guarantee the consistent collection and integration of feedback from all stakeholders. This can be achieved through regular surveys, user testing sessions or review meetings, ensuring that every voice is heard and considered in the development process.

3. Be data driven. Decision-making in business inherently involves risk, and this is particularly true when dealing with AI, a relatively new technology. To navigate these risks, it’s crucial to be well-informed by reliable data. Implementing robust data collection methods is essential to this process. Aim to gather the highest quality and most relevant data possible, utilizing analytics tools, customer feedback channels, or automated reporting systems. Establishing these as continuous channels of input allows for more informed and safer decision-making in the context of AI integration in business operations.

Having access to quality data allows for the creation of a framework to evaluate the risks associated with various decisions, especially in the context of AI implementation. Carefully assess the likelihood of undesirable events and understand their potential impact on operations. Additionally, this framework should include strategies for response and mitigation in case these risks materialize. Such proactive planning ensures that the organization is well-prepared to handle challenges and minimize potential damage effectively.

Starting Your AI-Powered Software Development Journey

AI holds the promise of transforming software development, but its success hinges on the right approach. By prioritizing thorough testing, fostering collaboration, and basing decisions on robust data, you can effectively mitigate risks and drive progress. 

Embracing these principles and starting your AI journey now sets the stage for significant achievements in the near future. You’ll likely find that taking this step toward an AI-driven approach is a decision you’ll be glad you made.

About the Author(s)

Jason Goth

Chief Technology Officer and Partner, Credera

Jason Goth is chief technology officer and partner at Credera. With over 25 years of experience, Jason plays an important role helping clients deliver complex technology solutions, modernize legacy environments, and adopt emerging technology. Jason works across all of Credera’s technology teams to align capabilities with market need and ensures that Credera’s resources deliver innovative solutions with high quality and velocity.

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