Leveraging AI in Software Development: Tips and Considerations

Great software is key to the success of any modern business, which is why many developers have turned to AI-enabled software development to gain a competitive advantage. With the potential to increase efficiency and productivity while improving code quality, artificial intelligence (AI) provides developers with scalable tools capable of producing quality software in a matter of minutes. 

It’s clear that AI-enabled tools will have a major impact on software development, increasing the volume and velocity of code delivery and innovation; however, there are ethical and practical guardrails that need to be considered. 

Tips for Leveraging AI in Software Development

AI's influence in software development reaches much further than code generation. Whether deploying, optimizing, documenting or maintaining code, this technology is reshaping how development teams tackle their projects. For instance, developers can leverage AI to automate monotonous tasks, like code documentation. Similarly, there are software testing tools available today that use AI algorithms to identify patterns, predict potential issues and optimize software by removing unused portions of code.

At the end of the day, understanding the potential for AI to boost developer productivity and efficiency will be critical. Luckily, there are a few tried-and-true ways that modern engineering teams are leveraging this technology to improve product build processes: 
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• Automated Code Generation: As previously mentioned, AI-powered coding tools can help streamline code generation, but let’s dig into this a bit more. It’s paramount that developers understand the limitations of AI-enabled tools and ensure compatibility with existing code. As a result, many developers are opting to use AI to generate more manageable code snippets and templates, as opposed to larger pieces of code. Although convenient to generate entire pieces of software automatically, it’s much easier to refine a set of initial ideas than it is to generate them from scratch. In other words, generative AI (GenAI) models create the “sparks” that a good developer can turn into a full-blown creative process.
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• Intelligent Testing and Quality Assurance: Testing code has notoriously been a monotonous, time-consuming task, but by integrating AI into the process, developers will be able to   automatically analyze data, identify patterns, and predict potential issues in their software. This, in turn, will lead to shorter development cycles, quicker time to market, and provide developers an opportunity to gain additional insights to improve their code. It’s important to keep in mind, however, that balancing automated testing with manual testing is crucial for comprehensive coverage and accurately identifying issues.
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• Predictive Maintenance and Monitoring: AI-enabled tools often leverage historic datasets and patterns, making them great assistants when monitoring and maintaining code. Specifically, these tools can prevent software failures by gathering data, like code changes, bug reports, feedback and systems logs.  Once this data has been processed, the learning model can be used to predict the likelihood of future software failures, effectively arming developers with the information needed to maintain code and optimize performance. Keep in mind that the accuracy of these models depends on data quality, as input directly impacts output. When working with larger datasets, be careful to prioritize data privacy and protect sensitive information from misuse. Data encryption, access controls and anonymization techniques can all help to ensure data privacy while enabling high-quality maintenance and monitoring.

Considerations When Leveraging AI in Software Development

We’ve established that the promise of AI-enabled tools boasts competitive advantages for developers, like improved code quality and increased efficiency and productivity.... but all that glitters is not gold. While AI algorithms strive for efficiency, they may inadvertently generate code with vulnerabilities. Addressing these security concerns requires ongoing effort, thorough testing and strong cybersecurity measures to ensure quality. AI algorithms may also inadvertently perpetuate biases present in the training data, leading to unfair outcomes or discriminatory decisions. To mitigate this risk, developers must proactively identify and address biases by carefully examining training data and implementing measures to correct it. 

Seamlessly integrating AI into existing workflows and tools also requires careful planning and adaptation. Ensuring that AI-powered tools are compatible with existing development frameworks and infrastructure is crucial, along with facilitating interoperability between AI solutions and existing development tools. Additionally, some developers may need to be trained to effectively implement AI into their workflows and remain competitive. Not only are developer workflows changing, but so is technology. This presents another challenge for AI models, as they may struggle to keep up with changes in programming standards (i.e. languages, frameworks, etc.), potentially causing compatibility issues and requiring continuous updates to maintain relevance.

Regulate your workflows with RapidFort

We know that implementing new technology, like GenAI, in your day-to-day business operations can be daunting, but we strongly encourage developers to explore the capabilities of these tools and techniques to augment their workflows. That said, if we want to leverage AI responsibly and effectively, it’s critical that we’re fully aware of the limitations. If you’re looking to get the most bang for your buck when it comes to your AI-enabled toolsets, consider investing in RapidFort’s Software Attack Surface Management (SASM) platform.

RapidFort arms your business with both run-time and build-time tools, enabling you to scan, understand and harden your software. The ability to automatically scan containers and remove unnecessary software components can reduce software vulnerabilities by 60%-90%. The result? More secure, cost-efficient containers and a significant decrease in vulnerabilities, lightening the load on security teams and streamlining DevSecOps processes.

Ready to improve your workflow with RapidFort? Click here to schedule a demo.