How AI and software development intersect
Though generative AI seemed to emerge from relative obscurity in late 2022, its effects are already significantly impacting software development, enhancing coding efficiency, enabling new development capabilities, increasing productivity, and improving testing and maintenance.
Not only can it help accelerate code development, but generative AI can also aid engineers in creating more valuable, easy-to-use applications that can optimize everything from the customer journey to business operations and reporting.
Applications built with generative AI are also proving to be more flexible and scalable, with the ability to continuously learn, adapt, and evolve as users’ needs change. That will allow for greater and faster feature development and rollout, resulting in more seamless user experiences and more up-to-date software, requiring less manual maintenance and versioning.
AI is also streamlining software testing to allow enterprises to move their proofs of concept (POCs) to production faster than ever before and reduce overall time to market. And natural language processing (NLP) is democratizing code development, allowing more users within an organization to contribute to application development and design processes.
However, as AI becomes a more integral facet of development, the teams that use and deploy it will need to evolve to stay up to date on the latest AI tools and techniques and best apply the technology for the enterprise and its customers.
Where AI for software development is most effective
Teams are already leveraging AI technologies to help unlock a wide range of software development benefits. Here are a few examples:
Predictive analysis
By analyzing historical data, AI can help predict future development processes and requirements. That can streamline development timelines, pinpoint and prevent potential issues before they arise, and aid in resource planning. AI can also perform ongoing code maintenance to deliver a more reliable, optimized user experience.
Automatic code generation
AI can eliminate time-consuming, repetitive development tasks by automatically creating elements like code snippets and blocks. This can fast-track software creation and introduce more consistency into development processes, while allowing developers to focus on more high-value tasks. In addition, generative AI can analyze code performance on the fly and identify areas for improvement, making software more easily scalable.
Code translation, reviews, and testing
Translating code from one programming language to another can be time-consuming and error-prone. AI tools can make this process faster and more accurate. And as organizations look to update their technology stacks, it can also help with migrating legacy systems to modern platforms.
In addition, AI can quickly compare new code to best practices and coding standards to accelerate and optimize code reviews and reduce errors. Plus, it can automate testing by creating test cases, performing the tests, and generating test data reports. That streamlines the testing phase and can allow for more comprehensive test cases.
Easier deployments
By automating certain deployment tasks and anticipating potential testing and integration issues, AI can optimize continuous integration and continuous deployment pipelines so new software versions can be delivered more quickly and reliably.
More effective teams and greater innovation
As AI tools accelerate coding processes, increase their reliability, and make coding more accessible, developers, engineers, and others throughout the organization can spend more time on high-value strategy and innovation to maximize business value, improve customer experiences, and drive better business outcomes. These process improvements can also help developers expand their own coding knowledge to tackle more complex coding challenges, assist managers in improving resource allocations, and aid in preventing developer burnout over the long-term.
How AI for software development can be less effective
Although AI tools can provide a wide range of helpful ways to improve code development and software releases, they should not be seen as a replacement for the human element. This is especially true for more complex projects where developer expertise is required to maintain overall consistency and ensure certain code requirements are met.
Unintended bias
Though AI models can evolve and learn, they can also inherit unintended biases present in the training data, which can lead to skewed outcomes. For example, inaccurate or biased facial recognition data could lead to misidentification and discrimination and result in privacy and rights violations. Skilled developers are required to troubleshoot such biases to avoid inadvertent or inaccurate results.
Data quality and interpretation
AI models require enormous amounts of data for training. Incomplete or inaccurate data can lead to flawed, subpar results, or sometimes outcomes that can be difficult to explain. Trained developers can spot these flaws and inconsistencies and correct them. They can also investigate unexpected decisions and adjust models accordingly.
Implementing complex code requirements
AI can be very effective at answering straightforward text prompts, but it can struggle with complicated requirements or those that contain ambiguity—situations where human developers can fall back on past experiences or intuition. In short, many AI tools simply lack the creative, problem-solving skills that human developers possess, making complex code requirements difficult to implement properly.
Contextual and organizational understanding
Intricate code often requires specific organizational knowledge and industry expertise to deliver accurate results. AI models may not have the contextual information required to understand and generate code for certain situations or business needs. This can make it challenging to integrate with an organization’s other systems, comply with security and privacy policies, or provide customers with useful, valuable outcomes. Developers are critical in ensuring AI tools can meet and overcome these hurdles.
Ethical issues
One of the biggest challenges facing developers and users of AI is regulation. In December 2023, the European Union agreed on the AI Act, a law designed to limit the use of some AI technologies and protect users against risks, including the spread of AI-powered misinformation and misuse of facial recognition software.
But it can be challenging for developers that work on AI projects to know where the ethical guardrails lie. Human oversight and intervention are critical for creating reliable, accurate, and transparent AI applications that limit the potential for unintended consequences.
How is AI impacting software development roles?
As AI initiatives expand across enterprises, so will development roles and other AI opportunities.
Experienced software architects will be even more critical
Developing and operating AI not only requires large amounts of data, but it also requires systems in place to store that data, make it readily accessible, and integrate it with other technologies and legacy tools, replacing data silos with streamlined, connected solutions. Software architects will be even more important as organizations seek to implement cost-effective data and AI platforms to efficiently develop and deliver AI projects. As an organization’s data needs expand, a well-designed data architecture can adapt and scale accordingly to ensure data availability, integrity, security, and compliance while optimizing resource utilization and storage costs.
Quality assurance (QA) engineers’ roles will evolve
AI will be able to automate many development test cases, but that will first require the right quality assurance expertise to configure those models and monitor them to ensure they’re delivering expected results. As the models become more accurate and reliable, QA engineers will be able to shift their focus from manual testing to more strategic tasks, including comprehensive test scenario design, results analysis, and complex testing scenarios that AI may struggle with.
Security engineers will be tasked with more oversight
As AI plays a growing role in identifying and limiting security vulnerabilities, security engineers must have a full understanding of AI-driven security tools to adapt to evolving threats and potential concerns. That means engineers will need to be even more proactive in identifying and addressing possible security risks in software development and work closely with AI tools to ensure robust safeguarding measures.
User experience (UX) designers will be in higher demand
AI will assume traditional design-related tasks, such as creating website layouts or graphical user interface elements. But that will create more valuable opportunities for UX designers. The ability to analyze user behavior with AI and leverage predictive modeling will enable UX designers to develop more hyper-connected and useful interaction processes to optimize the customer experience and maximize business value.
Project management will become more efficient
Project managers can leverage AI to assist with and streamline project planning. They can use it to analyze historical data to predict potential issues, estimate development timelines, and optimize resource allocation. This informs decision-making and reduces costs.
Preparing for the future of AI in software engineering
AI is poised to change and evolve traditional software development significantly in the coming years. From more streamlined code development to faster time to market for new products, these changes can yield a host of positive outcomes for organizations across industries. But this will also require organizations to prepare their teams for the coming AI-driven decade. Here are a few key steps businesses should focus on:
Ongoing training will be key
Many developers already pursue training and education to keep up with evolving technologies. But AI is advancing so quickly that organizations will likely need to take a more comprehensive and consistent approach so everyone in the enterprise stays aligned on how AI can be leveraged responsibly for specific business and customer needs. This may include team workshops on AI best practices, privacy issues, coding standards, and more. It could also involve ongoing training and coaching to share knowledge, streamline coding, and identify potential development issues early on.
Prepare for adapting roles
AI’s ability to automate many traditional development tasks will alter the makeup and responsibilities of software teams. But that doesn’t mean AI will replace them. Rather, teams should be ready to take on more complex and strategic projects. This will create new opportunities for team members to advance their skills and for managers to realign and staff their teams to meet the organization’s needs as demand for AI applications grows.
The significance of trusted AI
While emerging generative AI and language learning models (LLMs) can speed development and analysis, organizations also need to ensure those analytics can be trusted—and that AI will deliver ethical outcomes that stay compliant with strong model governance. That will require experienced software teams capable of identifying code errors, unexpected results, and unintended biases to deliver trusted AI.
This is a critical convergence of AI technology and those who build it. No matter how advanced the technology is, if it fails to focus on improving people’s lives, users will not view it as ethical or trusted.
FAQ
What new software development tools are using AI?
Machine learning (ML) frameworks, such as TensorFlow, PyTorch, and Keras are key AI development tools. They offer developers consistent libraries to design, build, and train AI applications. A robust cloud-based platform, such as Teradata VantageCloud, is another critical AI development component, providing a streamlined infrastructure to create and deploy AI/ML at scale, as well as integrations with leading cloud services like Amazon Web Services (AWS) and Microsoft Azure.
How can a software engineering leader help their team embrace AI?
Engineering leaders should focus on the benefits of AI for those on their teams. This can include education and training opportunities so team members can advance their skill sets, or mentorship and guidance to share knowledge with newer developers and help them become more comfortable with AI technologies.
Leaders should ensure their teams have access to the necessary tools and resources needed for AI development, including frameworks, libraries, platforms, and other systems to support development. In addition, they should create an integrated development approach that fosters a culture of collaboration between those creating AI applications—including software development teams, data scientists, and other stakeholders—so teams can see how their work is being leveraged throughout the enterprise.
What are some myths around AI for software development?
A common myth is that as AI becomes more capable of automating common development tasks, it will replace human software developers. Though AI can perform straightforward coding and testing tasks, more complex ones will still require human involvement to ensure models are configured properly, errors and bugs are fixed quickly, and unexpected results are explained and corrected.
Another myth is that AI is neutral and objective. Actually, AI models can inherit biases present in training data, so it’s critical for teams to monitor these biases and correct them to ensure accurate and equitable results.
Teradata VantageCloud helps enterprises build and deploy data architectures critical for helping software teams accelerate development and deliver trusted AI. With a cloud-native deployment approach and streamlined data integration capabilities, our platform delivers architecture solutions that streamline development, evolve as your data needs grow, and drive maximum business value. Contact us to learn more about VantageCloud and how we can help you streamline AI development.