AI in Software Development: Accelerating CI/CD Pipeline by 55%

OpenAI is constantly releasing new models that are continuously performing better in programming tasks. For example, the latest GPT o1 model replicated a year's worth of a student's work in astrophysics with just 6 prompts and one hour of work.

As LLMs become more and more advanced, developers become increasingly worried that AI will take their jobs. For now, AI is still a tool and not a replacement for employees. However, it's a tool with unprecedented efficiency. With today's AI capabilities, tech companies can double the speed of certain development tasks using Generative AI.

In this article, we'll explore how AI in software development can translate these speed gains into increased productivity, surpassing past advances in engineering productivity through the use of new tools and processes.

What is AI-Augmented Software Development?

AI-augmented software development integrates traditional software methods with AI-powered tools.

For instance, tools like GitHub Copilot and TabNine assist developers by suggesting code snippets, completing functions, and generating entire blocks based on prompts. This speeds up development and reduces the cognitive load on developers.

Will AI Replace Software Engineers?

Let’s address the elephant in the room — if AI can write, review, and document code, what’s the future for software developers?

Despite the progress, current LLMs won’t replace developers anytime soon. Writing documentation or refactoring code is one thing, but making complex changes, understanding business needs, or integrating APIs is a different level of challenge.

Moreover, AI still makes mistakes, requiring human review. So, while AI can help developers be more productive and focus on more challenging problems, it isn’t ready to fully replace them.

Key Benefits of AI in Software Development Processes

Here are key software development activities where AI shines:

• Overcoming Developer's Block: AI can help developers kick-start projects by providing code suggestions, enabling them to start faster and get into the flow.
• Automating Routine Tasks: Generative AI automates repetitive tasks like auto-filling functions, completing code, and documenting, allowing developers to focus on complex, high-value work.
• Enhancing Security and Quality: AI-powered tools like DeepCode detect issues before code merges, enforce security best practices, and provide immediate feedback.
• Enforcing Best Practices: AI ensures compliance with security directives and minimizes the chances of errors that may occur during manual reviews.

How AI is Used in Software Development

AI is a game changer for nearly every aspect of the software development lifecycle. Here’s a deeper look into specific areas of impact:

Code Generation and Completion

AI tools like GitHub Copilot and TabNine provide intelligent code suggestions and autocomplete entire functions. This allows developers to focus on high-level problem-solving and increases coding speed.

By analyzing and learning from massive amounts of code, these tools provide best-practice suggestions, minimize human error, and help companies produce better software faster, giving them an edge.

Code Refactoring

AI helps restructure and modernize code without altering its functionality, automating tasks like simplifying functions, removing redundancies, and improving readability. Tools like Tabnine offer:

• Faster Refactoring: Quickly analyzes codebases to identify areas for improvement.
• Enhanced Code Quality: Consistently applies best practices.
• Reduced Workload: Automates repetitive tasks, freeing developers for complex problems.
• Minimized Errors: Ensures refactoring doesn’t introduce bugs.
• Improved Maintainability: Keeps codebases clean and easy to manage for the long term.

To address inaccuracies, some AI tools introduce fact-checking to reject incorrect refactoring attempts, ensuring up to 98% accuracy.

Automated Testing

AI transforms traditional testing by making it faster and more scalable. Benefits include:

• Scalability and Speed: Tools like Testim.io and Applitools automate test creation and review, reducing manual intervention.
• Adaptability: Algorithms adapt tests to code changes, ensuring faster releases and fewer bugs.
• Agile Integration: AI-driven testing supports continuous integration and deployment, perfect for agile teams.
• Compliance and Quality: Industries like healthcare benefit from AI testing to meet strict standards.

For example, Keith Powe, VP of Engineering at IDT Corporation, says that after implementing an AI-driven testing tool their team has increased automated testing coverage from less than 34-33% to more than 91% in under 9 months. Now, the team spends a maximum of 0.1% of the time in test maintenance.

Predictive Analytics

Beyond automated testing AI can be used to predict everything from deployment issues and resource needs to user demand by analyzing historical data to find patterns. This predictive power applies to almost any industry that has large amounts of data, not just software development.

For AI in software development the impact is felt more in project management and resource allocation where delays or bottlenecks can impact deadlines and budget. AI tools can analyze data from past projects to predict how long specific development tasks will take, what resources will be required and where potential risks will occur. AI software developers can use this data to make better decisions, optimize workflows and keep development on track.

Here are the most impactful use cases for predictive analytics in software development:

• Risk management: By analyzing historical data, teams can prioritize testing efforts based on the likelihood of defects, ultimately improving software reliability. Project managers can also anticipate bottlenecks in future sprints, allowing them to adjust team capacity and workload.
• User behavior prediction: Predictive analytics can analyze user interaction data to forecast future behaviors and preferences, allowing developers to tailor features that enhance user experience.
• Performance optimization: Predictive analytics allows analyzing usage patterns and server load. Using these insights, developers and DevOps engineers can adjust resource allocation to ensure optimal performance during peak hours and maximize resource efficiency during low-traffic periods.

Current Limitations of AI in Software Development

Generative AI is powerful, but its effectiveness depends on the skills of the engineers using it. By researching feedback from on Reddit and other forums we see three key areas where human developers remain essential.

Code Review

Multiple developers report that GenAI tools sometimes provide incorrect code suggestions or introduce errors. It's not uncommon to tweak the same prompt multiple times to get a correct output from AI, or "spoon-feed" AI tools to properly debug the code.

Aligning Software Development with Business Needs

While Gen AI understands a lot about code structure and how to write it, it doesn’t have the specific knowledge of an organization's needs. Developers need to provide context, like how the code will be used, the types of interfaces it must connect with, and the end-user requirements. This human input ensures that the final product integrates well, meets performance standards, and solves real business problems.

Combining multiple frameworks or code bases

For now, AI in software development is better suited for one-off tasks that can be resolved with a single prompt. If your task is more complex, AI models often fail to see the bigger picture and can't perform the task. Developers mentioned that, to meet a complex requirement, they often need to manually combine different components or break the code into smaller, more manageable pieces.

One of the biggest benefits of AI-powered software development is in security and quality assurance. Data breaches, data destruction and ransomware attacks are a daily occurrence. They are a sad reality that requires an immediate response which AI can provide.

What AI Tools Do Software Developers Use?

Introducing AI in software development has been possible only through creation of a brand new toolset. Developers could only use AI in software development through these new tools. As we will see all these AI tools have different purposes, configurations and interfaces. But regardless of the tool, here are the categories where AI in software development has seen the most progress and adoption.

Code Assistants

In AI software development code assistants like GitHub Copilot and TabNine are changing the way developers write code. These AI powered tools suggest code snippets, complete functions and even generate entire blocks of code based on natural language input or incomplete code. GitHub Copilot for instance is trained on massive open-source repositories so it can predict and offer real-time code completions that follows best practices. So developers can speed up their workflow, reduce the time spent on mundane tasks and focus on complex problems. By using AI in software development companies can produce high quality code faster and with less errors making it a must have tool for faster development cycles.

Testing Tools

In AI software development services automated testing tools like Testim.io and Applitools are a must have to ensure software product stability and reliability. These AI powered platforms generate test cases, execute them and analyze results without human intervention. For example Applitools uses AI to detect visual bugs by comparing UI elements across different devices and environments, issues that might go unnoticed during manual testing. AI software developers rely on these tools to speed up the testing phase so products are thoroughly tested for performance and security. Integrating AI in software development testing reduces costs and minimizes the risk of deploying bad code.

Bug Detection

Bug detection has seen a huge leap forward with AI in software development. Tools like DeepCode and Snyk are used to analyze codebases in real-time and detect vulnerabilities and suggest fixes. For example Snyk scans open-source dependencies for known security flaws and helps developers protect their projects from potential breaches. A 2024 Verizon Data Breach Investigations Report  found that 68% of data breaches were due to human error so we need AI powered bug detection tools that minimizes these risks. By using AI software development services focused on bug detection companies can proactively address potential issues and reduce the chances of costly security breaches or performance issues.

Analytics and Monitoring

In AI and software development analytics and monitoring tools are crucial to keep applications healthy and performant. Platforms like Datadog and Splunk use AI to monitor system performance, track user behavior and predict potential failures. AI software developers can use these tools to detect anomalies in real-time and respond to issues before they become big. For example Datadog uses machine learning to predict system outages based on historical data so businesses can maintain uptime and avoid costly downtime. By using AI software development tools focused on analytics companies can get deeper insights into their applications and make better decisions and improve operational efficiency.

What are the challenges and risks of using AI in software development?

AI in software development is still new but it’s spreading fast. However AI in software development has risks. Despite all the hype around AI’s arrival it has risks to areas such as data privacy and security. Another obvious risk is that developers will become too dependent on AI powered software development and their skills and experience will stagnate.

Data Privacy and Security

Back to one of the main concerns in the AI software development process we need to dig deeper into AI in software development’s impact on data privacy and security. AI runs on data. It needs huge amounts of data to work and this data can include sensitive information. Companies that don’t secure the data used by AI tools are vulnerable to breaches and misuse.

Unfortunately there are many instances of these kind of breaches. IBM found out that 40% of data breaches were of data that was not protected by human programmers. Decentralization of cloud storage and computing means data lakes are scattered. Lax security means these forgotten sources are infiltrated, corrupted and even held for ransom.

AI models trained on unsecured data can be exploited in AI software development and expose confidential or personal information. These kind of data breaches are more damaging. IBM found out that attacks on unsecured data took 26% longer to detect and cost companies over $5 million excluding any additional costs that the breach might incur (legal challenges, settlements, ransom payments).

When compared to other breaches there is a huge difference in cost. A different 2024 study by IBM found that the average data breach cost was $4.88 million. According to IBM researchers this difference in cost is not because of AI but because of poor data governance protocols that programmers and developers don’t always follow. So addressing these AI and software development concerns is critical.

Integration Challenges

We’ve talked a lot about AI but also the challenges that comes with it. The AI software development process requires integrating new AI models into legacy infrastructure which can be complex and expensive. Many companies need help to align their tech stack with AI capabilities because of differences in architecture or programming languages. For example integrating AI in software development into large monolithic systems can cause compatibility issues which can lead to delays or errors. AI software developers must assess a company’s existing systems before starting the integration process or the system will become inefficient. As AI evolves companies must have the right infrastructure to adopt AI software development services seamlessly.

Over-Reliance on AI

Another risk of using AI in software development is over-reliance on AI tools which can undermine developer’s decision making skills. While AI can automate routine tasks and suggest optimizations there’s a risk that developers will become too dependent on these tools and neglect critical problem solving skills. Over reliance on these tools can lead to reduced human oversight and increase the chance of errors in the final product. According to many experts, while AI aids automation, developers must be involved in decision making to ensure AI driven outputs align with the company’s strategic goals. Balancing human expertise with AI capabilities is key in the AI software development process.

Transforming Software Development at Vodworks with AI-Driven Workflows

Vodworks is a global technology company providing nearshore development services across Europe, Asia-Pacific, and North America. Headquartered in London, Vodworks has four global R&D centres in Ukraine, Romania, Pakistan, and Cambodia, with an in-house team of over 150 engineers supported by a broad talent network across regions.

Vodworks team has 13 years of experience in delivering app development, data engineering, and AI-driven projects across industries such as telecom, healthcare, streaming, fintech, and more.

Vodworks team uses cutting-edge AI technologies to streamline software development process and deliver functional solutions, aligned with your strategic goals.

Ready to unleash the power of AI? Contact Vodworks today and start your AI journey.