5 Ways Gen AI Boosts Efficiency and Productivity in Software Programming Development

Gen AI can impact nearly every stage of the software development process, from writing and testing code to managing resources and documentation. SoftServe conducted research to validate this in real-world settings.

We involved over 1,000 SoftServe associates, including software professionals across seven countries, multiple disciplines, and different levels of experience. Participants were grouped by specialization, role, technology, and seniority. Each group completed a set of common, repetitive project tasks (1500 experiments in total), once using Gen AI (test group) and once without it (control group).

Our findings show that integrating Gen AI across SDLC teams delivers clear improvements. Some domains saw significant gains, while others were more modest, but overall, productivity increased consistently. These results suggest that Gen AI can speed up software development and bring measurable benefits to the projects.

One of the most impactful ways software companies use Gen AI is for code generation and automation. AI models can produce code snippets, modules, or complete applications based on high-level specifications, reducing repetitive coding, minimizing errors, and speeding up the development cycle.

ISVs and DNBs, in particular, boost productivity with AI coding, as they often need to produce customized software for specific client needs. Gen AI allows them to scale and deliver solutions faster.

SoftServe’s study reported a 42% increase in such outcomes:

• Code completion: Gen AI provides intelligent suggestions and auto-completion, helping developers work faster and with fewer errors. This is a practical way to improve coding productivity with AI.
• Code refactoring: AI models refactor and optimize code, improving readability and performance.
• Generating boilerplate code: AI generates boilerplate for common tasks, saving time and effort.

Efficient software development isn't about writing code quickly; it's also about ensuring the quality of the code. Gen AI tools play a crucial role in bug detection and resolution. Advanced AI models scan and analyze code to find potential bugs, vulnerabilities, or performance issues. This proactive approach to debugging saves time otherwise spent searching for and fixing issues.

SoftServe’s study reported a 62% increase in outcomes with Gen AI bug detection tools:

• Automated bug detection: Gen AI scans codebases for potential issues and helps developers proactively address them.
• Automated bug fixes: Some tools suggest or automatically implement fixes, accelerating debugging.

Documentation is often overlooked but essential. Clear documentation helps developers understand the codebase, maintain software, and collaborate effectively. Gen AI, especially NLP models, can generate comprehensive and up-to-date documentation automatically.

Software companies and ISVs use NLP models to automatically generate documentation from code comments and annotations. This saves time and ensures that documentation is always up to date. This also helps in knowledge transfer within teams and makes it easier for developers to collaborate efficiently.

SoftServe’s study reported a 28% improvement in outcomes when using Gen AI for documentation.

• Documentation generation: NLP models create documentation from code comments, simplifying maintenance and knowledge transfer.
• Natural language interfaces: Chatbots or conversational agents allow developers to access documentation and get quick answers.

Efficient resource allocation is critical for software development projects. Gen AI supplies valuable insights through predictive analytics. By analyzing historical project data, AI models predict resource requirements, project timelines, and potential roadblocks. This data-driven approach allows organizations to allocate resources more effectively and ensures that projects stay on schedule and within budget.

SoftServe’s study reported a 44% increase in productivity in this area:

• Resource allocation: The use of AI-driven predictive analytics optimizes project timelines and budgets by allocating resources more efficiently, including personnel, time, and infrastructure.
• Project management: Predictive models forecast project completion times, which aids organizations in planning better and meeting deadlines.

SoftServe’s study reported a 42% improvement in testing outcomes with Gen AI:

• Test case generation: AI generates test cases automatically to ensure comprehensive test coverage and reduce the effort needed for manual test case creation.
• Regression testing: With the implementation of automated regression testing using AI, issues are quickly found when new code changes are introduced.

• Conversational coding assistants: The creation of chatbots or voice assistants helps developers write and debug code by providing real-time guidance and suggestions.
• Code translation: With AI, the translation of code between programming languages eases cross-platform development.

• Automated code reviews: Gen AI assesses code quality and streamlines the code review process, ensuring that it adheres to coding standards and follows security guidelines.
• License and dependency analysis: AI tools find third-party dependencies and licenses, and help organizations manage open-source components more effectively.

• Continuous integration and continuous deployment (CI/CD): The implementation of AI-powered CI/CD pipelines drives automated code deployment, testing, and monitoring.

• AI-powered training materials: The creation of interactive training materials and tutorials using Gen AI makes it easier to onboard new developers and upskill existing teams.
• Auto-generate FAQs: Use AI to automatically generate frequently asked questions (FAQs) and knowledge base articles for user queries and issues.

At SoftServe, we take a structured approach to integrating Gen AI, making sure adoption is safe, measurable, and effective across projects. Our goal is to fit AI naturally into existing workflows and help teams get the most out of its capabilities.

We begin with assessment and design, reviewing the SDLC, team structure, infrastructure, and security protocols. This helps us pinpoint where AI can add value and how it should be introduced.

Next, we focus on productivity monitoring. We define baseline metrics and track improvements once AI is in use, so teams can see tangible gains in efficiency and output.

Then comes infrastructure setup. We configure environments for AI-assisted coding and content creation, ensuring security and smooth integration with the tools teams already use.

Finally, education and support help teams adopt AI confidently. Developers learn prompt engineering, follow best practices, and expand AI usage across projects with ongoing guidance.

This approach helps organizations get the most out of Gen AI across the SDLC. Learn more by contacting us.