5 Ways Gen AI Boosts Efficiency and Productivity in Software Programming
10 min readAcross today’s fast-paced digital landscape, efficiency in software programming is a top priority for software companies, high-tech firms, independent software vendors (ISVs), and digital native businesses (DNBs). For them to meet the growing demand for innovative software solutions, Generative AI technology has been explored by these organizations to streamline their software development processes.
Redefining the Economics of Software Development
SoftServe views Gen AI as a time-saver and a way to foster creativity and efficiency for software development. In the end, it’s not only about how much time you save with the help of Gen AI but also how much more work gets done.
SoftServe recently tested this conclusion in a study with the integration of Gen AI into the software development life cycle. For this research, more than 1,000 SoftServe associates — software professionals across seven countries, multiple disciplines, and various levels of maturity — took part. These individuals were from diverse directions and were grouped into different specializations, roles, technologies, and levels. For each group, there was a defined set of the most common and repetitive project tasks. Participants were then asked to complete these tasks with Gen AI — the test group — and without it — the control group.
Based on SoftServe’s research findings, Gen AI across the entire software development life cycle team yields value. The extent of this value varies, with some software development domains experiencing significant benefits and others modest ones.
Nevertheless, the positive impact of Gen AI tech use shows a consistent increase in software development outcomes during requirements management, design, coding, testing, deployment, quality control, documentation, and project management. Gen AI was also applicable to most software development life cycle projects.
5 Ways to Boost Efficiency and Productivity
Gen AI technology offers a wide range of vertical use cases for software companies, high-tech firms, ISVs, and DNBs to significantly enhance software development productivity. Here are some key vertical use cases:
1. Code Generation and Augmentation.
One of the most significant ways software companies harness Gen AI is through code generation and automation. Gen AI models produce code snippets, modules, or complete applications based on high-level specifications provided by developers. This approach reduces the manual labor involved in writing repetitive code, which minimizes errors and speeds up the development cycle.
ISVs and DNBs, in particular, benefit from code generation tools as they often need to produce software tailored to specific customer requirements. Gen AI allows them to customize and scale software solutions quickly, delivering them to customers faster and more efficiently.
SoftServe’s study reported a 42% increase in such software development outcomes using Gen AI.
- Code Completion: Gen AI assists developers by providing intelligent code suggestions and auto-completion, which makes coding faster and reduces errors.
- Code Refactoring: AI models automatically refactor and optimize code that improves readability and performance.
- Generating Boilerplate Code: Generative algorithms generate boilerplate code for common tasks, which saves time and effort for developers.
2. Bug Detection and Resolution.
Efficient software development isn't about writing code quickly; it's also about ensuring the quality of the code. Gen AI plays 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.
High-tech companies and software development teams use Gen AI-powered tools to improve code quality, which results in more reliable and robust software products.
SoftServe’s study reported a 62% increase in such software development outcomes using Gen AI.
- Automated Bug Detection: Gen AI analyzes codebases to find potential bugs, vulnerabilities, and performance bottlenecks, thereby enabling developers to proactively address issues.
- Automated Bug Fixes: Some AI tools suggest or automatically apply fixes for identified bugs and accelerate the debugging process.
3. Natural Language Processing (NLP) for Documentation.
Documentation is often an overlooked but essential aspect of software development. High-quality documentation helps developers understand the codebase. It makes it easier to maintain and extend software applications. Gen AI, particularly NLP models, help create comprehensive and user-friendly documentation.
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% increase in such software development outcomes using Gen AI.
- Documentation Generation: NLP models generate documentation from code comments, which makes it easier to support and understand software.
- Natural Language Interfaces: The development of chatbots or conversational agents helps developers access documentation and get answers to programming-related questions.
4. Predictive Analytics for Resource Allocation.
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.
High-tech companies and DNBs with large-scale software development operations benefit from these insights and optimize their development processes and resource management.
SoftServe’s study reported a 44% increase in such software development outcomes using Gen AI.
- 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 to plan better and meet deadlines.
5. Automated Testing.
Testing is a time-consuming but indispensable part of software development. Gen AI automates various testing processes, including unit testing, integration testing, and user interface testing. AI-driven test automation executes test cases faster and more accurately than manual testing, thus reducing the testing cycle and increasing software quality.
SoftServe’s study reported a 42% increase in such software development outcomes using 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.
As a result of SoftServe’s study, overall, it was found that using Gen AI in the software development life cycle increased the number and quality of software development outputs by 45%. Additionally, there’s a potential for enhanced results by advancing expertise and knowledge in prompt engineering.
The complete research and results are available in the white paper “Redefining the Economics of Software Development.”
Other Gen AI Use Cases for Software Development
The field of Gen AI is in a state of evolution. In addition to the above use cases, there’s an expectation that more innovative applications will further streamline software development processes. Consider:
Natural Language Interfaces for Development
- 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.
Code Reviews and Compliance
- 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.
Automated Code Deployment
- Continuous Integration and Continuous Deployment (CI/CD): The implementation of AI-powered CI/CD pipelines drives automated code deployment, testing, and monitoring.
Knowledge Base and Training
- 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.
Customized Software Solutions
- AI-Powered Personalization: With recommendation systems and Gen AI, tailor software solutions to individual user preferences and needs.
Conclusion
Gen AI is a game changer for software companies, high-tech firms, ISVs, DNBs, and any organization where software is an advantage as they look to enhance the efficiency of their software programming endeavors. From code generation and automation to bug detection, documentation, resource allocation, and automated testing, AI-powered solutions transform the way software is developed.
By embracing Gen AI technology, software-centric organizations stay competitive by bringing new software products and feature enhancements to market, while delivering high-quality solutions faster and more efficiently.
Learn more about the state of Gen AI (and large language models), with an emphasis on market opportunities and use cases across industries. Join Iurii Milovanov, AVP, AI and Data Science at SoftServe, during his session — Disrupt or Be Disrupted: Generative AI’s Unexpected Reality Check — on October 18, 2023, at Big Data & AI in Toronto. There, Iurii will share:
- A strategic framework to show and prioritize your high-impact use cases, and turn ideas into tangible products, while you maximize value, optimize cost-efficiency, and accelerate time to market.
- Best practices for successful implementations that cover your solution ideation and key design considerations, data prerequisites, model development, adaptation, and production deployment.
- How to navigate the complexities of the rapidly advancing Gen AI landscape, drive value across all aspects of your business, and secure a competitive advantage in the digital age.