Four ways AI can augment software development
Learn how to enhance the software development life cycle with AI
It's brutal out there.
In the pressure cooker environment of software production, developers are burning out. The software development life cycle (SDLC) might be to blame. Today's SDLC runs through various stages – requirements gathering, design, development, testing, deployment, and maintenance – in just a few months. Inefficiencies, ranging from inconsistencies in programming requirements to a lack of knowledge in writing test cases, plague each of these steps.
The good news is that generative AI (gen AI) can relieve some of the stress developers are under and make the process more efficient. Developers can use gen AI tools to pinpoint and understand errors in code, find security loopholes, and receive suggestions for bug fixes. Despite these benefits, several factors may hinder the adoption of generative AI. What are these challenges, and how can enterprise leaders effectively address them? We explore.
The role of gen AI in software development
It used to be that software development followed a waterfall process, cascading through each of the phases systematically and linearly. The final product took months upon months, if not years, to develop.
Today's agile and DevOps landscape, in which DevOps practices fuse development and operations teams, is a completely different beast in a few important ways.
The challenge: Navigating rapid software development
To meet the demand for faster software development, organizations have adopted agile and DevOps methodologies. This focus on speed aims to enhance market positioning, but the resulting compressed production cycles can place significant pressure on developers.
The gen AI solution
By using gen AI, developers can automate mundane tasks, allowing them to concentrate on work only they can do. A slate of to-dos in software development relates to necessary documentation and endlessly repetitive processes that soak up expensive developers' time. Activities such as generating configuration files, code reviews, and setting up infrastructure environments benefit from automation and generative AI. Layering AI in the SDLC has another key potential benefit: even more speed.
The challenge: Balancing skills beyond coding
The term full-stack developer best captures the many aspects of software development programmers must know in addition to coding. They must understand the mechanics of the entire process, how to set up infrastructure to develop and test code, how to write test cases, and how to deploy to the cloud. Such high expectations add to the pressure and cognitive load on developers.
The gen AI solution
Generative AI helps developers code faster and enhances their understanding of existing code, particularly when making modifications. It can also summarize lengthy documentation to make information more accessible. During the testing phase, the technology can create test cases from user stories and requirements as well as generate synthetic data and scripts for evaluating applications.
The challenge: Inconsistent frameworks for development
With distributed architecture on the rise, developers from different teams, including external outsourced ones, contribute to the project, cobbling it together to form a whole. Even if standardization guidelines are in place, there's a high chance that the code or application architecture one developer delivers might be different from that generated by another team member. Such inconsistencies make it more difficult for subsequent developers to understand the code thoroughly.
The gen AI solution
Implement consistent standards to improve code quality. Whether it's through templates for writing code, documentation, user stories, or test cases, gen AI in software development can support necessary processes and meet consistency guidelines.
The challenge: A skills gap increases technical debt
Companies with bloated code in outdated programming languages like COBOL carry large amounts of technical debt – the long-term costs and complications arising from quick fixes and outdated code practices. Today's developers might not have the technical skills to wrestle with these legacy systems and put them to work.
The gen AI solution
Generative AI can parse lines of code and explain its functionality in plain English. A developer who is unfamiliar with COBOL, for example, can simply click the corresponding block of code and activate the coding assistant to generate an explanation. Accessing such information means developers do not have to spend hours trying to understand code functionality or learn an obsolete language. They can quickly catch up with projects for improved productivity. AI-augmented software development can create documentation on process flow and even help modernize technology stacks by converting legacy code to newer in-use languages.
Case study
Using generative AI in the testing phase for more efficient software development
Behavior-driven development (BDD) tests are integral to software development because they help tailor code to user behavior. While necessary, BDD tests were a challenge for developers at an investment management firm. The team manually created test scenarios, delivering inconsistent and often inaccurate scenarios that did not mirror actual user experiences.
In addition, the test scenarios were untraceable, which meant team members could not confirm which test scenarios mapped to which real-world requests. Because the manual process was time-consuming, developers could not respond to more pressing business requirements.
We rebooted the BDD testing process in stages, updating the company's tech stack and introducing gen AI. Our Genpact Cora solution analyzes user requirements and specifications to create realistic test scenarios. Using gen AI helped free one developer a day, and the team at the investment management firm now cycles through the software development process in half the time.
Obstacles to AI augmentation in software development
Generative AI offers promising possibilities for improved developer environments and efficiencies, but obstacles to adoption persist.
- Generative AI assistance is often not an integral part of the developer ecosystem
How often would you exit Outlook to ensure your message is grammatically correct? The chances are likely slim to none. Similarly, developers might not want to exit their development ecosystem to access gen AI coding assistants. These tools need to be a part of the developer ecosystem, so using them becomes seamless and a habit.
- Concerns about security and the sharing of proprietary information
Enterprises are understandably concerned about security compliance when adopting any new technology. Since gen AI tools feed on data, security teams worry that in-house proprietary information might leak into the larger AI ecosystem. Before they can be adopted widely, gen AI tools will need guardrails to ensure security protocols, like conducting security audits and granting access to authorized team members.
- The accuracy of results and potential for bias
Organizations worry that gen AI coding assistants might spit out inaccurate code that creates problems instead of solving them. Generative AI is meant to augment developer intelligence, not replace it. Humans will still need to check code validity and attend to higher-order tasks. Bias in software is another concern, and enterprise leaders can address it by developing a set of ethical development guidelines for all developers to follow.
How to get started with AI-augmented software development
Given gen AI's many advantages in software development, now's a good time to get started by familiarizing yourself with the tools.
Understand which problems AI can tackle and, equally important, which ones it cannot. Remember, AI serves as an augmentation tool, not as a replacement for software developers, who will remain the critical anchor for production. The world of gen AI can sometimes feel overwhelming, so choose the right ecosystem partner to help you navigate the bumpy landscape.
Rajesh Padmakumaran, Application Modernization and Gen AI Architect at Genpact, authored this point of view.
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