Software development plays a crucial role in the modern business ecosystem. Integrating software applications into the business infrastructure enables workers to streamline workflows starting from bookkeeping and scheduling to marketing and payroll management. However, IT leaders and software teams are under pressure to deliver solutions faster without compromising quality. Meaning that, the pace of technological evolution in software development is only increasing. They continue to struggle with code generation, test coverage, cybersecurity vulnerabilities, and technical debt.
Fortunately, evolving technologies like Generative AI promise to enhance the way leaders organize software engineering and development. This ground-breaking technology can modernize the entire Software Development Life Cycle (SDLC), from ideation to software design, packaging, testing, and monitoring. Thereby, expediting various development processes and making work easier for developers.
Page Contents
Generative AI in Software Development – A Quick Glance at the Potential
Generative AI is revolutionizing software development. Large Language Models (LLMs) have proven to deliver impressive outcomes when inputting vast quantities of coding data. Generative AI solutions make IT leader’s jobs even easier since it relies on standardized inputs, such as code, data models, user tickets, test packages, and system logs.
A group of specialists from IBM presented a study that Generative AI can bring about a 20-25% rise in the number of new features developed within a software product. Similarly, they recommended that using Gen AI models enables software developers to reduce the number of bugs found in production and market new products or features 72% faster than conventional approaches. These productivity gains can be experienced by tech leaders based on three factors:
- Developer Expertise – Leaders need to know how what to request, and have the capability to review GenAI output without compromising quality.
- Familiarity with GenAI – GenAI tools require prompt inputs, which means experts need knowledge and skills to not just inscribe human inputs, but also the tools, study how to use them, and leave when it is just a diversion.
- Nature of the Complexity – GenAI for software development is profitable when IT leaders categorize and document development complexities. For software projects with greater complexity, GenAI solutions will be much less likely to offer productive outcomes.
4 Ways Generative AI Solutions Adds Value to Software Development
1. Minimizing Repetitive Work
GenAI excels at pattern correlation and pattern synthesis, like converting one language into another. The most obvious use of that feature for software delivery is for an innovative kind of code generation, where the AI deciphers formal command into code, or one form of code into another. But this can also be beneficial in other functions, such as rendering change records into a release description, turning team discussions into documentation, or mapping unstructured data into organized templates. It could even help development teams produce sample and test data.
In other terms, Generative AI can remove the time-intensive tasks so programmers can focus more on multifaceted, value-adding activities. Gen AI solutions can perform the pattern matching and developers simply need to augment the results and complete the coding process.
2. Comprehensive Ideation
Large language models (LLMs) might generate an output that is deceptive or false, apparently in conflict with the information on which it has been inputted. While this can be problematic, they are effective in other aspects. Gen AI algorithms are optimal ideation and brainstorming partners. They can highlight gaps in the developer’s planning and thinking.
Many tech leaders have witnessed great results for strategy and product ideation, such as prompting an LLM to produce situations that can activate divergent thinking. It is also advisable to use LLMs as sparring partners to improve testing circumstances and user stories. For example, if developers are trying to conceptualize various ways a given product may be utilized, LLMs can help them expand their discerning, bridging gaps with scenarios that they hadn’t assumed about. The advantage of this is that, by analyzing requirements more meticulously, developers can reduce the need for rework in the future.
3. Finding and Processing Information
One of the major sources of inadequacy for software programmers is identifying the right data. From online research to in-house documentation, knowing where to find the right information can be a challenging task.
GenAI provides the chance to implement new forms of search functionalities on top of loads of unstructured data sources. This is already available in GitHub’s Copilot, offering coding assistance functionality for developers to obtain context-specific support. Likewise, Atlassian provides developers with a way to search unstructured information effortlessly. Properly integrating and training GenAI systems on the available data is important. When software delivery teams use them effectively, these systems can provide an easy entry to data in the context of their development task. Additionally, this unlocks new ways for leaders to display particularly critical information, reminding developers of security or compliance issues that they need to consider.
Gen AI Chatbots shouldn’t be considered a complete replacement for detailed and sourced data research. Most importantly, tech enablers should always monitor Gen AI models for accuracy and implement ways to minimize friction and drive productivity.
4. Bridging IT and Business
With Generative AI for software development, tech leaders may form the basis of a new association between business and IT teams. Designing applications in a few hours can lead to robust specifications, rapid iterations, and better business adoption. Gen AI will speed up the “no code” adoption that helps in digitization. Both business stakeholders and IT experts can build dashboards, produce insights from data sets, and create personalized workflows simply by entering what is required into a Chatbot powered by the LLM model.
Realize the Opportunities of Gen AI in Software Development With a Strategic Plan
Framing Gen AI Vision
The first stage is to determine where Generative AI solutions can have early influence and how the influence can scale and expand. During this stage, leaders should consider technology’s potential and the enterprise’s objectives and starting point. This may necessitate experimentation and learning. However, it is crucial to think based on tools and processes (such as reforming software development and maintenance activities) rather than augmenting steps distinctly.
Leaders should prepare to address concerns and limitations (legacy technology, share provided by off-the-shelf software packages) and explain how they will restore productivity gains. As the IT department gains experience, it can transform the vision into a strategic roadmap based on practicality and value.
Establishing the Funding and Prerequisites
Gen AI adoption signifies an overall IT transformation, which needs investment for the essential modifications in talent, tooling, and processes. To generate better annual savings, leaders should collaborate with stakeholders and make tactical decisions. Adequate enterprise-wide technical maturity also must be attained in various areas before Gen AI deployment. This includes a consolidated code base, configuration management database, knowledge repository, and an active DevOps toolchain.
Since the Gen AI tools used will be off-the-shelf products, most of the investment will go toward talent retention, and operating model expenses will be greater. Upskilling and change management should obtain adequate budget provisions.
Setting up Gen AI Metrics
Gen AI adoption will gain traction by testing on the most viable and high-value software development projects. It is imperative for leaders to set up development KPIs, such as deployment regularity, lead time, software quality, and developer fulfillment. By tracking these KPIs throughout the development cycle, leaders can save up to 95% of resources. Monitor and share the key performance outcomes from each stage to enhance future processes.
Redefining Sourcing Strategies
Offshore Generative AI consulting service providers will be designing and rolling out exclusive solutions to enhance their cost structures. Try to share in the savings by collaborating with them. By using the custom Generative AI solutions developed by offshore agents, leaders can reduce unwanted licensing fees. Over time, Gen AI consultants may provide an opportunity for leaders to minimize their need for in-house resources. This is because the design and architecture phases of the software lifecycle will be managed by external resources. Tech enablers that seek offshore support stand to gain more from Generative AI in the long term.
Leveraging Responsible AI for Mitigating Legal and Ethical Risks
Generative AI risks are easier to identify and control in software development than in other functions. For code creation, most Gen AI tools provide the option to utilize models trained on code that involve explicit programs for training purposes. Chatbots may be vulnerable to falsehoods, deceptions, and bias from their inputted data sets.
Most commercial Gen AI tools are powered by Cloud and need integrating assets such as tests, code, and tickets. Even enterprise-grade solutions with greater data protection can experience data privacy and regulatory restrictions. To mitigate these risks, leaders need to implement independent tools to evaluate quality, and service level agreements and evade security risks.
Closing Thoughts
Generative AI is transforming software development in a way that no other technology or process enhancement has done. Using the latest version of Gen AI tools, developers can accomplish tasks up to five times quicker—and this is just the start. When the technology evolves and is impeccably united within tools across the development life cycle, it is anticipated to further enhance the speed and even quality of the creation process.
