The rapid evolution of the technology landscape emphasizes the need for agility, flexibility, and efficiency in software development across businesses. To enable this transformation, incorporating operational efficiency, and accuracy, and to accelerate innovative software deployments, the integration of Artificial Intelligence (AI) with software development is envisioned as a pioneer solution. The AI domain includes a subset known as Generative AI (Gen AI), which fuels the Software Development Lifecycle (SDLC) transformation.
Generative AI is getting a lot of recognition because of its key influence on SDLC. Generative AI Gen AI indicates a set of AI models leveraging deep learning algorithms to identify patterns and create code to realize real-time decision-making and task automation. However, the amalgamation of Gen AI with SDLC presents advantages as well as challenges. Read the blog to explore the key benefits and challenges of Gen AI in SDLC.
Before looking into the benefits Gen AI can bring to software development, let’s understand the conventional SDLC.
The typical SDLC consists of the following stages:
Planning: This primary stage gathers, evaluates, and records the project requirements. A project plan is made by the team specifying the project scope, cost, and resources needed.
Design: The comprehensive software design is developed during this stage. The design indicates the software design framework, modules, and interfaces. The team also designs a trial product or a replica of the user interface.
Development: This stage develops the software system; actual coding happens at this stage. With the help of programming languages, tools, or frameworks, software modules are generated and combined into a working technique. Tools such as GitHub Copilot help developers by indicating code extracts and finishing lines of code based on contextual interpretation. This saves time and enhances the quality.
Testing: The testing stage checks for faults, errors, or bugs in the software system. The testing team sets up test cases and executes those to identify any faults and communicate them to the development team. The testing can be automated or performed manually.
Maintenance: In this stage, the software system deployment is complete in the production environment and is available for the end-users. The development team is responsible for maintaining, supporting, observing, and fixing bugs, and performing regular updates to the system.
However, these phases in SDLC often encounter certain challenges. The key challenges need to be understood to appropriately leverage the capabilities of GenAI to enhance the conventional approach to software development. One of the significant challenges is the time and work involved in developing and testing software. The completion of software development projects can extend for months or years, and the testing part single-handedly exhausts a substantial volume of time and resources. As the software development project includes various participants such as developers, testers, managers, and more, a challenge exists in managing and collaborating with these teams. The success of the project lies in the successful communication and teamwork among these participants. The project progress, timelines, and cost should be continuously monitored to ensure good quality product release. As software systems are complex, there can be scenarios where some faults go unnoticed till it is deployed in the production environment. Project managers are responsible for persistently observing and supervising the project’s development and regulating the plan to finish the project on time and within the estimated budget.
Now, let’s delve into how integrating GenAI brings benefits in SDLC.
The incorporation of Generative AI in the SDLC transforms how software is planned, developed, and delivered, reinventing the tasks developers, testers, and machines.
Innovation and Creativity: Automating Code Creation
Gen AI can automate repetitive tasks or code generation processes. This is achieved using machine learning or deep learning algorithms which help developers build and execute functional codes. This saves time in the development process and enables developers to focus on complicated tasks. In addition, Gen AI analyses massive datasets to detect and fix faults in code. This analysis phase automatically analyses huge datasets, utilizing generative models, to recognize patterns and probable threats or issues. This in turn improves the accuracy of project specification and requirement details.
Enhanced Software Testing and Quality Assurance
Gen AI automates the testing phase and enhances the quality of the software product. Generative models can easily identify any anomalies or threats, enhancing the quality of product and reducing the effort and time in the testing phase. The automated test cases and scripts speed up the process significantly. This ensures comprehensive testing coverage and enables faster identification of defects. With generative AI, developers can expect more efficient and effective software testing, leading to higher-quality products in less time.
Improved Customer Experience
As Gen AI enhances the software quality, in turn this leads to a better user experience and reduced cost of software development. Gen AI generates UI prototypes, proposes right design elements, and even provides blueprint suggestions supporting users’ demands.
Faster Product Development: Modernizing Project Management and Teamwork
AI models help automate operations or deployments and the seamless shifts from development to production environment. For instance, Gen AI can automatically attribute work to team members depending on their skill sets, saving time and work in the project coordination. Besides, Gen AI can accelerate the SDLC by generating models of novel software, to support developers in the development process. This leads to faster Time-To-Market (TTM), and a better insight into user requirements.
The following table shows the tools that can be utilized to achieve the above benefits.
| Gen AI Benefits in SDLC | Key Tools |
| Automated Code Generation | Kite and tabnine |
| Quality Assurance and Software Testing | DeepCode |
| Project Management and Collaboration | Notion AI, Jira, and Trello |
| Natural Language Processing (NLP) and Code Generation | TensorFlow and PyTorch, GitHub Copilot |
Source: GoRetro
Here are some specific examples of how generative AI is being used in the SDLC by top companies today.
Top Trending Applications of Generative AI for SDLC
In Summary
GenAI brings unparalleled enhancements in various phases of SDLC. Enterprises should embrace GenAI in their software development process to stay competitive in the software market. Gen AI integration realizes quicker development cycles, superior code quality, and increased innovation.
Calsoft being a Technology-First company with its comprehensive software product engineering experience of 25 years, always stays at the forefront of modern innovations. Our unique combination of end-to-end software product engineering and digital transformation services helps product and platform companies, ISVs, and digital enterprises innovate seamlessly to gain competitive advantage, generate new revenue opportunities, enhance business agility, lower operational costs, gain operational efficiency, and accelerate time to market.
With Generative AI on charts, Calsoft is all set to serve customers with the following Gen AI services
- Generative AI Product Development & User Experience Design
- Generative AI Driven Testing & Quality Engineering
- Integrations and Plugin Development to Integrate Products and Platforms with Generative AI Tools
To know more about Generative AI, explore Calsoft services or contact our AI experts.
