How Artificial Intelligence Can Propel Software Projects Through the Development Lifecycle Process
Wanda Matuszewska
Customer Success Manager | Ensuring Client Success & Growth at Andersen
The Software Development Process
Software development projects are developed to align their outcomes with the business’s overall strategy and to deliver high-value goals.
Regardless of whether a project uses a Waterfall SDLC model or an Agile process, they stress baselining the requirements (and, in the case of Agile, for a few weeks anyway) before the design and development start.
A typical SDLC model will comprise the following phases:
How AI Can ‘Influence’ the Software Development Life Cycle
AI software development has successfully increased the development process’s productivity, speed, and quality. AI encourages computer systems to learn and ‘think’ for themselves. For example, computer vision, a field of AI, entitles computer systems to analyze various digital images, videos, and other graphical media to derive meaningful metadata concerning those image inputs. Once a determination has been made, recommendations are then based on the initial analysis.
How AI can ‘Quicken’ the Software Development Process
1. Code Automation and Generation Saves Valuable Time
Writing quality code for any project will take time. Regardless of the amount of code required or how competent your developers are, the technique of writing code that satisfies project goals is a time-consuming process. Automated code generators cannot replace a skilled systems engineer, but they can help reduce the development process by automating those software requirements that are standard practice for each project.
Basic auditing functions, data backup functionality, Add, Update, Read, and Delete data record processing, and standard menu display and user options are common functionality segments that code generators can quickly deliver. In addition, AI coding tools can deliver working code and suggest reusable snippets so that developers can focus on delivering those substantial portions of the software — analyzing and processing business requirements.
2. Fast and Efficient Testing
Effective software testing is crucial to ensure business requirements can be met, and a quality product is delivered. AI-powered quality assurance testing tools will find errors, fix bugs in the code, and confirm smooth performance in all environments it has been configured for. At Meta, their bug prediction tool has an 80% success rate of uncovering software errors and suggesting appropriate bug fixes.
AI can also assist with the testing process before test execution commences. For example, new generation AI testing tools can now formulate use test cases and compare these to what testing engineers have devised manually. This process assists with identifying and testing coverage gaps, and this use case formulation can be achieved by collecting relevant information from the system logs post-automated testing exercises.
3. Generating Software Design
Customers want unique system designs to satisfy their business requirements, and they want them to be out-of-the-box as well. Therefore, simple but effective design solutions that can be quickly delivered are their common mantra. AI design tools such as digital assistants can analyze requirements, discover inconsistencies, and make recommendations for design improvements.
Importantly, these AI design tools function using natural language processing and utilize popularly referenced guidelines for self-training purposes. Additionally, AI tools learn from previous software application projects and business requirements to analyze the existing projects’ performance. As a result, they can offer meaningful modifications and improvements.
This AI automation does help both customers and software providers design acceptable software solutions.
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4. Simplifies Rapid Prototyping
This process is more than merely making wireframes interact with each other. A prototype represents a simplified development process outcome, and is delivered in a timely fashion for the customer to review the (proposed) software solution.
Using machine learning, AI prototyping tools can analyze and chart the business functionality into a viable temporary solution that can be reviewed together. Rapid AI prototyping provides developers with valuable outcome prediction information, and subsequent design modifications can be fast-tracked without any quality compromise.
5. Project Budget Automation
Software solution providers that deliver custom software development services have the option to use AI planning tools to forecast project timelines and provide a level of assurance and project accountability.
These AI tools factor in the business requirements, and the overall project scope. Previous project lessons learned, and other holistic factors that affected past projects, are compiled and generated to deliver an automated project budget and timeline.
What Are These AI Tools That Assist Software Development?
Numerous software solution providers use various AI tools to develop software applications. Some of the most common AI tools on the market are as follows:
AI Tools and SDLC Into the Future
AI is invaluable to all business applications derived from the software development life cycle. In addition, you can enhance the software development process by incorporating it within as many development phases as practically possible.
Eventually, AI will evolve into a necessary addition to a software developer’s skillset. This new process has already taken a prominent position within large MNCs, and the spotlight will not fade soon.
The software development landscape is constantly changing its processes, but its competitive nature amongst solution providers will not. Therefore, to stay ahead of the competition or to have that distinct competitive advantage, senior business decision-makers must be aware of new technology tools that will enable their development teams to deliver quality software solutions much more quickly.
Therefore, they will need to adopt such tools as soon as practically possible.
The most beneficial aspect of AI is reducing time within any given development process, which is a crucial factor for all DevOps institutions. AI tools exist to help human endeavors and reduce intensive human labor activity. As architects, developers, testers, and project managers become more productive using AI toolsets, software solution providers are more likely to produce higher quality software solutions at more competitive prices.
Conclusion
The market is witnessing enormous growth in AI software development. AI software engineering has been growing exponentially and will continue to do so well into 2023. With AI-based tools, it is now attainable to automate realistic ML software design modeling, thus improving ML consulting practices.
Undoubtedly, software development providers must adopt the potential benefits of AI development tools, as it is a significant game-changer for the software development future.