Traditional vs AI-assisted Software Development: A Head-to-Head Juxtaposition
Artificial intelligence is a disruptive technology powering multiple innovations across numerous digitally driven industry verticals. The IT realm is one of such fields where it is revolutionizing the very approach to creating software and paving the way to minimizing the role of human personnel in it. However, for many entrepreneurs and high-tech professionals, AI-assisted software development is still an uncharted territory. For them, entering it is fraught with unknown risks and challenges, so they prefer to stick to the good old human programming routine.
This article will explore the nitty-gritty of both software-building techniques, compare them across the key parameters, and give advice concerning the choice of approach for your project. We at Metatable.ai provide an AI coding tool helping developers stay more effient. However, today we’ll try to be as impartial as we can be, and give our take on traditional vs AI-assisted software development comparison.
Traditional Software Development as Large as Life
This is a software development classic. It relies on go-to instructions devised by programmers in their explicit scenario-specific development logic. As a result, the SDLC is predictable and stable. Let's see how a typical software project is handled by the team adhering to the traditional development scheme.
Stage 1. Discovery phase
To collect and analyze requirements, the project team holds a series of meetings with customer representatives and other stakeholders. During these meetings, developers meticulously record everything they learn about the client's enterprise, its business goals, pipeline, target audience, the industry niche it operates in (with its dominant trends), the problem they want to address with the future solution, etc. The major deliverable of such meetings is a detailed document containing all project information that is subjected to a thorough analysis.
Pros:
A comprehensive dossier where all relevant data is collected
Cons:
The creation of such a document is extremely time-consuming
Because of possible miscommunication, it may contain inaccurate information
Evolving requirements and market fluctuations often result in scope creep, which necessitates introducing significant changes to the initial document
Stage 2. Design
Software engineers outline the system architecture of the future solution. As a rule, they leverage Unified Modeling Language (UML) diagrams and other modeling tools to represent code architecture, basic design elements, and proposed implementation of the solution they build.
Pros:
The creation of the product's blueprint serving as a link between problem space and solution space, that makes software design understandable, adjustable, and reusable.
Cons:
It should be preceded by a considerable upfront planning effort
Change resistance late into the project
Stage 3. Implementation
Using design specifications as their guidelines, software engineers write tons of code manually, which is then tested and debugged in the same way.
Pros:
The coding process is rigorous and transparent, during which developers can record their thoughts in analytic memos containing a wealth of additional project data.
Cons:
Extremely time-consuming
Requires first-rate expertise
Become substandard in case of inadequate coordination between stakeholders
Prone to error and other human-driven factors
Stage 4. Testing
QA specialists utilize various types of manual tests (user acceptance, integration, unit, compatibility, accessibility, and the like) to ensure the solution's seamless and bug-free performance.
Pros:
High accuracy
Capturing real user experience
Low technical requirements
Visual issue identification
Cons:
Time- and effort-consuming
Turns into a bottleneck if not automated
Stage 5. Deployment
The finished solution is prepared for release by performing environment configuration, packaging, and transferring ownership of the solution.
Pros:
Real-time monitoring
Addressing regulatory compliance
Tailored approach to handling complex issues
Cons:
Requires robust coordination and careful planning
High risk of errors
Stage 6. Maintenance
The support team ensures the solution's smooth operation by updating its features, checking security, fixing bugs, and addressing performance issues.
Pros:
Human personnel attend to every problem, finding individual solutions in each particular case
Cons:
Requires continuous effort
Big-ticket item
Resource-intensive endeavor
Now, let’s see how AI goes through all these stages of the SDLC.
AI-fueled Software Development Dissected
Unlike traditional programming guided by pre-defined instructions, artificial intelligence tools powered by ML models can learn in the process and make decisions without specific directions. One may say that conventional development teams program software to perform the task, whereas AI tools are programmed to learn how to create software that performs the task. As a result, using AI for software engineering streamlines and facilitates the SDLC immensely. One more thing that makes employing AI tools attractive is the ability to integrate them at any stage of software development.
Stage 1. Discovery phase
The project team relies on NLP techniques to interpret and analyze customers’ opinions and requirements, automatically generating initial specifications.
Pros:
Requires minimal time
Rules out miscommunication
Guarantees an accurate foundation for the SDLC
Cons:
Requires attention to handling unstructured natural language data with its ambiguity, vagueness, subjectivity, and incomplete logic.
Stage 2. Design
AI mechanisms generate system architecture and design documents using project requirements as input.
Pros:
Accelerates the routine drastically
Enables swift prototyping
Flexibility in adapting to changes
Cons:
Tells negatively upon the designers’ creativity
Stage 3. Implementation
Software developers utilize generative AI programming tools that rely on predictive mechanisms and machine learning algorithms to anticipate recurrent code lines and automate repetitive tasks.
Pros:
Accessibility to non-tech personnel, ushering in no-code/low-code development
High productivity of AI assisted development teams
Enhanced code quality
Scalability and continuous improvement of ML models
Boosted collaboration between team members
Cons:
Issues with code interpretability
Questionable security
Stage 4. Testing
QA experts employ AI-powered testing tools that automatically generate test cases and detect potential bugs.
Pros:
Accelerated bug detection and resolution
Augmented test coverage
Increased bug identification accuracy
Cons:
Dependency on additional wrappers
Questionable stability after UI updates
Stage 5. Deployment
Continuous integration and environment configuration are a breeze with specialized AI deployment tools, which automate the entire process.
Pros:
High-level consistency
Minimal deployment errors
Short release cycle
Cons:
Lack of manual oversight
Stage 6. Maintenance
AI algorithms not only monitor the solution’s performance in real-time but also suggest proactive maintenance measures.
Pros:
Reduced downtime
Higher software reliability
Lower maintenance budget
Cons:
Dependence on data quality
Lack of context and intuition
As you see, both approaches to software development have their own merits and demerits, with AI having an edge across most stages. What about particular aspects of the two methods?
AI vs Traditional Software Development: Key Comparisons
Let’s go through the basic parameters of SDLC performed with the help of each approach.
Development Speed and Efficiency
Due to extensive project documentation and manual processes utilized for implementation, traditionally managed software development is slow and laborious. AI-enhanced software creation proceeds much faster since it relies on machine assistance and a high degree of automation in code generation.
Accuracy and quality of the end product
Planned and carried out by a human workforce, traditional software creation is prone to inconsistencies and errors. AI-powered software development is proud of characteristics traditional techniques can’t achieve. Automated testing, machine code review and optimization, intelligent bug detection and resolution, and quality assurance performed with the help of AI tools that offer real-time suggestions for improvement allow for the creation of smoothly functioning and bug-free digital products.
Adaptability and scalability
Once the plan is approved and the design is set, traditional software development can't take alterations in stride and requires cumbersome procedures for introducing corrections, especially at later development stages. AI in software engineering stays open to modifications and fine-tuning the product during its creation, adapts to changes easily, and supports iterative development.
Innovation and creativity
The lion's share of development tasks is repetitive. When working on a project, software engineers spend most of their time replaying the same scenarios and re-running the same processes. Naturally, this leaves no room for trying something new or coming up with offbeat ideas. When redundant and mechanical jobs are delegated to AI algorithms, developers can pay more attention to the creative aspects of their work and focus on embracing innovation.
Collaboration and communication
Conventional collaboration tools focus on enabling file sharing and communication across the development team. With software projects becoming more sophisticated and teams more dispersed, such capabilities fall short of specialists’ expectations. AI-driven collaboration systems go beyond being simple communication platforms. They summarize discussions, send action items around to stakeholders via AI-fueled notification tools, provide insights, and boost decision-making, thus taking collaboration to a new level and dramatically facilitating project management.
Cost
Extensive manual labor and a longer development cycle, symptomatic of a traditional approach to software creation, spell higher project costs. Of course, harnessing AI tools is not a chump change endeavor either, but it is more cost-effective in the long run since its automation mechanisms and minimal reliance on human personnel allow customers to save a pretty penny on the implementation of digital projects.
Evidently, in comparison with traditional SDLC, the AI-powered approach gets the long end of the stick when it comes to mission-critical characteristics of software development. However, it would be a mistake to claim that AI is a master key opening any door in product building. How should we opt for the best method in every particular case?
Choosing the Right Approach: Essential Tips
The ultimate choice of the software development approach is conditioned by several pivotal factors, among which the project type and scope, the sum you can spend on it, the time you have before the solution should be marketed or put into operation, and the technical expertise of the project team.
While delivering our product to various organizations worldwide, we at Metatable.ai have understood which scenarios and use cases better suit either approach.
You can do well by sticking to the traditional software development strategy if:
you face a simple project. In this case, you won’t deal with complicated data-sorting procedures or hard decision-making choices to resort to AI’s help.
the project must be accomplished on short notice. AI mechanisms need quite a time for setting up and fine-tuning. Being in a hurry, you can hardly hope to make the best of such a sophisticated technology.
you have limited resources. AI projects require significant computer power and seed capital to see them through. Missing those, it is preferable to move along the traditional pathway.
your workforce lacks qualification. Unless your internal team has proper competence in leveraging AI software development tools, you can’t hope to implement the project up to the mark.
your project is not data-dependent. To function properly and yield adequate results, AI mechanisms must sift through tons of relevant, consistent, and accurate data. In case you can’t provide it, AI-powered software development will fall short of its expectations.
By contrast, AI tools will bring maximum value to your digital project if:
you have a complex project to complete. The more complex the project objectives are, the more need for AI power it will require. Such projects are just out of the traditional approach's league.
you have huge volumes of initial data. It will be overwhelming for humans to process and analyze, so the involvement of advanced AI data analytics tools is the prerequisite for tackling such projects.
you confront uncertain project conditions. Ambiguous data, vague requirements, or numerous variables and constraints can baffle traditional project teams. In contrast, AI algorithms will find solutions much quicker and more efficiently than IT personnel relying on manual decision-making methods.
your project requires high adaptability. Sometimes, the problem to be addressed is very dynamic or volatile, with the product likely to evolve over time, so tackling it with strict and predefined rules governing traditional SDLC is impossible. In such cases, AI reigns supreme, digesting new data and experiences and learning in the process.
As you can see, each approach excels at different use cases. Yet, with the rapid advancement of low-coding/no-coding practices, even simple projects can greatly benefit from harnessing AI-driven software development. That is why the most sensible way out of the traditional vs AI-powered SDLC dilemma is adopting a hybrid model that combines the best of the two worlds. You can leverage AI to boost productivity and automate repetitive tasks but retain human oversight in mission-critical decision-making instances and engage employees in creative problem-solving.
To maximize the contribution of artificial intelligence to software building routine, you should opt for the right tools, and Metatable.ai is a second-to-none smart instrument for accomplishing digital projects swiftly and efficiently. This comprehensive AI-based platform contains everything you need to perform the end-to-end creation of first-rate web apps, being guided by the configurable AI assistant through all stages of SDLC. Reinvent your software development pipeline by harnessing the power of cutting-edge innovation with Metatable.ai!
Final Thoughts
Contemporary software engineers have two approaches to creating digital products. Traditional SDLC is valued for its predictability and thorough preparation of all stages, as well as its reliance on human oversight, but it is criticized for the enormous time and effort required to implement projects. AI-driven software development is more flexible, fast, error-free, and cost-efficient (in the long run), allowing developers to delegate routine coding and testing tasks to machines and focus on the creative aspects of SDLC.
To make the right choice of software development technique, you should consider the scope and peculiarities of the project, the expected delivery time, the size of your wallet, and the expertise of the available IT personnel. The most optimal way out is to combine both approaches, delegating repetitive assignments to machines and retaining the human touch for creative problem-solving. Besides, you can maximize the value of AI-powered software development by leveraging top-notch tools for software creation.
Ready to try AI-assisted software development? Start with Metatable.ai, and we can’t wait to hear your thoughts!