Engineering brilliance can’t rescue a poorly managed project. The most popular software development methodologies (also known as models) define how teams communicate, make decisions, commit code, and maintain quality. Whether for small mobile apps or enterprise management systems, your success largely depends on the methodology.
This article breaks down key types of software development methodologies, including hybrid models that combine elements of well-known approaches. We will demonstrate how they work, where they shine, and where they could fall short. Finally, we provide tips to help you choose and adopt the right model.
What are software development methodologies?
A software development methodology (model) is a structured framework for planning, executing, and managing software projects throughout the software development life cycle (SDLC). It defines how teams approach requirements gathering, design, coding, testing, deployment, change management, quality assurance, and other tasks to deliver reliable, maintainable software.
The purpose of the software development methodologies
“Everything in software changes. … The problem isn’t change, because change is going to happen; the problem, rather, is our inability to cope with change.” - Kent Beck, Agile methodology creator.
Think of a methodology as a way to structure the development process that impacts your ability to produce value and respond to changes. Here are a few core goals a well-defined model can provide:
Task priority during development. The methodology defines rules and tools used in the development, priority tasks, and approaches to testing and quality assurance.
Project roles. Software development methodologies clarify team roles and responsibilities. Companies can set accountability for each team member or role (who approves features, handles bugs, or makes architecture decisions) to eliminate confusion.
Adaptation based on feedback. Modern software development methodologies empower teams to test product prototypes and gather feedback that can be used to shift priorities or change direction.
Collaborative aspects. A development model (method) sets how developers, testers, product owners, and customers assess results.
Budget estimation. Models set the rules for gathering product requirements, managing changes, and approving critical decisions. They have a significant impact on the way you calculate the budget and delivery timelines.
Methodologies try to bring order into the creative and often unpredictable process of software development.
Some methods are more popular than others. For example, nearly 47% of companies in 2022 used DevOps for development (according to Statista’s 2024 report). About 49% of larger and 45% of medium-sized organizations prefer a hybrid methodology that includes aspects of different models (2023 State of Agile Report).
Given their differences, we should go through the complete list of software development methodologies and explain how they work.
Sequential methodologies
Sequential methodologies treat the software lifecycle as a linear sequence of events. Each step produces a set of deliverables that become the foundation for the next, with key requirements and decisions made early.
Types of sequential methodologies
1. Waterfall
How the methodology works:
Waterfall divides the SDLC into clearly defined, sequential steps of software development. Each stage must be completed before moving to the next one, like water flowing down a set of steps.
There’s little to no iteration or overlap between stages. Once the requirement is signed off, the team moves to the next stage.
Suitable for:
Waterfall best suits smaller software projects with fixed scopes, low uncertainty, and strict regulatory constraints. It could fit smaller restaurant-ordering apps or fixed-bid government contracts with locked-down deliverables.
Pros:
Each phase has a clear start and end, making progress easy to track and report.
The stages have exhaustive documentation with explicit references.
Full scoping is done upfront, making the delivery more predictable.
Cons:
The project is inflexible after it enters the design or implementation stage, and changes can cause major disruptions.
Clients are primarily involved in the initial planning and have a limited impact on the development.
Many problems can be revealed only at later stages of testing, meaning no early validation or user acceptance.
Practical insights:
Waterfall methodology requires extensive investment in the research and requirement-gathering stages. Clarity here prevents scope creep and rework later.
Establish a formal impact assessment and change control, as even minor adjustments can affect downstream design and timeline.
Conduct a formal gate review to validate deliverables at each stage.
Never rush through the early stages and allocate resources proportionally, as mistakes compound.
2. V-Model (Verification and Validation)
How the methodology works:
The Validation and Verification Model is an extension of the classic Waterfall with a key difference: each development state has a testing phase. For example, requirement analysis is paired with acceptance testing, and architectural design is validated with integration and unit tests. This difference makes quality assurance continuous instead of treating testing as a downstream activity.
Suitable for:
The V-Model works for regulatory-heavy projects with clearly defined requirements that still rely on stable performance. These can include medical (healthcare) devices, aerospace control software, and automotive apps.
Pros:
Every major design decision is matched with a validation mechanism, making the outcomes more predictable.
Projects are clearly defined and documented (like in Waterfall).
By linking requirements to a test case, the project becomes more traceable.
Cons:
Like Waterfall, the V-Model assumes that all requirements can be known and fixed upfront. Midway changes can ripple design and testing plans.
The extensive overhead of paired design-validation phases can be overkill for smaller projects.
Detailed test cases, traceability matrices, and validation plans at every stage demand more upfront resources.
Practical insights:
Plan all tests as soon as your functional and design specs are drafted. Ideally, the acceptance criteria, edge case tests, and integration checks should be designed during the requirements-gathering phase.
Use traceability matrices to map every requirement to its verification method.
Involve the QA team during architecture reviews, requirement discussions, and design sessions to reduce backtracking further down the SDLC.
3. Prototyping
How the methodology works:
In the Prototyping model, teams focus on building working, interactive models of parts of the software. Prototypes allow teams to clarify project requirements, validate their functionality, and expose usability issues with real users.
Prototyping methodology follows a sequential order of phases but allows iterations within some phases. Unlike traditional linear methods that rely on documentation and long planning, prototyping encourages early experimentation before committing to full-scale production.
Suitable for:
Prototyping is a good fit for innovative projects that require customer validation, as well as software with unclear (loosely defined) requirements.
Pros:
Prototyping helps stakeholders identify missing features, edge cases, and design flaws that are almost impossible to catch in controlled testing environments.
The ability to spot more usability gaps and bugs reduces late-stage reworks, which are usually the costliest.
Customers feel more involved as their feedback shapes the production and user experience.
Cons:
Users who see more working versions tend to request more features (scope creep).
Successful prototypes can instill a sense of false confidence. However, a working prototype doesn’t mean the developed system will behave the same way.
Creating and revising multiple working prototypes can consume a lot of resources and time from every team.
Practical insights:
Keep the features to a minimum for each prototype. Before building, decide what the prototype is meant to validate (for example, user flows, UI aesthetics, or integration logic).
Don’t wait for the perfect prototype — show progress early, gather feedback from users and product owners, and iterate.
Expect to abandon early prototypes. They are rarely foundations for full-scale systems but somewhat disposable learning tools.
4. Rapid Application Development (RAD)
How the methodology works:
The RAD methodology prioritizes rapid prototyping and frequent iteration over long planning cycles. The requirements-gathering phase is intentionally short and done to outline the core business needs instead of extensive documentation.
Developers and users work side-by-side to iteratively build and refine functional prototypes. Once the prototypes have been validated, they are integrated into a broader system and tested again.
Suitable for:
One can choose RAD for projects that must be developed quickly and require extensive user testing. For example, a marketing agency could use it to get fast feedback on an ad campaign tracking tool.
Pros:
RAD allows tasks to be run in parallel, skipping lengthy documentation and requirement gathering.
Continuous user input throughout the prototyping makes it more likely to create a product that users need and understand.
Teams can shift priorities more easily (than in Waterfall) without derailing the timeline.
Cons:
RAD depends on experienced developers, designers, and testers who can work independently and solve problems quickly.
As the team or project scope grows, managing simultaneous modules and components can become too challenging.
You need a constant feedback loop with business users who can validate features in real time.
Practical insights:
Limit each prototype cycle to fixed durations with clear exit criteria to avoid chaos in development.
Use low-code platforms, component libraries, wireframe tools, and rapid backend frameworks to accelerate prototyping.
To reduce integration debt, you should go with modular (pluggable) architectures.
5. Rational Unified Process (RUP)
How the methodology works:
RUP is a phase-driven methodology that incorporates elements of iterative development within a tightly controlled sequential framework. The main difference from Waterfall is that the feedback cycle and adjustments happen within each phase (inception, elaboration, construction, and transition).
Suitable for:
RUP is a good fit for large-scale projects with complex requirements that can evolve over time. The methodology also works for software with a high cost of failure and that operates in a strict regulatory environment. Developers might choose RUP with detailed audit trails to build a custom pension management system for a government agency.
Pros:
Every phase is clearly defined but allows room for learning and refinement.
Technical, organizational, and usability risks are managed early to prevent surprises during later stages.
RUP delivers high levels of traceability (from business requirements to final deliverables).
The methodology is customizable, meaning companies can add or remove activities, workflows, and artifacts based on the project’s scope.
Cons:
A large taxonomy of roles, artifacts, and workflows may overwhelm some teams.
RUP requires large teams and a significant investment in planning, documentation, and training.
While it allows for iterative development, RUP is less flexible than Agile methodologies. Drastic mid-phase changes can disrupt the phase gate and result in a massive rework.
Practical insights:
Don’t go strictly by the book — you can strip RUP down to workflows that your project actually needs.
Use mini-loops within elaboration or construction phases, but avoid bouncing between phases to keep the process structured.
Focus on system architecture in the Elaboration phase, as it defines layers, technology stacks, and scalability levels that minimize technical debt.
Agile methodologies
Agile methodologies break down the SDLC into a series of smaller development cycles. Each cycle is dedicated to specific software components and followed up by rapid feedback.
Iterative methodologies for software development promote adaptive planning, evolutionary development, early delivery, and continual improvement. Each methodology has its distinct strengths, use cases, and implementation strategies.
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Types of Agile methodologies
“Agility is more attitude than process, more environment than methodology.” - Jim Highsmith, software engineer.
1. Scrum
How the methodology works:
Scrum centers on delivering value in short, fixed-length sprints. These sprints usually take two to four weeks, resulting in product deployment-ready increments. Scrum is usually divided into several phases, such as sprint planning, daily stand-ups (meetings where the team sync progress), sprint review (happens at the end of a cycle), and a retrospective (assessment of a larger number of sprints or a finished product).
What makes Scrum unique is a sharp focus on team roles, primarily:
Product owner: Responsible for product vision and maintains contact with the customer (or the company’s business leaders).
Scrum master: Keeps the team focused and handles impediments that detract from development.
Development team: Developers act as a cross-functional and self-organized group where each member is collectively accountable for outcomes.
Suitable for:
Scrum thrives in experienced teams that can work independently and communicate effectively. It’s usually chosen for projects where requirements can change frequently and when companies want to rush to the market with a minimum viable product (MVP).
Pros:
Regular feedback loops through sprint reviews and retrospectives help expose more problems.
Developers feel more responsible for the results of their work.
Short, consistent sprint cycles create a rhythm for planning and delivery so the stakeholders know when to expect updates.
Cons:
Scrum is more optimized for smaller teams, and scaling to enterprise-level projects may require a more complex framework to maintain control.
Scrum can degrade into chaos if teams skip regular backlog assessments and retrospectives or treat stand-ups as status update meetings.
Without proper management, the intense pace of sprints can burn people out and, ultimately, affect productivity.
Practical insights:
Make sure everyone knows who owns delivery, process, and priorities. The Scrum Master isn’t the team lead, and the product owner isn’t a project manager.
Treat the sprint review not just as a demo but as a moment to gather feedback and pivot. Use retrospectives to identify root causes of friction and try improving in the next sprint.
Avoid over-committing during sprint planning. Rank items by business value and keep the sprints laser-focused.
2. Kanban
How the methodology works:
Kanban focuses on visualizing tasks and switching between stages. In this methodology, developers visualize tasks as cards on a board, divided into columns representing different workflow stages (usually, To Do (or Backlog), In Progress, Code Review, Testing (or QA), and Done).
Kanban doesn't dictate roles or strict protocols. As team members begin work, they move cards across the board. The tasks themselves are switched based on priorities and team capacity.
Suitable for:
Kanban is more of a management system that can help any project. It’s particularly useful for projects where incoming work is frequent and unpredictable but needs to be addressed quickly.
Pros:
A well-maintained Kanban board communicates team progress at a glance. Teams can see who’s working on what, what’s worked on, and where the team faces bottlenecks.
Kanban makes it easier to reprioritize if new critical tasks appear (if the team load and capacity allow it).
Teams analyze lead time (total time the task was tracked across columns) and cycle time (how long the task took after the team started working on it actively) to highlight inefficiencies and re-balance workloads.
Cons:
Kanban doesn’t guide feature planning, architectural evolution, concrete roles, or cross-team coordination. You will need other Agile methodologies to handle more complex projects.
Without iterations or deadlines, it’s harder to predict exact delivery timelines. Some teams may struggle with scope creep without planning boundaries.
Practical insights:
Limit work in progress to focus on specific tasks. It prevents the engineers from switching contexts and clogs the project with unfinished work.
The Kanban board must accurately reflect your actual progress. For example, if “Review” has become a bottleneck, make it its own column with more tasks.
Define the rules that govern work transitions and board usage (for instance, you can prohibit moving the task to “Review” without going through specific test cases).
3. Lean
How the methodology works:
Lean combines manufacturing principles with software delivery, focusing on delivering business value and stripping away anything that doesn’t. It centers around waste elimination, learning, optimization, and delivering in small batches.
Suitable for:
Lean suits experienced cross-functional teams with a high degree of autonomy. It can benefit startups that desire continuous improvement, fast iterations, and high user involvement in the SDLC.
Pros:
Lean ditches excessive processes that don’t benefit development, which helps reduce expenses, time, and effort.
The methodology integrates quality checks and short feedback loops from users into the SDLC, making it easier to pivot late into development.
Lean developers are autonomous enough to solve problems themselves instead of just being micromanaged.
Cons:
Too much freedom can backfire if engineers lack discipline or judgment.
Some process optimizations in Lean can sabotage global outcomes and hurt value. For example, cutting the time for QA doesn’t help if bugs lead to rework.
The loose structure can falter at scale so that larger enterprises may combine Lean principles with other methodologies.
Practical insights:
Encourage teams to reflect on activities, features, or reports that don’t serve the development.
Instead of micromanaging every step, define clear outcomes and allow engineers some sovereignty.
Ship small but often, using real-time usage data and customer feedback to verify whether features solve real problems.
4. Extreme Programming (XP)
How the methodology works:
Extreme Programming (XP) focuses on rapid software delivery, continuous integration, automated testing, and pair programming. For example, developers write tests that define code behavior before writing anything. Usually, two developers work at a single machine: one writes the code and the other one reviews each line in real-time, frequently switching roles.
XP discourages over-engineering. The design should be the simplest possible to meet current needs. Meanwhile, the code is continuously cleaned up — renamed for clarity, simplified, or restructured
Suitable for:
XP works for projects with tight deadlines, vague requirements, and evolving business goals. It fits startups, innovation labs, and highly technical products where deployments matter more than heavy documentation.
Pros:
Shorter iterations help the teams gather real feedback before bad decisions and unnecessary features impact the development.
Developers are forced to build high-quality software that adheres to code quality standards — they can’t rely on the QA to clean things up later.
Frequent retrospectives and refactoring allow the engineering team to learn about code conventions, write better tests (not just code), and understand design principles.
XP includes an "on-site customer" or product representative who ensures the features are grounded in real-world needs.
Cons:
Pair programming doubles up developer time per task and, potentially, a customer representative who can answer questions and validate work on a regular basis.
Face-to-face pairing, frequent whiteboarding, and rapid code sharing work well for smaller teams, but scaling XP can introduce overhead and drive up expenses.
This software development methodology has little tolerance for sloppiness or lack of management.
Practical insights:
Set clear rotation schedules and rules for the developers working in pairs. Otherwise, pair programming can devolve into just two engineers working on their own tasks.
Automated tests and refactoring should happen regularly. Such practice encourages engineers to improve the codebase nearly daily and can prevent technical debt.
Clarify the responsibilities of the “on-site customer” to make sure the role is embedded with the team.
5. DevOps
How the methodology works:
DevOps is often seen as a philosophy and a set of practices that combines software development (Dev) and IT operations (Ops) into a single workflow.
The key components of DevOps include:
Continuous integration and continuous delivery: Repetitive tasks are handled by scripts and tools that test each code commit.
Feedback loops: DevOps encourages quick feedback via testing, infrastructure monitoring, and user input.
Shared responsibility: Engineers and operations teams co-own the results (quality, performance, and uptime of their deployments).
Modularity: Teams favor modular (loosely coupled) systems that allow deploying features independently and containerized environments that make it easier to move and test services on different operating systems.
Suitable for:
DevOps is a go-to model for experienced teams where developers, testers, security experts, and operations teams collaborate in tight loops. It allows for frequent (almost routine) deployments with minimal downtime risks. For example, SaaS fintech apps and streaming services.
Pros:
Automation eliminates bottlenecks in testing, configuration, and release, which can reduce time-to-market.
Testing and deployment are highly automated and repeatable, allowing fewer hotfixes and faster recovery times.
Cons:
Companies require high initial investments to set up a proper DevOps pipeline, which involves tooling, process and policy changes, and employee training.
DevOps demands a mindset shift in traditional organizations, especially regarding shared responsibilities.
Both developers and Ops teams must have proper knowledge of the deployment pipelines and infrastructure.
Organizations must avoid vanity metrics and instead prioritize the key performance indicators that help gauge the efficiency of the SDLC pipeline.
Practical insights:
Set clear DevOps metrics that help evaluate your tech capabilities and identify inefficiencies. These can include deployment frequency, lead time for changes, change failure rate, defect escape rate, and mean time to recovery.
Sequential vs. Agile software development methodologies: comparison
Companies can address different project demands with sequential and Agile methodologies. Let’s compare and contrast these software development methodologies to highlight their key differences.
| Aspect | Sequential | Iterative (Agile) |
|---|---|---|
| Project requirements | Requires clearly defined technical requirements from the outset | Effective with evolving requirements and user involvement |
| Team skills | Works for most teams and needs less communication between roles | Works best with highly collaborative, self-organizing, cross-functional teams |
| Risk management | Risks primarily addressed upfront | Ongoing risk detection and mitigation |
| Estimates | Prioritizes predictable outcomes | Timelines and cost may fluctuate |
| Change management | Changes are more expensive and disruptive further into the SDLC | Changes happen systematically in the workflow |
| Quality assurance and testing | Testing occurs after implementation (or at the end of a phase) | Testing occurs continuously throughout development |
| Time-to-market | Slower due to extended planning, documentation, and sequential phases | Faster delivery cycles due to rapid iterations, prototyping, and immediate feedback loops |
| User input implementation | Feedback is mostly limited to initial and final phases | Consistent user involvement and usability testing |
| Best suited for | Regulated industries, fixed-scope government projects, well-defined IT projects | Startups, innovative products, consumer apps, or rapidly evolving software services |
Our comparative study on software development methodologies shows that models are a tool. It’s how you use these models for a particular project that matters.
“The amateur software engineer is always in search of magic, some sensational method or tool whose application promises to render software development trivial. It is the mark of the professional software engineer to know that no such panacea exists” - Grady Booch, creator of the Unified Modeling Language.
Some companies don’t limit themselves to models. Instead, they combine different elements and practices to suit their needs.
Hybrid methodologies
Hybrid models combine aspects of sequential and Agile methodologies in software development. You have the freedom to combine these methodologies the way you want. Among the existing hybrid methodologies, we’d like to highlight two of the most popular ones:
Types of hybrid methodologies
1. Incremental model
How the methodology works:
The incremental model structures software development into increments representing a self-contained portion of functionality. Each increment undergoes phases (analysis, design, coding, testing, deployment) before moving on to the next.
Suitable for:
The Incremental model fits projects that need controlled delivery, demand frequent user validation, or plan to refine requirements as the development progresses. For example, an enterprise migrating legacy software to the cloud or a healthcare provider that adopts electronic medical records may prefer this methodology.
Pros:
The methodology doesn't require complete requirements at the beginning. Instead, the requirements can evolve progressively across these increments.
Risks during development decrease substantially because easy increments go through extensive tests.
Clearly defined phases and incremental delivery simplify communications with stakeholders. For example, the client can receive usable functionality after each increment.
Cons:
You need experienced project managers to manage several increments at different phases simultaneously.
If teams lack standardized protocols and tech stacks, merges can lead to compatibility issues over time.
Practical insights:
Companies must clearly define the deliverables for each increment (acceptance criteria, measurable outcomes, timelines, etc.).
Focus on critical functionality first. For example, a financial trading platform might prioritize implementing core trading features in early increments.
2. Spiral
How the methodology works:
The Spiral methodology blends the structured process of Waterfall with iterative models. It allows teams to repeatedly revisit SDLC phases in cycles (spirals), with each cycle representing one iteration.
Suitable for:
Spiral works for projects with defined requirements and high technical risk, where early fault detection can help save massive amounts of money on rework and compliance. It also works for projects where requirements can evolve.
Pros:
Risk planning is the centerpiece of the methodology. Basically, every iteration starts by asking, “What could go wrong?” and building a strategy around that.
Unlike most sequential models, spiral doesn’t demand fixed requirements and allows for incremental discovery during development.
The model encourages building throwaway or evolutionary prototypes early in the cycle. Each cycle allows us to assess whether the project is worth continuing in the current direction.
Cons:
Spiral projects consume more resources than most traditional models due to repeated iterations and prototyping.
Some teams lack the technical or domain expertise to assess risk accurately at every cycle.
A simpler Waterfall or Agile model might be more efficient and cost-effective for a basic app with well-defined requirements.
Practical insights:
Use tools like risk matrices, SWOT analysis, or fault tree analysis to reassess risks at the end of each cycle.
Define and review success (exit) criteria for each spiral to avoid scope creep, costly reworks, and budget overruns.
Build low-fidelity prototypes (mockups, code spikes, or wireframes to get stakeholder reactions and identify usability flaws, integration gaps, and other assumptions.
Maintain a spiral audit trail and a log with key lessons your team learned during development.
Choosing the right software development methodology: factors to consider
Different software development methodologies address different challenges. They define how teams collaborate, deliver products, and respond to change.
As long as each model comes with its trade-offs and blind spots, you'll need to understand whether a model fits your project, team, and market. Consider these criteria to choose correctly:
Project scope and requirements: Structured methods with sequential steps (like Waterfall or V-Model) when project requirements are well understood. However, these might not be flexible enough if your scope is likely to change or if you’re working with innovative projects that need a lot of trial and error.
Team expertise and structure: Agile needs seasoned developers who can self-manage, take ownership, and communicate well without micromanagement. Sequential or hybrid models are usually easier for less mature teams.
Stakeholder (customer) involvement: Agile and hybrid methodologies rely on regular input from clients and customers. However, these models may be unnecessary if the customer doesn’t want to have too much say in the production.
Risk tolerance: Projects that can’t afford post-launch problems (like aviation tools and healthcare software) may go with methodologies like RUP and V-Model that have tight controls, traceability, and a focus on testing. Projects that can course-correct may benefit more from Lean and XP models.
Market specifics: If developing for volatile markets, high-speed models like Lean and Scrum can help you deploy software faster. But if you work for the government or medical agencies, you may want to stick to RUP or other strict models.
Resource constraints: Software products with strict budgets (if you’re delivering under a fixed contract or a grant) need more predictability of sequential plans. Agile models might work better if their production allows for change and course corrections.
Cultural compatibility: Not all companies can provide the development and testing teams with too much autonomy. For instance, a slow-moving, top-down enterprise with layers of approval won’t adapt well to Lean or XP. Meanwhile, a tech startup that moves weekly can grind to a halt under Waterfall.
Selecting software development methodologies can impact the success chances of your project. These factors can point different types of businesses toward the right fit.
Which development methods are considered to be most cost-effective?
Sequential methods like Waterfall and V-Model make calculating the cost of building an app easier. These models have a more structured and predictable process. On the downside, the costs can spiral out of control if changes become necessary mid-way.
Iterative models are more cost-effective in uncertain environments or markets demanding frequent adjustments. Agile emphasizes iterative releases and continuous feedback, which can reduce unnecessary features and help shift priorities at any stage of the SDLC. Yet, it requires skilled developers and potent feedback loops with customers and end-users.
Which software development approach will be the best for a startup?
Startups typically choose Agile methodologies like Scrum and Lean due to rapid iteration, short feedback loops, and minimal upfront planning. For instance, a Scrum sprint allows teams to quickly develop minimum viable products (MVPs), gather user data, and adjust functionality accordingly.
Extreme Programming (XP) is another viable option for startups because it focuses on quality and continuous testing. For instance, a startup building an innovative mobile app can leverage continuous integration and immediate feedback loops to identify issues swiftly.
What’s even more important than a model is to choose the right technical partner that can adapt to your requirements of software development methodologies and understand your vision for the project
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Future of software development methodologies
Methodologies don’t stay in one place. Companies combine human and AI tools to automate (and accelerate) as many processes as possible and minimize the number of risks and bugs. The most notable trends that impact software development methodologies in 2025 include:
The growing role of AI for automation. Methodologies will increasingly integrate predictive analytics, intelligent automation, and AI-driven management in development and operations. According to IDC’s 2025 FutureScapes report, AI spending will reach $227 billion by the end of 2025, 67% of which will be invested in core business operations. Plus, about 63% of Statista’s 2024 survey respondents use AI-assisted development technologies.
Popularization of low-code and no-code tools. Low-code (no-code) platforms streamline software development through tools for visual programming, pre-built templates, and ready-made code. Forrester’s 2023 report predicts that generative AI technologies will drive low-code adoption, expanding the market to around $50 billion by 2028.
Emphasis on interoperability. Gartner's 2025 CIO survey highlights that over 80% of technology leaders will prioritize foundational capabilities like integration technologies and APIs (among others). This means that the teams will likely adopt methodologies that allow for early integration and compatibility planning.
Security-first integration. Security-focused methodologies like DevSecOps are projected to reach $52.67 billion by 2032 (from $5.89 billion in 2024). Companies increasingly integrate AI and machine learning into the SDLC, helping teams to identify threats at every step of software development.
Companies that follow the future trends of software development methodologies and blend models will be rewarded in the long term. That’s why many businesses will shift from the traditional approach toward hybrid models that combine different aspects of Agile, DevOps, and AI-enhanced automation.
Overcode has a mature team that follows Agile methodologies but can adapt to your project’s needs and business vision. Our focus is on building your product efficiently, keeping costs under control, and delivering top-tier quality.
