The problem with poor software is that it doesn’t just taint your image; you also lose revenue. Software engineering best practices have undergone many changes in recent years. Capers Jones said in 2009 that software engineering needed to discuss many technical issues “to become a legitimate term for an occupation that has been a craft or art form rather than a true engineering field”. Since then, software engineering has evolved to more iterative development processes, flexible and automated.
The best practices in software engineering in 2026 focus on collaboration, continuous delivery, and adaptation to emerging technologies, especially artificial intelligence and machine learning.
We have curated a list of best practices in software development in 2026, along with examples. Whether you’re leading a team of experienced engineers or newbies, these practices can help ensure your project’s success.
Why are Software Engineering Best Practices so Important?
Software engineering best practices are crucial for producing high-quality, reliable, and maintainable software. Agile Project Management is not enough to ensure that software is robust, secure, or can adapt to changing needs over time.
In fact, many software engineers resent Agile. Robert Martin, also known as “Uncle Bob”, author of Clean Code and Clean Architecture, attacks what he and Martin Fowler (co-author of the Agile Manifesto) call “Flaccid Scrum”, which means “Scrum without Software Engineering Best Practices”. According to him, Flaccid Scrum leads to lower productivity overall.
In contrast, these software engineering giants always recommend Extreme Programming, which was an Agile Framework before the Agile Manifesto was even written. Extreme Programming shares many elements with Scrum, while keeping software engineering best practices.
In short, software engineering best practices are unavoidable if you want to develop and deploy the best software.
Ten Essential Practices for Software Engineers to Implement
There are many software development practices that we can’t possibly cover in this piece. But here are ten practical tips to guide your team in 2026.
1. Requirements Gathering & Analysis
In software engineering, requirements gathering is the process of defining exactly what the software should do and how it should perform. It’s the most critical step in the SDLC.
Fixing a misunderstanding during the design phase costs almost nothing. On the other hand, fixing the same misunderstanding after the code is written requires rewriting, re-testing, and re-deploying.
It also avoids “scope creep”, the slow, uncontrolled growth of a project’s goals. Without a defined list of requirements, it is impossible to say “no” to new feature requests. Gathering requirements creates a baseline that keeps the project on schedule and within budget.
Key Requirements Elicitation Techniques
Gathering requirements isn’t just about asking, “What do you want?” It’s a systematic process of discovery, negotiation, and documentation.
- Interviews: One-on-one or group discussions with stakeholders to gain detailed, qualitative insights.
- Workshops: Collaborative, structured meetings involving users and stakeholders to define requirements, resolve conflicts, and foster agreement.
- Brainstorming: An informal, creative group session used to identify new, innovative, or undocumented ideas.
- Prototyping: Creating preliminary versions or wireframes of software to test functionality, reduce ambiguity, and gather feedback early.
- Observation (Job Shadowing): Watching users perform their daily tasks to understand operational workflows, pain points, and implicit needs.
- Surveys: Efficient tools for gathering high-level, quantitative information from a large, dispersed group of stakeholders.
- Document Analysis: Reviewing existing system documentation, business plans, and competitors’ products to identify requirements.
- Scenarios: Modeling interactions to define how users will interact with the system to achieve specific goals.
- Focus Groups: Small, guided groups that provide feedback on specific aspects of the project, useful when individual stakeholders are unavailable.
Key Techniques from the Product Discovery
Product Discovery is an ongoing process of discovering what needs to be done without asking the customer what they want.
- User Journey Mapping: A visual timeline of a customer’s interactions with your brand. It tracks their actions, emotions, and pain points from initial discovery to final goal.
- Jobs To Be Done: It focuses on the “job” a customer hires a product to do. JBTD identifies the functional, emotional, and social progress a user seeks in a specific circumstance.
- Persona: A persona is a representation of a relevant group of a company’s ideal target audience. Personas are built based on research and real data about potential customers, considering aspects such as demographics, behaviors, and specific needs.
2. Coding Standards
Maintaining a consistent coding style throughout the project is a best practice for software engineering. It includes meaningful names for variables, functions, and other entities. It also includes proper indentation and dividing complex logic into manageable chunks.
Don’t forget to keep your code clean. A clean code is a robust code that is easy to read and maintain. A robust code handles errors and unexpected situations gracefully. A readable code is a code with proper indentation, clear structure, meaningful names, and consistent formatting. Finally, maintainable code should prioritize ease of understanding for unfamiliar developers.
3. Improve Code Quality
To improve code quality, you must understand its three basic principles. DRY, KISS, and YAGNI. DRY means “Don’t Repeat Yourself”; avoid redundant code. Every piece of knowledge or logic within a system should have a single, unambiguous, authoritative representation. In other words, if your bank system already has a deposit function or class, don’t create other deposit functions or classes. It will make a mess.
KISS means “Keep It Simple, Stupid”. Most systems work best if they are kept simple rather than made complex. Avoid unnecessary complexity in your code; only add complexity when really needed.
YAGNI, “You Aren’t Gonna Need It”, advises against adding functionality until it is absolutely necessary. So, if you are planning to create an API that will connect your application to its many microservices while your software is still a modular monolith is not the best idea. Let your grandiose plans have a time to shine later.
4. Version Control Management
When submitting code for review, it is best to make smaller pull requests (PR) or change lists, not larger ones. One of the main reasons for this is that it makes the job of the code reviewer easier.
Nothing is worse as a code reviewer than reviewing 1,000 or 2,000 lines of code. And because reviewing that many lines of code is difficult, it is easier for a bug to introduce itself in the pull request and eventually into the central repository.
Ideally, make your pull request under 200-400 lines for thorough and faster reviews.
Also, it makes it more challenging if the requester eventually has to fix the pull request because a bug made its way into the main code repository. The more code you have in a PR, the more likely you are to run into a merge conflict.
This is especially useful if you work at a large company like Google or Facebook, where many engineers share the same code repository. So, try to err on the side of smaller pull requests.
That might mean the requester has to put in more effort because they’ll have to break up their code into pieces that can be submitted as separate pull requests. But it will go a long way in improving the entire software development process.
Git is the industry-standard distributed version control system, while platforms like GitHub, GitLab, and Bitbucket provide collaborative interfaces for code hosting and DevOps. Other top tools include SVN (centralized), Mercurial (distributed), and Perforce (high-performance for large files).
5. Shifting-Left Testing
Shift-Left Testing is the practice of moving testing and security checks earlier in the development lifecycle to reduce rework costs. From the beginning of a project, integrate various testing types such as integration testing (ensuring parts work together), end-to-end testing (simulating user scenarios), and Test-Driven Development (TDD) (writing tests before code).
For example, writing unit tests is one of the easiest ways to improve code quality. Unit tests focus on small, modular pieces of code to test one functionality at a time. Consider different scenarios, including deliberately making test cases fail to observe behavior, and aim for 100% test coverage.
Run all tests before requesting a review to catch bugs early, as fixing defects in early stages can be significantly cheaper than after release. Also, consider training the whole team in basic testing, even juniors. If the engineers can perform basic testing before sending their tasks to the test phase, the QAs can focus on deeper level testing. This is the essence of Shifting-Left Testing.
Also, don’t forget to automate aspects of testing and review using linters, vulnerability scanners, and tools for checking best practices and coverage. AI tools can significantly streamline code reviews by detecting issues and providing suggestions.
6. Effective Code Reviews
Engage in code reviews where another software engineer examines your code or pull request to improve overall code quality, identify bugs, and reduce future optimization work.
Stack related pull requests to separate logically distinct changes, enabling parallel work and more granular feedback. For example, while coding an API, you might feel inclined to split your pull requests into three parts: “Front-End”, “API endpoints and business logic”, and “database and schemas”. In that way, your reviewer might be able to review each snippet with clear intent.
Google researchers found that limiting review sessions to under 60 minutes helps reviewers to maintain focus and accuracy.
Use a code review checklist to standardize the process and ensure checks for functionality, readability, maintainability, security, performance, and coding standards. Maybe the company already has one. Use it.
Document and explain code decisions, including the motivation, alternatives explored, and trade-offs, to provide context for reviewers and future maintenance.
7. Design Patterns
Instead of designing things from scratch, it’s an advisable best practice for software engineers to look for design patterns. A design pattern is a solution template used often to create software. Refactoring Guru compares them to “pre-made blueprints that you can customize to solve a recurring design problem in your code”.
They are not code snippets; they are more like design choices for architectural decisions. While design patterns are more under the scope of software architects, in some companies, there is no architect, so a software engineer must decide for the project. Even if there is an architect, a software engineer must know how to build them.
Design patterns come in three flavors: creational patterns, structural patterns, and behavioral patterns. Creational patterns create reusable objects, structural patterns connect different objects, and behavioral patterns assign objects with roles and responsibilities.
8. Optimization and Performance Practices
There are many best practices to optimize and add performance to your software. Among them, it’s worth noting:
- Asynchronous Jobs
- Cache-Aside Patterns
- Parallelism and Concurrence
- SQL Performance and Optimization
- Content Distribution Network (CDN)
Asynchronous Jobs
Asynchronous jobs are tasks that run independently and concurrently, without blocking other processes. They aim for more efficient use of system resources and a more responsive user experience. Instead of waiting for one operation to complete before starting another, asynchronous programming enables multiple tasks to proceed simultaneously.
Cache-Aside Patterns
Applications use a cache to deliver information and data held in databases. That being said, cached data won’t always match the real data stored in the database. A cache-aside pattern is a strategy to update the cache, to keep it relevant and reduce database overhead.
Parallelism and Concurrence
Concurrency is when multiple threads can be paused and resumed. In other words, it’s when a CPU pauses one task to prioritize another. Parallelism is when tasks are running on multiple cores (a quad-core CPU means four tasks running at the same time). In short, concurrency is about dealing with many things at once; parallelism is about doing many things at once. Pausing parts of the application while allowing the meaningful ones to run optimizes performance.
SQL Performance and Optimization
Optimizing SQL performance is key to guaranteeing SQL queries are as fast and smooth as possible, improving performance on any database that uses a relational database. This is especially concerning when dealing with Big Data, where many tables might have millions of rows.
Content Distribution Network (CDN)
A Content Distribution Network (CDN) is a server cluster distributed worldwide. They store cache content near the end user, making access to the services easier and faster. Important websites use this performance practice, such as Amazon, Facebook, Google, and Netflix.
Don’t Overly Optimize or Modularize Upfront
You complicate the code and risk improving runtime inconsequentially when you optimize early. Instead, get a clean, clear algorithm and prioritize it later. Similarly, trying to make the system modular and extensible should be done moderately. Wait until a need arises, or you risk making the wrong interfaces and wasting time and resources.
9. AI-Assisted Development
AI-Assisted Development consists of using LLMs for code generation and automated refactoring, and deploying AI tools for static analysis and vulnerability scanning.
For code generation, have in mind that AIs don’t make architectural decisions alone, nor code considering your business needs, no matter how much context you give them in a prompt.
According to a METR study published in July 2025, senior developers using AI tools were, on average, 19% slower at completing tasks compared to working without AI assistance. The reason for the slowness is both prompt and code refactoring. Despite decreased speed, these developers perceived themselves as being 20% faster when using AI. The phenomenon was described as a “productivity placebo”.
To maintain high standards while leveraging these tools, divide the AI workflow into two categories: high-velocity delegation and high-stakes manual oversight.
| Leveraging AI | Avoiding AI |
| Boilerplate & Scaffolding: Generating repetitive structures, standard API endpoints, and setup files. | Architectural Decisions: Deciding how systems scale, how data flows, and how the overall design remains coherent. |
| Unit Testing: Rapidly generating test cases for various edge scenarios. | Ambiguous Requirements: Navigating the “gray areas” where business needs are poorly defined or contradictory. |
| Documentation: Drafting initial versions of README files or inline comments based on existing logic. | Root-Cause Debugging: Solving deep, systemic bugs that AI-generated “quick fixes” often overlook or exacerbate. |
| Refactoring Logic: Using LLMs to suggest more idiomatic ways to write a specific, isolated function. | Long-term Sustainability: Ensuring the code remains maintainable five years from now, not just “runnable” today. |
10. Automated And Secure CI/CD Pipelines
Continuous Integration and Continuous Deployment (CI/CD) pipelines are the backbone of high-performing engineering teams. A robust pipeline ensures that every code change is automatically built, tested, and prepared for release, reducing human error and increasing deployment frequency.
- Continuous Integration (CI): Every time a developer pushes code, the system should automatically run unit tests and linters. This catches “breaking changes” immediately, preventing technical debt from accumulating.
- Continuous Deployment (CD): Once code passes all checks, it should be deployed to a staging or production environment automatically. This eliminates the “it works on my machine” syndrome and allows for faster feedback loops with users.
Automation without security is a recipe for disaster. Integrating security into your pipeline (shifting left) is a critical best practice:
- Secrets Management: Never hardcode API keys or passwords. Use specialized tools (like HashiCorp Vault or AWS Secrets Manager) that your pipeline can call securely.
- SCA and SAST: Implement Software Composition Analysis (SCA) to check for vulnerabilities in third-party libraries (like Log4j) and Static Application Security Testing (SAST) to scan your own code for security flaws before it even hits production.
- Container Scanning: If you are using Docker or Kubernetes, ensure your pipeline scans images for vulnerabilities during the build process.
Conclusion
Software engineering best practices can help you build maintainable, scalable, and secure software products.
We have shared ten software engineering practices that can enhance the outcome of your software projects. However, this is not an exhaustive list. The technology industry constantly evolves, so it can be tricky to keep up with the latest trends, especially when you’re looking for talent.
That’s where we can help at DistantJob. We can help you find and vet potential hires to ensure they stay up-to-date with industry trends. Our culture-centric approach ensures that the candidates not only have the technical skills but also fit well into your company’s culture.
In addition, we take care of all the HR processes, from finding suitable candidates to onboarding them. This enables you to focus on building your software products and growing your business. Ready to get started? Contact us!
