Software Design & Code Quality Interview Questions - Software Engineering

Software Design & Code Quality in software engineering focuses on creating software architectures and implementations that are maintainable, efficient, and aligned with requirements. Good design applies principles like modularity, abstraction, and separation of concerns, while code quality emphasizes readability, consistency, performance, and low defect rates.

1. How does the SOLID principle improve both design and code quality, and what trade-offs can it introduce?

The SOLID principles: Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion, guide developers toward creating maintainable, extendable, and testable code.

• Design Impact: Encourages modular, loosely coupled architecture where each class has a clear purpose, improving scalability.
• Code Quality Impact: Reduces code duplication, improves readability, and enables easier debugging.
• Trade-offs: Applying SOLID rigorously can lead to over-abstraction, increased class count, and higher upfront complexity. For example, overusing the Interface Segregation Principle might lead to too many small interfaces, making the system harder to navigate.

Example: Using Dependency Inversion to inject database connectors allows easy swapping from MySQL to MongoDB without touching core business logic. Thus, SOLID improves adaptability but must be balanced with practical constraints.

2. What’s the difference between high cohesion and low coupling, and why are both essential for code quality?

• High Cohesion: Ensures related functions are grouped within the same module, improving maintainability and reducing conceptual overhead.
• Low Coupling: Ensures modules are independent, so changes in one module minimally affect others.

Importance Together:

• High cohesion ensures each module does one well-defined job.
• Low coupling ensures modules can be changed or replaced without a ripple effect.
• Balancing both reduces debugging time, supports parallel development, and increases system robustness.

Example: In an e-commerce system, the payment module should handle only payment-related tasks (high cohesion) and communicate with the order module through an API instead of directly accessing its database (low coupling).

3. How does code smell detection relate to long-term maintainability, and can you give an example of a subtle code smell?

Code smells are structural weaknesses in code that suggest deeper design flaws. Detecting them early prevents technical debt accumulation, which impacts long-term maintainability.

• Subtle Example: Data Clumps, multiple functions repeatedly passing the same group of parameters (like latitude, longitude, altitude) might indicate the need for a class or struct.
• Impact: Leads to code duplication, higher maintenance cost, and potential inconsistencies if one value changes without updating others.
• Solution: Refactor into a Location object to encapsulate related data.

Note: Ignoring subtle smells increases the risk of cascading changes and bugs during system evolution.

4. How do design patterns affect both performance and maintainability, and when might they harm a system?

Design patterns provide proven solutions to common design problems, improving maintainability, scalability, and clarity by promoting best practices.

• Positive: Patterns like Singleton (for configuration management) or Observer (for event-driven systems) reduce redundancy and increase flexibility.
• Negative: Overuse leads to complexity and performance penalties. For instance, unnecessary use of the Decorator pattern can add deep object chains, increasing call overhead and making debugging harder.
• Rule: Apply patterns only when a problem truly fits the pattern, not for theoretical purity.

Note: Pattern misuse often occurs when developers prematurely apply them without concrete requirements.

5. In what ways does testability influence software design decisions?

Testability ensures software can be easily verified for correctness through automated or manual testing.

• Design Influence: Promotes modularity, dependency injection, clear interfaces, and minimal hidden states.
• Impact on Code Quality: High testability reduces regression risk, enables continuous integration, and improves developer confidence.
• A design ignoring testability often results in tightly coupled, hard-to-test code, leading to fragile systems that resist change.

Example: Designing with interfaces allows mocking of external services in unit tests.

6. How do static code analysis tools impact design quality in large projects?

Static code analysis tools (like SonarQube, PMD) automatically scan code for bugs, vulnerabilities, and adherence to coding standards.

Impact on Large Projects:

• Enforces uniform coding style across teams.
• Detects potential runtime errors early.
• Flags complexity hotspots for refactoring.

Design Quality Effect: Encourages modular, less complex code by highlighting overly long methods or high cyclomatic complexity. However, over-reliance may cause developers to focus on passing tool checks instead of addressing deeper architectural flaws.

7. How can poor naming conventions degrade both code quality and system design over time?

Names communicate intent. Poor naming leads to misunderstandings, wrong assumptions, and bugs.

• Example: A variable named temp might hold a file path, causing confusion.
• Impact on Design: Misnamed classes/functions misrepresent their responsibilities, potentially violating design principles like SRP (Single Responsibility Principle).
• Long-Term Effect: New developers waste time deciphering code, increasing onboarding time and risk of incorrect modifications.

Note: Adhering to clear, consistent naming conventions is one of the lowest-cost, highest-return code quality practices.

8. Why is refactoring essential even in well-designed systems, and what risks does it carry?

Refactoring improves internal code structure without changing external behavior, essential for adapting to evolving requirements.

• Benefits: Reduces complexity, removes code smells, and improves readability.
• Risks: May introduce bugs if not backed by comprehensive tests.
• Even in well-designed systems, evolving features can introduce entropy; regular refactoring prevents design decay.

Example: Extracting methods from a long function in a payment system to isolate validation logic.

9. What role does technical debt play in software design quality?

Technical debt refers to shortcuts taken during development that save time now but incur higher maintenance costs later.

• Impact on Design: Increases coupling, reduces cohesion, and makes the system brittle.
• While some debt is strategic (to meet deadlines), unmanaged debt erodes both design quality and development speed over time.

Example: Hardcoding configuration values instead of externalizing them saves initial effort but complicates scaling and environment-specific deployment.

10. How can continuous integration (CI) indirectly improve code and design quality?

CI involves frequently integrating and testing changes in a shared repository.

• Design Benefit: Encourages modularity since large, tangled changes cause frequent merge conflicts.
• Code Quality Benefit: Automated testing in CI pipelines detects regressions early.
• By enforcing small, incremental changes, CI prevents design degradation and promotes a cleaner architecture.

Example: A CI system rejecting builds with high cyclomatic complexity encourages developers to refactor and simplify code.

11. How do code quality metrics like Cyclomatic Complexity and Maintainability Index influence refactoring decisions?

• Cyclomatic Complexity (CC): Measures the number of independent execution paths. High CC (>10–15) indicates overly complex code that’s harder to test and maintain.
• Maintainability Index (MI): Combines CC, lines of code, and comment density into a single score. Lower MI signals poor maintainability.
• Refactoring Impact: High CC may prompt breaking functions into smaller, more focused ones; low MI may indicate over-complex modules needing simplification.

Example: A method with CC=25 might be split into smaller functions, improving readability and reducing bug probability.

12. What are the dangers of over-engineering in software design, and how can it harm code quality?

Over-engineering occurs when solutions are more complex than necessary for current requirements.

Dangers:

• Increased maintenance cost.
• Steeper learning curve for new developers.
• More dependencies and moving parts, raising bug risk.

Best practice: Design for current needs but ensure scalability without premature abstraction.

Example: Implementing a microservices architecture for a small tool with only two features introduces unnecessary network calls, deployment overhead, and debugging complexity.

13. How can architectural drift degrade both software design and code quality?

• Definition: Architectural drift occurs when the actual system implementation gradually diverges from the original planned architecture.
• Impact on Design: The documented architecture no longer matches reality, reducing trust in design artifacts and making onboarding harder.
• Impact on Code Quality: Leads to inconsistent design practices, tangled dependencies, and modules violating boundaries.
• Prevention: Regular architecture conformance reviews, static analysis tools, and strict enforcement of design principles.

Example: Adding direct database queries in a UI module instead of following the service layer breaks the layered architecture principle.

14. How does the “Law of Demeter” relate to code quality, and what are its practical limitations?

• Law of Demeter (LoD): “Only talk to your immediate friends”, classes should avoid chaining calls through multiple objects.
• Quality Impact: Reduces coupling, increases encapsulation, and makes code easier to refactor.
• Limitations: Too strict application can lead to bloated interfaces with many forwarding methods. Balance is key.

Example: Instead of order.getCustomer().getAddress().getCity(), provide order.getCustomerCity().

15. Can you explain the difference between an anti-pattern and a design smell, with examples?

Anti-pattern:

• A recurring design solution that is counterproductive (e.g., God Object, one class does too much).
• Example: God Object - one class doing everything.

Design smell:

• A weaker design indicator that may lead to anti-patterns if ignored (e.g., Divergent Change, one class frequently modified for unrelated reasons).
• Example: Divergent Change - one class frequently modified for unrelated reasons.

Relation: Smells are early indicators; if ignored, they crystallize into anti-patterns.

Example Scenario: A User class handling authentication, profile management, and notifications -> initially a smell (Divergent Change) -> eventually an anti-pattern (God Object).

16. How can improper error handling affect both design and code quality?

• Impact on Design: Poorly designed error handling (like swallowing exceptions silently) makes system behavior unpredictable and harder to debug.
• Impact on Code Quality: Leads to hidden bugs, inconsistent error states, and poor user experience.
• Proper design isolates error handling in a consistent layer, preserving modularity and clarity.

Example:

Bad: catch (Exception e) { System.out.println("Something went wrong"); }
Better: catch (DatabaseTimeoutException e) { log.error("DB timeout on query X", e); retryQuery(); }

17. How do you balance flexibility and simplicity in API design for long-term maintainability?

• Flexibility: Allows future enhancements without breaking existing clients (e.g., optional parameters, versioning).
• Simplicity: Keeps the API intuitive and easy to learn.
• Trade-off: Too much flexibility leads to bloated, inconsistent APIs; too much simplicity can limit adaptability.
• Best Practice: Start simple, evolve based on real needs, use versioning to support old clients.

Example: REST API for product search might allow optional filtering and sorting but avoid exposing every database query parameter to prevent over-complication.

18. How does “YAGNI” interact with software design quality?

• YAGNI (You Aren’t Gonna Need It): Avoid implementing features until they’re actually required.
• Positive Impact: Reduces complexity, keeps codebase lean.
• Negative Impact (if misapplied): Under-designing may require costly redesigns later.
• Balance: Anticipate likely needs but avoid speculative complexity.

Example: Adding an unused plugin system in a prototype wastes time and adds maintenance burden. Following YAGNI ensures design effort aligns with real needs.

19. Why is modularity sometimes at odds with performance optimization?

• Modularity: Breaks systems into independent components, improving maintainability.
• Conflict with Performance: More layers, indirections, and abstractions can slow execution.
• Note: Trade-offs require profiling to ensure performance-critical paths aren’t over-abstracted.

Example: Overuse of microservices in a high-frequency trading system may cause latency due to network calls, whereas a monolith could process in-memory faster.

20. How do code review practices influence both design and code quality in distributed teams?

• Design Quality: Early detection of architectural violations before code merges.
• Code Quality: Ensures adherence to coding standards, catches potential bugs, and promotes shared ownership of code.
• Team Impact: Promotes knowledge sharing and shared code ownership across geographies.
• Best Practice: Use structured review checklists (naming, readability, performance, design adherence).

Example: A reviewer spotting a service bypassing the caching layer prevents a scalability issue.