AI in Software Development

Real-world gains, hard limits, and how teams ship faster

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Publication date: 21.08.2025

AI in software development works best when the work is clear and testable. The benefits show up in unit testing, typical technical tasks, faster code comprehension, and initial bug localisation. It is less helpful with complex business logic, greenfield integration tests, and anything that crosses many system boundaries. The message is simple. Treat the AI coding assistant as a capable helper. Let engineers make the decisions.

“AI is a strong assistant in experienced hands. It speeds up repeatable work and leaves judgement to people.”

Boosting developer productivity with AI

Developer productivity with AI improves when the goal is precise. Ask for a mapper, a DTO, or a unit test for a single class. Keep the scope small. Review the output with the same care you give to human code. In these conditions the gains are obvious.

What most teams see in practice

Faster understanding of existing code. Use an IDE AI assistant to outline flows, map dependencies, and create brief summaries. Many engineers report 30% to 40% faster onboarding to a microservice or module they have not touched before.
Quicker delivery of typical technical tasks. Adapters, mappers, DTOs, small validators, common response handling, and routine client integrations respond well to AI-assisted development. Cycle time often drops by 60% to 80% when the spec is clear.
Sharper triage during incidents. Given a stack trace and logs within one service, the assistant can highlight likely failure points. The time saved on each investigation is small, yet it compounds over a sprint. The average net improvement sits near 10%.
Lower cognitive load. The AI coding assistant drafts and explains. Engineers spend more time on design choices and less time on boilerplate.

Where the limits appear

Complex business logic. Multi-step flows with compliance, edge cases, and state transitions still need human design. AI can suggest steps. It often misses domain constraints.
Integration tests from scratch. Containers, stubs, secrets, network quirks, and orchestration call for careful setup. AI can draft scaffolding. Engineers finish and harden it.
Unreviewed code quality. Without review and static analysis it is easy to accept code that references methods or fields that do not exist. Guardrails are essential.

A concise view of the patterns we observe

Task category	Outcome with an AI coding assistant	Indicative acceleration
Unit tests for focused classes	Useful drafts in minutes	50–90%
Typical technical tasks	High-quality scaffolds that need tweaks	Up to 80%
Understanding legacy code with AI	Faster mapping of flows and structure	30–40%
Bug localisation inside one service	Faster triage from logs and stack traces	About 10%
Business features end to end	Mixed and scope dependent	5–25%
Integration tests from scratch	Inconsistent and needs human orchestration	Low or variable

“If two or three precise attempts do not land, switch to manual. Time saved beats time hoped for.”

IDE AI assistant for developers and typical technical tasks

In daily work the speedups come from less friction. An IDE AI assistant for developers keeps momentum high when it removes micro-delays and avoids new ones.

Practical wins you can repeat

Boilerplate that does not drain energy. Repositories, REST clients, mapping layers, validation rules, and DTOs are good candidates. Provide the schema and the contract. AI-powered coding produces a solid first cut.
Safer refactors with a plan. Ask the assistant to propose steps and update call sites. You approve changes. The tool explains its moves so you can verify.
Inline explanations and quick notes. When you scan an unknown service, use understanding legacy code with AI to summarise method intent, data flow, and side effects. Small explanations reduce context switching.
Query scaffolds and schema awareness. For routine queries the assistant drafts joins and filters. You tune performance and correctness.

House rules that avoid churn

Keep prompts small and concrete. A tight request gets a tight answer. It is easier to check and merge.
Anchor on agreed patterns. Mention the house style, error policy, and naming. Consistency keeps maintenance cheap.
Review with intent. Run linters and static analysis. Confirm types and imports exist. Check error handling and logging paths.

Generate unit tests with AI and reduce lead time

AI test generation is the most reliable use case. The best results arrive when the class under test has clear contracts and limited dependencies. You can move from zero to useful coverage in minutes.

What to ask for

Targeted unit tests. Focus on a single service or utility. Mock external calls with care. Describe edge conditions. The assistant produces arrange, act, and assert blocks that are easy to run and adjust.
Explicit coverage goals. State branches, error paths, and boundary values. A prompt such as generate unit tests with AI for currency rounding, negative amounts, and overflow leads to focused checks that matter.
Tight feedback loops. Run tests. Paste failing output. Ask the assistant to patch expectations or mocks. The back and forth is quick.

What to watch for

Phantom methods and imports. Validate early in the IDE. Let CI confirm. Remove anything that is not real.
Over-mocking that hides behaviour. Keep unit tests crisp. Do not mock away the logic you need to verify.
Integration hiding in unit clothes. If a test starts containers or crosses a boundary, treat it as integration level. Manage it with a harness and fixtures.

Helpful prompt patterns

Generate unit tests with AI for PaymentCalculator. Include boundary checks for exchange rates, rounding and overflow. Use JUnit 5 and AssertJ. Mock only external clients.
Add negative-path tests for null inputs and invalid currency codes. Maintain naming and style from the sample test.

Debugging and bug localisation using AI

Fast localisation beats guesswork. When a defect shows up, an AI coding assistant can check logs, mark suspicious paths, and suggest possible fixes.

How to use it well

Share the exact stack trace and the relevant log block. Include the version, the feature flag state, and a short note on recent changes.
Ask for hypotheses that you can test. A ranked list is easy to validate. It keeps you moving.
Stay inside the service when possible. The assistant is most effective when the issue is in the codebase or its direct dependencies.

Limits to respect

External integrations and opaque platforms. If the cause sits across a boundary, visibility is weak. You still gain ideas. You do not get certainty.
Non-functional issues. Performance, timeouts under load, race conditions, and memory pressure require profiling and instrumentation.

Few successful projects of WislaCode

AI test generation with an AI coding assistant

AI test generation delivers the highest return at unit scope. For integration scenarios pair the assistant with a human-designed harness. Treat it as a scaffold that you refine.

A practical operating model that keeps teams steady

Define scope up front. State unit, component, integration, or end to end. Keep the request tight.
Pin the environment. For integration tests describe containers, fixtures, and data contracts. Ask the assistant to draft the setup. Do not let it guess.
Codify acceptance criteria. State the assertions that must pass. Avoid quiet happy-path checks.
Reuse scaffolds. When you receive a clean fixture or harness, keep it as a template for adjacent modules.

Quick checks for engineering leads

Do we keep a small library of tested prompts and snippets for AI in software development?
Are static analysis and code review mandatory for AI-generated changes?
Do we track coverage improvements by module to confirm real value?
Do we follow the two to three attempts rule to avoid sunk costs?

A simple view of where AI helps in testing

Testing layer	AI suitability	Typical use	Notes
Unit tests	High	Edge cases, negative paths, contract checks	Fast cycles and strong return
Component or service	Medium	Mocks and stubs, contract validation	Needs curated fixtures and contracts
Integration tests	Low to medium	Harness scaffolds and basic wiring	Human-led environment and orchestration
End to end	Low	Scenario outlines and living documentation	Better as docs than code

“AI speeds up the boring parts of testing. Engineers decide what matters. The assistant turns intent into checks.”

Practical dos and do nots

Do keep the test style guide in your prompt. Name the framework, the assertions library, and the structure.
Do ask for data-driven tests if the domain allows it. They generalise and extend well.
Do request explicit assertions for error handling, retries, and timeouts. Avoid hidden assumptions.
Do not let generated tests lock in brittle behaviour. Prefer contracts over implementation details.
Do document any deviations from house style. Future maintainers will thank you.

Best practices for prompting LLMs in development

State the role and the context. Mention the language, the framework, and any constraints such as thread safety or idempotency.
Provide a minimal code sample and the expected outcome. Short examples beat long descriptions.
Ask for a short explanation next to the code. One sentence is often enough and helps reviewers.
Limit the number of goals per prompt. One clear goal wins.

Security, privacy, and audit in regulated teams

Remove secrets and personal data before sharing code. Use redaction and synthetic data in prompts. Follow approved sandboxes.
Log when AI-generated code enters the codebase. Keep diffs and link to reviews so you have traceability.
Expect variance in output over time. Standardise prompts and templates. Keep humans in the loop for consistent outcomes.

AI in software development is a practical tool. Use it for unit tests, typical technical tasks, and quick code comprehension. Keep humans in charge for complex business logic and integration setup. Follow small prompts, strong reviews, and the two to three attempts rule. That is how developer productivity with AI translates into predictable delivery and reliable systems.

About WislaCode Solutions

WislaCode Solutions builds next‑generation fintech software. We deliver mobile and web applications with full-stack capability. Our teams cover data storage, backend, middleware, frontend architecture, design, and development. Looking to use an AI coding assistant, enhance AI test generation, or boost developer productivity in a regulated environment? We can help you plan a careful rollout that meets your goals.

FAQ About AI in Software Development

Where does AI add the most value in a typical development workflow?

AI in software development shines where work is clear and testable. Teams gain most in unit testing, small refactors and routine integrations. An IDE AI assistant for developers can draft DTOs, mappers and repositories, then help verify call sites and error paths. AI-powered coding helps you understand code faster. It outlines flows and data shapes in new modules. For incidents, it helps with debugging and bug localisation using AI when the fault is inside one service and you have logs and a stack trace. Limits appear with complex business rules, greenfield integration tests and multi system changes. Treat the tool as a helper. Keep people in charge of design, review and acceptance to protect developer productivity with AI.

How should we use an IDE AI assistant for everyday technical tasks?

Use the IDE AI assistant for developers to remove friction. Keep requests small and concrete. Ask for a mapper between known schemas or a repository interface that follows your rules. Provide a short example and name the framework and version. The assistant can propose safe refactors with clear steps you review and run. For scaffolding, AI-assisted development is reliable when you supply contracts and acceptance criteria. It can draft REST clients, mapping layers and basic tests that you refine. Avoid prompts that ask for full features. Those invite guesswork. These AI developer tools shorten cycle time, reduce context switching, and maintain quality. You can focus on what to ship.

How can we generate unit tests with AI and keep them reliable?

Start small. Generate unit tests with AI for a single class or service with clear inputs and outputs. Name the test framework and assertions library. List boundary values, negative paths and error cases. Ask for data driven tests where suitable. Run the tests and share failures so the assistant can adjust expectations or mocks. Watch for phantom methods and imports. Validate early in the IDE and confirm in CI. Avoid over-mocking that hides logic. If a test crosses a boundary or spins containers, treat it as integration and design a proper harness. Used this way, AI test generation reduces lead time and lifts coverage without trading away trust.

Can AI help us understand legacy code without slowing the team down?

Yes. Understanding legacy code with AI reduces onboarding time and cuts noise. Ask for entry points, request flow and key dependencies in a short summary. Request a map of domain objects, tables and relationships. Paste a focused class or controller and ask for intent, side effects and error paths. You can also request likely risks before a refactor, such as hidden coupling. Redact secrets and personal data. Use synthetic examples where possible. Confirm inferred behaviour by reading critical paths and running tests. With peer review, this turns scattered knowledge into useful notes. It also boosts developer productivity by using AI to cut down on time spent searching through files.

How useful is AI for debugging and bug localisation during incidents?

AI-assisted development helps when the fault sits inside one service. Share the exact stack trace, a small block of relevant logs and what changed. Ask for top hypotheses and files to inspect first. The assistant can flag brittle parsing, missing null checks and suspicious paths. When a fix is likely, request a minimal patch and a short explanation. Draft a focused test to lock in the expected behaviour. Be mindful of limits. Issues across service boundaries or with external providers are harder. Non functional defects under load need profiling. Even then, the tool offers useful prompts for instrumentation. Treated as a partner, AI in software development reduces time to diagnose.

What are the best practices for prompting LLMs in development work?

Keep prompts precise and grounded. State the language, framework and version. Include a minimal code sample and the outcome you want. Limit each request to one goal. Ask the output to follow your naming and style rules. For code edits, request a plan first and approve steps. For AI test generation, list edge cases and failure paths that must be asserted. During debugging, provide the exact stack trace and ask for ranked hypotheses. Maintain a small library of proven prompts for your stack. Adopt a two to three attempts rule. If useful results do not appear after a few tries, switch to manual. With these habits, AI-powered coding stays predictable and useful.