Technical Due Diligence Checklist: What to Evaluate Before You Acquire a Codebase

A technical due diligence checklist is the list of things you examine — software architecture, code quality, infrastructure, cybersecurity, compliance and the development process — before acquiring a company or investing in its technology. Hidden problems in a target codebase can derail an acquisition and inflate integration costs: undocumented dependencies and accumulated technical debt routinely turn a clean-looking deal into months of unplanned remediation.

This due diligence guide walks through the full technical due diligence process, section by section, ending with what belongs in the due diligence report. SWARECO runs technical due diligence engagements for acquirers and investors, and the checklist below is the structure our assessors actually work from — including the part most templates still miss: what to check about AI-generated code in a codebase you are about to inherit.

Key Takeaways

• Technical due diligence exists to uncover what you would otherwise inherit blind: undocumented dependencies, fragile legacy code, technical debt and unpatched vulnerabilities. Found early, each is a negotiating point; found after close, each is your remediation budget.
• The evaluation starts with software architecture: modularity, scalability, cloud-native patterns such as Docker and Kubernetes, and whether availability, latency and fault isolation are measured against KPIs at all.
• Cybersecurity due diligence covers incident response plans, penetration testing results, encryption standards, access controls (IAM/MFA), SOC 2 readiness, vulnerability scans (SAST/DAST), data residency, and regulatory compliance evidence for frameworks such as GDPR and HIPAA.
• AI-generated code needs its own line items now: review coverage for AI-assisted contributions, code provenance, license exposure from model outputs, and whether generated code silently violates schema and transaction constraints.
• The deliverable is a due diligence report with a prioritized remediation roadmap with cost and timeline estimates — findings converted into deal terms, not a pile of observations.

The technical due diligence process: scope, timeline, red flags to watch

Tech due diligence is a structured review of the target company's technology assets: code and infrastructure, the technology stack, the technical team, and the due diligence documents that prove how it is all run. A comprehensive technical due diligence engagement typically takes 2 to 4 weeks, depending on codebase size and how quickly the data room grants access to systems.

The due diligence framework below is organized the way the work actually proceeds: architecture and code quality first, then infrastructure, then cybersecurity and regulatory compliance, then the development process itself. At each stage the question is the same — what would the acquirer inherit, what would it cost to fix, and is any of it a reason to reprice or walk away?

Red flags to watch for across every section: documentation that exists only in one engineer's head, environments nobody can rebuild, dependencies with no owner, and — increasingly — large volumes of AI-generated code that no human has reviewed.

Software Architecture and Code Quality

Start with the structure of the system, because everything else inherits from it. Review the architecture diagrams against the running system, evaluate design choices such as microservices versus a monolith, and check whether the programming patterns support change or resist it.

System design and scalability

Check that the software architecture has clear documentation and modular boundaries — can the technical team change or extend one service without a large rewrite? Evaluate scalability and headroom: how the system handles load spikes, and how much capacity remains before the first bottleneck appears.

A technology due diligence checklist should cover cloud-native patterns, containerization with Docker and Kubernetes, serverless functions, and any planned migration path from monolith to microservices. Measure availability, latency and fault isolation, and inspect APIs, integration points and the technology roadmap for future growth.

Deployment pipelines and DevOps automation belong here too, because they set the time-to-market the valuation assumes. Tie scalability findings to valuation drivers and KPIs so the deal team can make informed decisions rather than reading an architecture review in isolation.

Assessment of technical debt

Measure technical debt by locating the legacy code the business actually depends on, then asking how much of it is documented, tested and owned. Undocumented or fragile code is where remediation budgets go to die, so distinguish necessary tech debt (deliberate shortcuts with a payoff plan) from the avoidable kind (nobody noticed until now).

Two places deserve special attention. First, deployment pipelines and automation scripts — debt there multiplies the cost of every future change. Second, AI-authored modules: hidden technical debt concentrates in generated code that was accepted quickly and reviewed lightly, and it will not show up in a conventional static-analysis pass calibrated for human-written code.

Adherence to coding standards

Verify that coding standards are enforced by machinery, not memos: linters, static analyzers and a formal code review process that actually gates merges. Where the stack includes machine learning components — Python, TensorFlow, PyTorch — check them against the same engineering best practices as the rest of the codebase, and do the same for mobile code on React Native, Android and iOS.

Ask for the policy on AI coding tools. If there is none, assume unvetted AI-assisted contributions are present and price the review debt accordingly.

AI-generated code: the checklist items most templates miss

Every codebase you evaluate from now on contains AI-generated code; the only question is whether anyone governs it. Add these items to the due diligence checklist:

• Review coverage. Do AI-assisted contributions go through the same code review gate as human commits, or are they merged on the strength of "it compiles"? Generated code that passes tests can still encode wrong assumptions.
• Schema and constraint violations. AI coding assistants cannot see runtime schema properties — nullability, foreign keys, transaction boundaries — so generated code tends to violate them silently. Sample generated modules and check them against the real schema.
• Debt concentration. Map where AI-authored code clusters. Modules written quickly during a crunch are where hidden technical debt concentrates.
• Provenance and licensing. Audit code provenance: commit histories, dependency manifests, SBOM and license scans. Model outputs that reproduce licensed code are an IP exposure the seller's warranties may not cover.
• Policy. A written AI coding-tool usage policy, with enforcement evidence, is now a maturity signal on par with CI/CD. Its absence is a finding.

IT Infrastructure and Systems

Evaluate both cloud and on-premise setups, including data centers where they exist. The goal is to uncover migration, scaling and operational risks before they become integration surprises.

Cloud and on-premise infrastructure review

Reliability and performance analysis

Validate that application logs are centralized and searchable, that synthetic monitors exercise the core user flows, and that performance baselines exist for the loads the business plan assumes. Then look at headroom: what happens at twice today's traffic, and who finds out first — the monitoring stack or the customers?

Quality assurance maturity belongs in this section: the ratio of automated to manual testing is a fast, honest proxy for how safely the target company can ship changes after the acquisition closes.

Cybersecurity and Compliance

Cybersecurity due diligence answers two questions: how likely is a data breach, and what would one cost? Regulatory compliance answers a third: what fines and legal exposure does the target already carry?

Vulnerability and risk assessment

• Evaluate incident response plans — do they name roles and procedures, and have they ever been exercised against a real or simulated breach?
• Analyze penetration testing results and prioritize remediation by severity and exploitability, not by how easy each fix is.
• Inspect access controls and credential management: password policies, service-account rotation, encryption standards, and whether anything sensitive travels unencrypted.
• Run or review SAST and DAST scans across the code and infrastructure, and check the patching cadence against CVE disclosure dates.
• Review intrusion detection coverage and where its alerts actually go.
• Scrutinize AI-generated code for unvetted contributions in security-sensitive paths — authentication, authorization and payment code written by an assistant and merged without adversarial review is a real and current failure mode.
• Work alongside legal advisors on anything touching regulatory exposure, and document every finding in the technical due diligence report so stakeholders see the red flags before signing, not after.

Compliance with relevant standards and regulations

Development Processes and Tools

How software gets built predicts how it will behave after the deal. Assess the software development lifecycle for maturity, and evaluate how the technology team setup uses automation, because that is what determines whether post-close plans ship on schedule.

Software development lifecycle evaluation

Look at deployment frequency and branch workflow first — they are the fastest honest indicators of engineering agility. Then check the QA automation ratio, release rollback capability, and whether documentation keeps pace with the code or trails it by quarters.

What you want to see is a clear and cohesive strategy and roadmap connecting the SDLC to business goals. What you often find instead is a process that works only because two specific people are still employed — which is a retention clause waiting to be written.

DevOps practices and automation

• CI/CD: automated integration and deployment through tools like Jenkins or GitHub Actions, with merge gates that actually block on failures.
• Infrastructure as code: environments managed from version-controlled templates, so provisioning a new environment is a workflow, not a project.
• Monitoring and logging: performance tracking and incident management wired to alerting, reducing both downtime and data-breach dwell time.
• Containerization: Docker images and Kubernetes orchestration that make deployments reproducible across environments.
• Incident management: automated escalation with post-incident reviews that produce fixes, not documents.
• DevOps maturity trend: whether the practice is improving, static, or eroding — direction matters more than the snapshot in a merger and acquisition context.

The due diligence report: turning findings into decisions

The output of the technical due diligence process is a due diligence report the deal team can act on. A useful one contains an executive summary for PE and corporate buyers, a risk assessment with every finding scored by severity and exploitability, and a prioritized remediation roadmap with cost and timeline estimates aligned to the deal calendar.

Every claim in it should be reviewed and validated against evidence from the data room — architecture diagrams, scan reports, CI records, access logs — so that findings survive challenge during negotiation. That is what makes the difference between a report that moves the price and one that gets filed.

Conclusion

A technical due diligence checklist works when it is systematic: software architecture and code quality, infrastructure, cybersecurity and compliance, development process, and a report that converts findings into deal terms. Run all of it, every time — the sections you skip are reliably where the expensive surprises live.

And update the checklist for the codebase you are actually buying in 2026: one where part of the code was written by AI tools, under policies that may not exist. Evaluating that code — its review coverage, its provenance, its silent constraint violations — is no longer an optional appendix. It is the newest section of the core checklist, and the one your target's last diligence never covered.

FAQs

1. What is a technology due diligence checklist and why use it in a merger and acquisition?

A technology due diligence checklist is the structured list of technical aspects an investor or acquirer evaluates before a merger and acquisition: architecture, code, infrastructure, security, compliance and team. Using one keeps the tech due diligence complete and comparable across deals, so nothing material is skipped because time ran short.

2. What technical aspects should I evaluate when I review a codebase and infrastructure?

Review the technology stack, the code and infrastructure together, data centers and cloud accounts, disaster recovery plans, and access to systems. Check the SDLC artifacts and the contents of the data room, and confirm the technology team setup — who actually keeps this running, and are they staying?

3. How do you assess the technical health and technical risks of the target codebase?

To assess the technical health, run static analysis, review tech debt, and perform penetration testing alongside broader security testing. Include cybersecurity due diligence covering vulnerabilities, security controls and intrusion detection. This is what finds the technical risks and gaps before you inherit the systems that carry them.

4. What due diligence documents and KPIs should I request from the seller?

Request the due diligence documents that show KPIs, governance and confidentiality controls, plus any existing fines and legal exposure, and cost and timeline estimates for known remediation. Ask for audit logs, code review records, service-level data and compliance evidence — documents that prove practice, not policy.

5. How does due diligence work to mitigate risks when acquiring a company?

Due diligence works to mitigate risks by identifying exactly what you will inherit: testing access to systems, validating disaster recovery, mapping which of the technical team stays, and pricing remediation. The findings feed strategic decisions, so acquirers negotiate from evidence instead of assumptions.

6. What practical steps, scope and schedule should a comprehensive technical due diligence follow?

Use a phased approach: collect files in a data room, run code and infrastructure reviews, verify the technology stack, perform penetration testing, evaluate tech debt, and review the development lifecycle records. Most engagements take 2 to 4 weeks. Finish with a due diligence report that states residual technical risks and recommended next steps.