Software lifecycle · AI

Application Lifecycle Management with AI

Secure, efficient, scalable – along the entire software lifecycle. From copilot-assisted development and AI in software testing to automated QA and security, with your own LLMs on request.

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Claude Code

as a Claude Certified Services Partner

GitHub Copilot

copilots in everyday development

Local LLMs

code and data stay in-house

ISO 27001

certified information security

Why now

Why application lifecycle management with AI matters now

Software development is becoming ever more complex: shorter release cycles, rising demands on quality and security and a growing shortage of skilled staff are increasing the pressure on IT teams. Classic application lifecycle management often reaches its limits.

Artificial intelligence already offers concrete support today: from requirements management with LLMs and code completion with copilots to smart test scenarios and security checks. We support you on this path – technology-agnostic, practical and along your individual lifecycle.

KI-gestützte Entwicklung: Code-Editor mit Live-Vorschau und Entwicklerkonsole

Faster time to market

Automated processes shorten the time to release.

Higher quality

More precise tests and fewer manual errors.

More security

Integrated AI checks in deployment.

Save resources

Targeted use of LLMs and copilots.

Lifecycle

AI along the software lifecycle – combined with your own LLMs

Five phases in which AI already delivers measurable help today – each with the added value of your own in-house LLM.

01 · Planning & architecture

AI-assisted requirements analysis, stakeholder summaries. Your own LLM: analysis of confidential business documents.

02 · Development

Code generation with copilots such as Claude Code or GitHub Copilot, consistent coding style. Your own LLM: trained on your coding style.

03 · Test automation

Test case generation from requirements, detecting coverage gaps. Your own LLM: generate tests from Jira or Confluence descriptions.

04 · Deployment & operations

Automated monitoring, AI-assisted error analysis from log data. Your own LLM: context-aware root cause debugging.

05 · Security & compliance

Code analysis for vulnerabilities, policy checks against company-specific rules. Your own LLM: checks against company-specific GDPR standards.

Tools

360° ALM integration with AI – concrete and practical

From the test idea to error-free delivery – how to use AI features and leading tools in a targeted way.

Test case generation

Automatically derive test cases from requirements and Jira tickets with GPT, Tosca AI or TestBrain.ai.

Visual testing

Applitools Eyes or Eggplant use AI to detect even small visual deviations in UIs – even without DOM access.

Self-healing

Tosca or Testim.io automatically adapt test scripts to UI changes – ideal for agile UI development.

Unit test automation

Diffblue Cover automatically generates JUnit tests for Java – ideal for legacy codebases without test coverage.

Error analysis

LLMs analyse logs and test results and uncover patterns and causes in bug data.

Test prioritisation

Historical data and machine learning produce a targeted test order – supported by tools such as aqua and Xray.

Your own LLMs

Use AI without giving away your code and data

For high security or data protection requirements, we integrate local LLMs – for example via Ollama. Your teams benefit from AI while source code and confidential documents stay in-house.

  • Local LLMs, AI hosted in Germany or an environment hosted by cimt
  • GDPR-compliant, with a role and permission concept
  • Human-in-the-loop for code reviews and approvals
  • Traceable and compatible with your ISMS – cimt is ISO 27001 certified

White paper: AI in the software testing process

Our exclusive white paper shows in practical terms how AI increases quality, efficiency and test coverage along the software lifecycle – from test generation and visual testing to error analysis. The white paper is available in German.

  • Intelligent test case generation with LLMs
  • Visual testing with Applitools & Eggplant
  • Self-healing tests in agile pipelines
  • Analysis of bug data & test gaps
  • Tool overview & recommendations for Java rich clients

Get the white paper now

FAQ

Frequently asked questions about ALM with AI

ALM with AI means using artificial intelligence and LLMs along the entire software lifecycle – from planning and development to testing, quality assurance and secure delivery. The goal is shorter release cycles, higher quality and more security.
We combine copilots such as GitHub Copilot and Claude Code with LLMs for requirements and test case generation, as well as specialised tools for visual testing (e.g. Applitools, Eggplant). The selection depends on your stack and your security requirements.
Yes. For high security or data protection requirements, we integrate local LLMs – for example via Ollama – so that code and data stay in-house.
Through intelligent test case generation, visual testing, self-healing tests in agile pipelines and the analysis of bug data and test gaps. This increases test coverage with significantly less manual effort.
For teams with short release cycles, high quality and security requirements and limited resources who want to use AI in a targeted, governed and traceable way – not as a playground, but in the productive process.
The easiest way is via our white paper or a free initial call. We look at your lifecycle and show concrete approaches where AI delivers the biggest leverage.

💬 Arrange a personal call

Talk to our experts about AI in application lifecycle management. We will call you back promptly – with no obligation.

  • ✅ Individual advice for your team
  • ✅ Practical examples & the right tools
  • ✅ AI solutions for development & testing

📞 Call-back service

Contact

Your contact for ALM with AI

Michael Schwarz, cimt
Michael SchwarzHead of Business Enablement & Customer Relations

Note: This text was created with AI assistance.

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