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Jenseits des Codes Rigorose Tests für ein zuverlässiges digitales Cockpit
Digital cockpit with multiple high-tech displays, showcasing the rigorous testing and validation processes essential for ensuring quality and safety.
High-tech digital cockpit undergoing rigorous software testing and validation to ensure reliability and safety.

In Kürze

  • Softwaretests und -validierung sind unverzichtbare Prozesse, um die Qualität, Sicherheit und Funktionalität des modernen digitalen Cockpits zu gewährleisten.
  • Strenge Testmethoden, einschließlich eines mehrschichtigen Ansatzes und fortschrittlicher Techniken wie Simulation, sind unerlässlich, um potenzielle Probleme zu erkennen und zu beheben, bevor sie auf die Straße gelangen.
  • Acsia bietet umfassende Test- und Validierungsexpertise, um Automobilherstellern zu helfen, außergewöhnliche digitale Cockpit-Erlebnisse zu liefern, denen die Fahrer vertrauen können.

Das digitale Cockpit, ein technologisches Wunderwerk, das das Erlebnis im Auto neu definiert, stützt sich in hohem Maße auf komplizierte Softwaresysteme. Auch wenn die Verlockung lebendiger Displays, intuitiver Schnittstellen und fortschrittlicher Funktionen unbestreitbar ist, hängt der Erfolg dieser Systeme von mehr als nur elegantem Code ab. Rigorose Softwaretests und -validierung sind die unbesungenen Helden, die dafür sorgen, dass das Cockpit nicht nur einwandfrei funktioniert, sondern auch die Sicherheit und Zufriedenheit seiner Benutzer in den Vordergrund stellt.

Software-Tests: Das Fundament eines zuverlässigen Cockpits

Betrachten Sie das digitale Cockpit als eine sorgfältig gefertigte Uhr mit zahlreichen Zahnrädern, Federn und Hebeln, die in perfekter Synchronisation arbeiten. Jede Komponente, sei es das Navigationssystem, das Unterhaltungsmodul oder die Schnittstelle für die Fahrzeugeinstellungen, ist ein Zahnrad in dieser komplizierten Maschine. Softwaretests fungieren wie die Lupe eines Uhrmachers, der jede Komponente und ihr Zusammenspiel genauestens untersucht, um sicherzustellen, dass sie sowohl einzeln als auch in ihrer Gesamtheit wie vorgesehen funktionieren.

Der facettenreiche Ansatz zum Testen

Das Testen des digitalen Cockpits ist kein einheitliches Unterfangen. Es erfordert einen vielschichtigen Ansatz, der verschiedene Methoden umfasst:

  • Unit-Tests: Auf der granularsten Ebene konzentrieren sich die Unit-Tests auf einzelne Softwarekomponenten. Das ist so, als würde man jedes Zahnrad der Uhr einzeln überprüfen, um sicherzustellen, dass es richtig funktioniert.
  • Integrationstests: Sobald die einzelnen Einheiten validiert sind, werden sie zusammengefügt. Die Integrationstests konzentrieren sich auf die Wechselwirkungen zwischen diesen Einheiten, so als ob Sie sicherstellen wollten, dass die Zahnräder einer Uhr reibungslos ineinander greifen.
  • Systemprüfung: Bei diesem umfassenden Ansatz wird das gesamte digitale Cockpit als ganzheitliches System untersucht. Es wird überprüft, ob alle Funktionalitäten, von der Navigation über die Unterhaltung bis hin zur Fahrzeugdiagnose, in realen Fahrszenarien harmonisch funktionieren.
  • Benutzerakzeptanztests (UAT): Das digitale Cockpit ist letztlich für menschliche Fahrer konzipiert. Beim UAT interagieren echte Benutzer mit dem System und geben wertvolles Feedback zur Benutzerfreundlichkeit, Intuitivität und allgemeinen Erfahrung.
  • Regressionstests: Nach Aktualisierungen oder Änderungen an der Software stellen Regressionstests sicher, dass neue Änderungen nicht versehentlich bestehende Funktionalitäten beschädigt haben. Dies ist entscheidend für die Aufrechterhaltung eines stabilen und zuverlässigen Cockpits während seines gesamten Lebenszyklus.

Fortgeschrittene Testwerkzeuge und -techniken

In der Automobilindustrie werden immer mehr fortschrittliche Tools und Techniken eingesetzt, um den Testprozess zu verbessern:

  • Simulation: Virtuelle Umgebungen, die reale Fahrbedingungen imitieren, ermöglichen es den Ingenieuren, die Reaktionen des Cockpits auf verschiedene Szenarien zu testen, ohne dass ein echtes Auto benötigt wird. Dies beschleunigt die Entwicklung, senkt die Kosten und ermöglicht die Bewertung extremer Bedingungen, die auf der Straße nur schwer nachgestellt werden können.
  • Hardware-in-the-Loop (HIL)-Tests: Hierbei wird die tatsächliche Cockpit-Hardware (Displays, Tasten usw.) mit einer simulierten Umgebung verbunden. So kann die Interaktion des Systems mit physischen Komponenten in einer kontrollierten Umgebung getestet werden.
  • Injektion von Fehlern: Durch die absichtliche Einbringung von Fehlern in das System können die Ingenieure dessen Widerstandsfähigkeit bewerten und sicherstellen, dass die Sicherheitsmechanismen wie erwartet reagieren.

Es steht viel auf dem Spiel: Warum gründliche Tests so wichtig sind

Die Folgen von Softwarefehlern in einem digitalen Cockpit sind erheblich:

  • Sicherheitsrisiken: Eine Fehlfunktion des Cockpits kann zu falschen Datenanzeigen, verzögerten Warnungen oder sogar zu unbeabsichtigtem Verhalten des Fahrzeugs führen, was die Sicherheit der Insassen und anderer Verkehrsteilnehmer gefährden kann.
  • Unzufriedenheit der Benutzer: Pannen, Abstürze oder verwirrende Benutzeroberflächen führen zu Frustration und untergraben das Vertrauen in die Marke, was zu negativen Bewertungen führt und sich möglicherweise auf zukünftige Verkäufe auswirkt.
  • Finanzielle Belastung: Die Behebung von Softwarefehlern in einer späten Phase des Entwicklungszyklus ist exponentiell teurer als das frühzeitige Erkennen von Fehlern.

Acsia: Ihr Partner für Tests und Validierung

Wir bei Acsia haben uns verpflichtet, digitale Cockpit-Lösungen zu liefern, bei denen Sicherheit, Zuverlässigkeit und Benutzerfreundlichkeit im Vordergrund stehen. Unsere erfahrenen Ingenieure folgen einem akribischen Testprozess, der die besten Praktiken der Branche und modernste Tools einbezieht, um sicherzustellen, dass Ihre Cockpitsysteme den höchsten Standards entsprechen.

Wir erstellen maßgeschneiderte Testpläne für Ihre spezifische Konfiguration, die eine breite Palette von Testszenarien abdecken. Unsere Erfahrung mit Simulations- und Emulationsumgebungen ermöglicht ein effizientes und umfassendes Testen. Mit Acsia als Partner können Sie sicher sein, dass Ihr digitales Cockpit auf einem Fundament aus strengen Tests und unerschütterlicher Qualität aufgebaut ist.

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AH2025/PS06 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

 

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

 

Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

 

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

 

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

 

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS05 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS04 | AI/ML

Context

Software teams struggle to diagnose system failures from massive log files. Manual analysis is slow, error-prone, and requires expert knowledge. Root cause extraction from unstructured, noisy logs. Use creative algorithms, LLM prompting strategies, or hybrid heuristics.

Pain Point

  • Manual log analysis is slow, error-prone, and requires deep expertise in both the system and its environment.
  • Critical issues can be missed or misdiagnosed, leading to longer downtimes and higher costs.
  • Existing monitoring tools often raise alerts without actionable insights, leaving developers to do the heavy lifting.

Challenge

Build an AI-powered log analytics assistant that can:

  • Ingest and parse unstructured application logs at scale.
  • Automatically flag potential defects or anomalies.
  • Summarize possible root causes in natural language.
  • Provide actionable insights that developers can use immediately.

Goal

Deliver a working prototype that:

  • Operates on sample log data.
  • Produces insights that are accurate, usable, and easy to interpret.
  • Bridges the gap between raw log data and developer-friendly diagnostics.

Outputs

  • Automated defect detection (flagging anomalies in logs).
  • Root cause summaries in natural language.
  • Actionable recommendations (e.g., suspected component failure, probable misconfiguration).
  • Visualization/dashboard (if possible) for quick triage.

Impact

  • Reduced time to diagnose failures, lowering downtime and maintenance costs.
  • Increased developer productivity, freeing engineers to focus on fixes rather than sifting logs.
  • Improved reliability of complex software systems.
  • Scalable approach that can be extended across industries (finance, automotive, telecom, healthcare).
AH2025/PS03 | AI/ML

Context

Drivers and passengers spend significant time in vehicles where comfort, safety, and accessibility directly affect satisfaction and well-being. Yet today’s in-car systems remain largely static and manual, requiring users to adjust climate, seats, infotainment, and navigation themselves. With increasing connectivity, AI offers the potential to transform cars into adaptive, intelligent companions.

Pain Point

  • Current in-car experiences are one-size-fits-all, failing to account for individual preferences or needs.
  • Manual adjustments while driving can be distracting and unsafe.
  • Accessibility gaps (e.g., for elderly passengers or those with hearing/visual impairments) remain unaddressed.

Challenge

Build a Generative AI-powered cockpit agent that dynamically personalizes the in-car experience based on contextual data such as:

  • Driver profile (age, preferences, past behaviour).
  • Calendar & journey type (work commute, leisure trip, urgent travel).
  • Mood (estimated from inputs like speech, facial cues, or self-reporting).
  • Accessibility needs (visual/hearing impairments, elderly passengers).

Goal

Deliver real-time, adaptive personalization of:

  • Comfort settings: AC, seat adjustments, lighting.
  • Infotainment: music, podcasts, news.
  • Navigation guidance: route optimization based on urgency, preferences, and accessibility.

Outputs

  • Dynamic in-car assistant that responds to context in real-time.
  • Personalized environment settings for comfort and safety.
  • Adaptive infotainment & navigation suggestions tailored to mood, journey type, and accessibility.

Impact

  • Safer driving experience with fewer distractions.
  • Higher passenger satisfaction through comfort and entertainment personalization.
  • Improved accessibility and inclusivity for diverse user needs.
  • New value proposition for automakers: cars as intelligent, personalized environments, not just vehicles.
AH2025/PS02 | AI/ML

Context

Automotive software development is highly complex, involving multiple tools (Jira, GitHub, MS Teams, Confluence), distributed teams, and strict compliance standards (ISO 26262, ASPICE). Project managers must continuously monitor tasks, track resources, and identify risks. However, the sheer volume of data across tools makes real-time visibility and decision-making difficult.

Pain Point

  • Project managers waste time manually consolidating data from Jira, GitHub, and communication platforms.
  • Resource allocation bottlenecks (overloaded developers, idle testers) often go unnoticed.
  • Risks (delays, defects, dependency issues) are only discovered late, impacting delivery timelines.
  • Lack of predictive insights leads to reactive, rather than proactive, project management.

Challenge

Build an AI-powered project management assistant that can:

  • Auto-generate project dashboards by integrating Jira, GitHub, and MS Teams data.
  • Provide real-time resource allocation insights (who is overloaded, who is free).
  • Predict risks and delays using historical patterns and live progress signals.
  • Deliver natural language summaries for managers and stakeholders.

Goal

Enable project managers to see the full picture instantly, automate reporting, and take data-driven decisions on resources and risks without manual effort.

Outputs

  • Automated project dashboards (progress, backlog, velocity, open PRs/issues).
  • Resource allocation map showing workload distribution across the team.
  • Risk prediction engine (e.g., “Module X likely delayed by 2 weeks due to dependency on Y”).
  • AI-generated summaries (daily/weekly status reports in plain language).

Impact

  • Reduced management overhead → fewer hours wasted on reporting.
  • Improved predictability → early identification of risks and delays.
  • Optimal resource utilization → balanced workloads across teams.
  • Better stakeholder communication → clear, automated updates.
  • Scalable for enterprises → can be deployed across multiple automotive software teams.
AH2025/PS01 | AI/ML

Context

In modern organizations, assembling the right project team is critical to success. Managers must balance skills, experience, cost, availability, and domain expertise, but decisions are often made using intuition or partial information. This leads to suboptimal teams, missed deadlines, or budget overruns.

Pain Point

  • Team formation today is time-consuming and heavily manual, requiring managers to cross-check spreadsheets, HR databases, and project needs.
  • Costs and expertise trade-offs are rarely quantified, making it hard to justify team composition to leadership or clients.
  • Traditional staffing tools focus on availability but fail to optimize across multi-dimensional constraints (skills, budget, past project fit, timeline).

Challenge

Build a Generative AI assistant that takes as input:

  • Employee database (skills, past projects, availability, cost)
  • Customer project requirements (tech stack, timeline, budget, domain)

Goal

Enable managers to form the best-fit, economically feasible project teams in minutes, rather than days, while providing transparency into why each recommendation was made.

Outputs

  • Optimal team composition: Recommended employees, with justification.
  • Economic feasibility analysis: Skill coverage vs cost vs timeline.
  • Alternative team recommendations: Trade-off scenarios (e.g., lower cost, faster delivery, more experienced).

Impact

  • Faster project staffing → quicker project kick-offs.
  • Higher client satisfaction due to right skills on the right project.
  • Lower staffing costs through data-driven optimization.
  • A scalable framework that can be extended for hackathons, consulting firms, or large enterprise project staffing.