Requirements management is a critical and time-consuming phase in the software development lifecycle, especially in highly regulated sectors such as automotive, where compliance with standards (e.g., ISO 26262, ASPICE) is essential. The complexity of modern systems leads to exponential growth of requirements, making tracking, verifying consistency, and maintaining traceability challenging. Automating these activities can reduce errors, improve efficiency, and enhance software quality. Machine Learning, Large Language Models (LLMs), and black-box approaches offer new opportunities to optimize requirements management, such as automatically linking system and software requirements or segmenting stakeholder requirements in tools like Jama and IBM DOORS.
This thesis aims to develop an innovative Python framework to address these challenges, contributing to the evolution of requirements management in the automotive sector.
Key Project Steps:
1. Study of problems, methodologies, and toolchain
2. Literature review and analysis of next best alternatives
3. Implementing an automatic linking system between system and software requirements
4. Developing an algorithm for automatic stakeholder requirement itemization
5. Integrating the framework with requirement management tools such as Jama and IBM DOORS
6. Evaluating system performance and comparing with traditional methods
Main topic:
Development in Python involving artificial intelligence algorithms.
Course of study and candidate requirements:
* Proficiency in Python and Machine Learning / NLP libraries (e.g., TensorFlow, PyTorch, scikit-learn)
* Alternatively, MATLAB knowledge and an optimization background
* Interest in software requirements management and automotive development methodologies
* Ability to analyze data and design AI-based systems
Location:
The internship can be carried out from one of the Kineton offices (Turin, Naples, or Reggio Emilia).
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