Master Thesis: Code-Graph-Based Context for AI-Assisted Software Engineering in Radio Baseband L1

Technology, Data & Digital · Software & Web Development · Software Engineering · Artificial Intelligence

In short

Master Thesis opportunity to explore code-graph-based context for AI-assisted software engineering in Radio Baseband L1. Analyze source code to build a queryable model for AI systems. Requires a Master's degree in Computer Science or related field, with skills in software architecture, graphs, Python, and C/C++. Based in Stockholm, Sweden.

Responsibilities

Study state of the art on code representation for AI.
Analyze Radio Baseband L1 components to identify key code entities and relationships.
Design and implement a method to extract a code graph and build domain context.
Define and measure 'effective context' including accuracy, coverage, freshness, and cost.
Evaluate the code-graph approach against alternatives on representative domain tasks.
Provide recommendations on maintaining context freshness and consistency.

Requirements

Pursuing a Master's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field.
Solid foundation in software architecture and data structures like graphs.
Experience with static analysis or compilers is ideal.
Comfortable working with Python.
Familiarity with C/C++ domain code.
Interest in program analysis, knowledge representation, and AI-assisted software engineering.
Analytical, systematic, and able to work independently.
Ability to communicate technical findings clearly and draw practical conclusions.

Benefits

Outstanding opportunity to use skills and imagination.
Work on challenging problems with a diverse team of innovators.
Suitable for 2 students.
Corresponds to 30 ECTS credits per student.

Skills

PythonC++GraphStatic analysisCompilersAIProgram analysisKnowledge representation

#Master Thesis#AI#Software Engineering#Code Graphs#Radio Baseband

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