The context is sustainbable software development. We try to find coding alternatives that are economical with regard to energy (electricity) consumption.
Indoor service robots carry medication, lab samples, and supplies through hospital corridors and move goods through warehouse aisles. They share this space with people who are not trained operators and who are often not paying attention to the robot. Navigation policies for such robots are increasingly learned through reinforcement learning or imitation. When a learned policy meets a situation outside its training data, it still produces an action, and nothing in that action tells the robot or its operator that the policy may be wrong.
Language models can generate programming exercises and solutions, but incorrect answers, weak tests, and repetitive tasks can limit their value as training data. This thesis builds on CodifieRL to generate synthetic coding tasks from real repositories and investigate whether execution-based filtering improves their quality and usefulness for training code models.
Foundation models let a robots accept missions in natural language, but they also let a wrong or malicious instruction reach the propellers. PEACE [1] is a planner–executor agent for robots in which a large language model emits a complete, typed mission plan in a single call, and an independent safety service validates every motion command before actuation. That service currently enforces two geometric constraints: an altitude band and a horizontal geofence. It cannot express, let alone enforce, the rules that matter in the real world: keep distance from people, do not hover in front of windows, maintain a minimum altitude over roads, respect regulatory limits. This project replaces the geometric check with a two-tier guardrail: a plan-level check that grounds natural-language safety rules into formal specifications and verifies the whole mission plan before dispatch, and an execution-level check that grounds the same rules into spatial keep-out sets around detected objects.
Swarms of unmanned aerial vehicles (UAVs) are used in search and rescue, infrastructure inspection, and environmental monitoring. An operator supervising such a swarm has to follow many vehicles at once, understand where they are relative to one another, and issue commands to groups rather than to single drones. Ground control stations present this three-dimensional situation on flat screens, which leaves the operator to reconstruct the spatial picture mentally. A Mixed Reality (MR) headset can instead place drone positions, planned trajectories, and status information in the space where the drones actually fly.
PEACE [1] is a planner–executor agent for a single-robot systems: a large language model (LLM) turns a natural-language mission into one typed plan of tool calls, an executor dispatches it deterministically over ROS 2, and an independent constraint layer validates every motion command. Many missions that motivate language-tasked drones, such as area survey, search, or inspection of distributed infrastructure, are naturally multi-robot. This project extends the planner–executor paradigm from single-robot system to a multi-robot system: the planner must decompose a mission into per-robot plans with dependencies, the executor must coordinate several robots, the world model must be shared, and the constraint layer must enforce inter-robot constraints such as separation and operational environment partitioning in addition to per-robot geofences.
Language models can generate plausible code that fails its tests. This thesis investigates whether internal attention patterns predict failure before generation finishes, allowing computation to shift toward more promising candidates. The student will build on the Prober repository to train and evaluate lightweight prediction models.
Modern Android applications are increasingly built using hybrid frameworks such as React Native, Flutter, and Cordova, allowing developers to deploy apps across multiple platforms with a single codebase. Like native apps, hybrid applications heavily rely on Third-Party Libraries (TPLs) to accelerate development and integrate functionalities like analytics, advertising, and UI components. However, TPLs can introduce severe security vulnerabilities and privacy risks. While numerous techniques exist to detect TPLs in native Android applications (analyzing Dalvik bytecode), identifying TPLs in hybrid apps remains a significant challenge. This project aims to bridge this gap by investigating the structure of hybrid apps and developing a novel approach to accurately detect embedded TPLs within their non-native components (e.g., JavaScript bundles, Dart binaries).
This topic investigates unsupervised anomaly detection for merge conflict resolution in software engineering. Existing approaches generally formulate conflict resolution as supervised classification over predefined resolution labels. However, even a large labeled dataset may contain rare, unusual, or incorrectly reconstructed cases, and the labels may not adequately represent the full diversity of merge behavior. This project will develop methods to identify anomalous merge conflicts using the available conflict representations while initially ignoring the nine resolution labels. The objective is to determine whether anomaly scores can identify unusual conflict structures, rare resolution patterns, or cases that are difficult for conventional classifiers.
This topic investigates the quality and correctness of nine automatically assigned labels for merge-conflict resolutions. The labels were reconstructed by replaying historical Git merges and mapping the tentative conflict representation to the final developer-committed file. Such mappings are reproducible and scalable, but they can be affected by formatting changes, reordered code, structural edits, ambiguous correspondence between conflict chunks, and compound resolutions. The project will develop methods to detect potentially incorrect, ambiguous, or unstable labels and will validate a sample of these cases through systematic inspection. The work combines supervised, unsupervised, and weakly supervised methods for label-quality assessment.