The context is sustainbable software development. We try to find coding alternatives that are economical with regard to energy (electricity) consumption.
The idea of this project is to extend an exsting dataset for Android apps and their third-party libraries. Our current research relies on a foundational dataset of Android hybrid applications to analyze Third-Party Library (TPL) usage and associated security risks. The dataset contains currently Flutter, .NET MAUI, React Native, and Apache Cordova apps and we want to extend this with additional frameworks, such as Ionic or Unity. While we already maintain an automated data acquisition pipeline, this seminar project focuses on extending its capabilities to systematically crawl, download, and build applications built with additional modern hybrid frameworks (such as Ionic, Unity, etc.) from public repositories (e.g., GitHub, Gitlab, etc.). Crucially, this project will also expand the pipeline and dataset to include purely native Android applications. This native inclusion will serve as a vital control group, enabling comparative benchmarks that highlight architectural, performance, and security differences between native and cross-platform paradigms.
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.
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.
Large language models (LLMs) are increasingly used as high-level planners for robots: a natural-language mission is turned into a sequence of actions that a classical controller executes. Recent work has shown that such LLM-controlled robots can be jailbroken into performing physically harmful actions, often with 100% attack success, on commercial platforms [1]. Proposed defenses either wrap the planner in a guardrail [2] or restrict what the planner may emit. Planner–executor agents such as PEACE [3] take the second route: the LLM produces a single, fully typed plan over a fixed tool schema instead of free-form code or a step-by-step reasoning loop. Whether this architectural choice actually reduces the attack surface has never been measured. This project builds a reusable red-teaming harness for LLM-enabled robot planners in simulation and uses it to compare architectures under a common set of attacks.
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.