Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements

This study examined code issue detection and revision automation by integrating Large Language Models (LLMs) such as OpenAI's GPT-3.5 Turbo and GPT-4o into software development workflows. A static code analysis framework detects issues such as bugs, vulnerabilities, and code smells within a large-scale software project. Detailed information on each issue was extracted and organized to facilitate automated code revision using LLMs. An iterative prompt engineering process is applied to ensure that prompts are structured to produce accurate and organized outputs aligned with the project requirements. Retrieval-augmented generation (RAG) is implemented to enhance the relevance and precision of the revisions, enabling LLM to access and integrate real-time external knowledge. The issue of LLM hallucinations - where the model generates plausible but incorrect outputs - is addressed by a custom-built "Code Comparison App," which identifies and corrects erroneous changes before applying them to the codebase. Subsequent scans using the static code analysis framework revealed a significant reduction in code issues, demonstrating the effectiveness of combining LLMs, static analysis, and RAG to improve code quality, streamline the software development process, and reduce time and resource expenditure.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing

Soft real-time applications are becoming increasingly complex, posing significant challenges for scheduling offloaded tasks in edge computing environments while meeting task timing constraints. Moreover, the exponential growth of the search space, presence of multiple objectives and parameters, and highly dynamic nature of edge computing environments further exacerbate the complexity of task scheduling. As a result, schedulers based on heuristic and metaheuristic algorithms frequently encounter difficulties in generating optimal or near-optimal task schedules due to their constrained ability to adapt to the dynamic conditions and complex environmental characteristics of edge computing. Accordingly, reinforcement learning algorithms have been incorporated into schedulers to address the complexity and dynamic conditions inherent in task scheduling in edge computing. However, a significant limitation of reinforcement learning algorithms is the prolonged learning time required to adapt to new environments and to address medium- and large-scale problems. This challenge arises from the extensive global action space and frequent random exploration of irrelevant actions. Therefore, this study proposes Agile Reinforcement learning (aRL), in which the RL-agent performs informed exploration and executes only relevant actions. Consequently, the predictability of the RL-agent is enhanced, leading to rapid adaptation and convergence, which positions aRL as a suitable candidate for scheduling the tasks of soft real-time applications in edge computing. The experiments demonstrate that the combination of informed exploration and action-masking methods enables aRL to achieve a higher hit-ratio and converge faster than the baseline approaches.

Uncertainty measurement for complex event prediction in safety-critical systems

Complex events originate from other primitive events combined according to defined patterns and rules. Instead of using specialists' manual work to compose the model rules, we use machine learning (ML) to self-define these patterns and regulations based on incoming input data to produce the desired complex event. Complex events processing (CEP) uncertainty is critical for embedded and safety-critical systems. This paper exemplifies how we can measure uncertainty for the perception and prediction of events, encompassing embedded systems that can also be critical to safety. Then, we propose an approach (ML\_CP) incorporating ML and sensitivity analysis that verifies how the output varies according to each input parameter. Furthermore, our model also measures the uncertainty associated with the predicted complex event. Therefore, we use conformal prediction to build prediction intervals, as the model itself has uncertainties, and the data has noise. Also, we tested our approach with classification (binary and multi-level) and regression problems test cases. Finally, we present and discuss our results, which are very promising within our field of research and work.