Static program slicing is a fundamental software engineering technique for isolating code relevant to specific variables. While recent learning-based approaches using language models (LMs) show promise in automating slice prediction, they suffer from inaccurate dependency modeling and unconstrained generation, where LMs fail to capture precise data flow relations and produce slices containing hallucinated tokens and statements. To address these challenges, we propose SliceFormer, a novel approach that reformulates static program slicing as a sequence-to-sequence task using small language models such as CodeT5+. introduces two key innovations that directly target the identified limitations. First, to improve dependency modeling, we design dataflow-aware pretraining objectives that leverage data flow graphs DFG to teach models data dependencies through dataflow-preserving statement permutation and dataflow-aware span corruption. Second, to eliminate hallucination, we develop a constrained decoding mechanism that enforces both lexical and syntactic constraints. We evaluate SliceFormer on Java and Python program slicing benchmarks, demonstrating consistent improvements over state-of-the-art baselines with up to 22% gain in ExactMatch.

Retrieval-Augmented Generation (RAG) enhances code generation by incorporating retrieved code examples into prompts, but the resulting long-context inputs impose substantial memory and computational overhead. Existing prompt compression techniques are largely designed for natural language and fail to account for the structural and semantic properties of code, while also lacking fine-grained control over compression ratios. We propose CodePromptZip, a code-aware prompt compression framework for RAG that enables precise length control while preserving critical information. Motivated by type-aware ablation studies, CodePromptZip leverages static analysis to rank code tokens by information gain and applies a dynamic compression strategy to retain the most informative tokens under a given budget. For incomplete or unparsable code snippets, CodePromptZip employs a language-model-based compressor trained on analyzable samples and augmented with a copy mechanism to preserve key tokens. Extensive experiments on three code-related tasks demonstrate that CodePromptZip consistently outperforms entropy-based and distillation-based baselines, achieving improvements of 23.4%, 28.7%, and 8.7%, respectively, while providing accurate control over compression ratios.

Optimizing Large Language Model (LLM) performance requires well-crafted prompts, but manual prompt engineering is labor-intensive and often ineffective. Automated prompt optimization techniques address this challenge but the major of them rely on randomly selected evaluation subsets, which fail to represent the full dataset, leading to unreliable evaluations and suboptimal prompts. Existing coreset selection methods, designed for LLM benchmarking, are unsuitable for prompt optimization due to challenges in clustering similar samples, high data collection costs, and the unavailability of performance data for new or private datasets. To overcome these issues, we propose IPOMP, an Iterative evaluation data selection approach for effective Prompt Optimization using real time Model Performance. IPOMP is a two-stage approach that selects representative and diverse samples using semantic clustering and boundary analysis, followed by iterative refinement with real-time model performance data to replace redundant samples. Evaluations on two datasets BIG-bench and LIAR, and two models GPT-3.5 and GPT-4o-mini, show that IPOMP improves effectiveness by at least 1.6% to 3.1%, and stability by at least 50% to 55.5% compared with the best baseline across the studied datasets and models, with minimal computational overhead below 1%. Furthermore, the results demonstrate that our real-time performance-guided refinement approach can be universally applied to enhance existing coreset selection methods.

Visual Question Generation (VQG) research focuses predominantly on natural images while neglecting the diagram, which is a critical component in educational materials. To meet the needs of pedagogical assessment, we propose the Diagram-Driven Course Questions Generation (DDCQG) task and construct DiagramQG, a comprehensive dataset with 15,720 diagrams and 25,798 questions across 37 subjects and 371 courses. Our approach employs course and input text constraints to generate course-relevant questions about specific diagram elements. We reveal three challenges of DDCQG: domain-specific knowledge requirements across courses, long-tail distribution in course coverage, and high information density in diagrams. To address these, we propose the Hierarchical Knowledge Integration framework (HKI-DDCQG), which utilizes trainable CLIP for identifying relevant diagram patches, leverages frozen vision-language models for knowledge extraction, and generates questions with trainable T5. Experiments demonstrate that HKI-DDCQG outperforms existing models on DiagramQG while maintaining strong generalizability across natural image datasets, establishing a strong baseline for DDCQG.