User experience (UX) evaluation is central to software quality, yet it remains difficult to integrate into software engineering workflows in a systematic, explainable, and early-stage manner. This paper presents TraceUX, a framework for operationalizing UX heuristics and design guidance into a rule-based static evaluation pipeline that combines machine-interpretable formalization, executability-aware assessment, context-sensitive scoring, and actionable reporting. The framework is instantiated using Apple Human Interface Guidelines, Swift abstract syntax trees, and mobile games, and implemented in a proof-of-concept tool named TraceHIG. Evaluation was conducted in four layers: analysis of the full rule repository, controlled synthetic validation with injected violations, baseline assessment of 12 public Swift game projects, and a case study on one project. The full repository contained 206 rules; after excluding non-iOS yet platform-specific rules, 193 rules were retained for the downstream experiments. In controlled validation, 216 injected violations yielded 99.2% precision, 61.6% recall, and an F1-score of 0.760. In baseline analysis, overall project scores ranged from 41.6 to 88.0, reflecting rule-conformance spread under the instantiated rule base rather than direct measures of UX quality. The case study demonstrated that profile-aware scoring can yield materially different UX assessments for the same codebase under different game configurations, highlighting the importance of app profiling in static UX evaluation. These findings show that a meaningful subset of UX knowledge can be operationalized into explainable, context-aware static analysis that provides structured and actionable decision support while complementing, rather than replacing, manual and empirical UX evaluation.
Large Language Models (LLMs) are now widely used for code generation, yet even syntactically correct output may contain logical and semantic errors that remain invisible until runtime, particularly in framework-driven applications, where correctness depends on dependency injection, framework conventions, configuration, library compatibility, and database interaction. This paper presents GenTest, an open-source platform for context-aware dynamic generation of Java code using LLMs, runtime compilation and class loading, Spring bean registration, JPA/PostgreSQL execution, and declarative YAML-based test validation. GenTest supports multiple LLM providers through a provider-agnostic interface and evaluates generated components within a live Spring Boot application context. Across 209 test cases and 933 assertions, GenTest achieves a 63.9% assertion pass rate, and 94.2% of failures occur after successful compilation, confirming that execution-centered evaluation reveals failure modes invisible to static analysis.
Muhammed Suphi Şeyhkuruş, Tolga Ovatman· Annual International Compute...· 0 citations
This paper introduces T2L (Trace-to-Line), a reproducible research framework that narrows repository-scale code into candidate vulnerable lines through AST-based chunking, structured diagnostic information collection, and evidence-guided refinement that improves trace-to-line localization.
A large-scale empirical study of mainstream open-source agent frameworks from an engineering perspective, providing empirical evidence linking framework design choices to engineering risks and highlighting the need for stronger guidance and support in agent framework development.
Yibo Zhai, Junjun Si, Yan Wang et al.· SIGSOFT FSE Companion· 0 citations
ReATest is introduced, an automated approach to enhancing PaC workflows through systematic test case generation from Rego specifications, which achieves an average 35.43% reduction in test suite size and retains 64.57% of the generated test cases.
Thanh-Binh Trinh, N. Le, Nguyen Viet Ha· Software quality journal· 0 citations
Generating structured artifacts with Large Language Models - e.g.\ database queries, threat framework mappings, entity schemas - is relatively straightforward; however, making them reliable enough for production deployments presents challenges. We present TAG, a lightweight framework based on a core principle: \textit{LLMs generate, we validate}. This reframing shifts responsibility from generation quality to validation rigor. The framework rests on three key attributes: First, \textbf{test driven generation}: when tests fail, the LLM receives indicative error messages that expose why the output failed, enabling the LLM to understand its mistakes and refine subsequent attempts. Second, \textbf{deterministic and LLM-based tests}: deterministic tests catch heuristics that can be programmatically verified (schema, syntax, cross-reference), while LLM-based tests evaluate nuanced semantic and delicate features that resist programmatic inspection (intent alignment, logical consistency, domain correctness). Third, \textbf{expert-distilled judges}: LLM-based tests are calibrated to distill and replicate human expert decision distribution, transforming manual human quality gates into scalable, reusable evaluation proxies that reflect professional-grade validation standards. We demonstrate the framework on three artifact types in the security domain - KQL query generation, MITRE ATT\&CK mapping, and entity mapping - deployed in production at Microsoft Sentinel. We believe this framework can be applied beyond security to other artifact generation tasks, providing a path to reliable, high-quality outputs without sacrificing the efficiency gains of LLM generation.
Yaniv Melamed, Yoni Zukerman, Michal Shechter et al.· 0 citations
These findings suggest that while AI agents excel at rigorous boundary testing, they lack the “environmental awareness” needed to write stable, hermetic tests.
Preet Jhanglani, Zeel Desai, Vidhi Kansara et al.· Annual International Compute...· 0 citations