Code review is essential for maintaining software quality but remains time-consuming and cognitively demanding, especially in industrial environments. While language models (LMs) have shown promise in automating code review tasks, most prior work targets dominant programming languages such as Java and Python, leaving industrially relevant languages like C# underexplored. This study presents an empirical evaluation of monolingual C#-specific fine-tuning of three architecturally diverse open-source LMs – a review-specialized pretrained model, a code-pretrained large language model, and a general-purpose instruction-tuned model – across three core automated code review tasks: Code Change Quality Estimation, Review Comment Generation, and Code Refinement. The review-specialized model was fine-tuned across all three tasks, while the two larger models were fine-tuned exclusively for comment generation due to computational constraints, using a dataset combining public benchmarks with proprietary industrial repositories. We systematically examined the effects of programming-language scope (C#-only vs. multilingual) and natural-language composition (English-only vs. English–German), using both automated metrics and expert-based human evaluations. Our results show that the benefits of monolingual fine-tuning are task-dependent: C#-specific adaptation improves review-worthiness detection and enhances lexical alignment and human-perceived quality in comment generation, yet it reduces performance in code refinement, where broader multilingual pretraining proves more effective. English-only finetuning consistently outperforms bilingual configurations in comment quality. Across all tasks, fine-tuned LMs offer substantial efficiency gains over human reviewers, but human judgment remains superior for semantically complex and context-sensitive assessments, suggesting that LMs are best deployed as assistive tools within hybrid review workflows.
TLQD is an unmanned aerial vehicle (UAV) test case generation tool based on quality–diversity algorithms. It employs a two-level structural strategy with behavior-guided search, exploring test cases at different granularities in both global and local regions of the search space. In addition, TLQD includes a feasibility repair operator to handle constraint-violating test cases. This short paper reports the implementation of TLQD in the UAV Testing Tool Competition at ICST 2026.
Individual fairness testing evaluates a machine learning (ML) classifier by estimating its individual fairness ratio (IFr) and has become an important approach for assessing ML classifiers. However, existing studies on ML fairness testing focus exclusively on the Fairness Through Awareness (FTA) setting, where protected attributes are explicitly used. In this study, we introduce the first framework for individual fairness testing under Fairness Through Unawareness (FTU), where protected attributes are not explicitly available. The proposed framework, called IFT ${ }_{\text {unaware }}$, evaluates a classifier by estimating its IFr in the FTU setting. It extends individual fairness testing approaches developed for FTA by incorporating techniques, including protected attribute inference with confidence control, a generalized notion of individual similarity, and statistically guaranteed termination criteria. Our experiments show that IFT $_{\text {UNAWARE }}$ can compute IFr estimates with statistical guarantees and demonstrate the effectiveness of both the generalized similarity notion and confidence-based protected attribute inference. These results highlight both the feasibility and the limitations of assessing individual fairness under FTU and provide a foundation for future methodological advances.
We present an exploratory study on using Large Language Models (LLMs) to generate Alloy formal specifications from both requirements documentation and production source code, and to derive executable test cases from those specifications. We evaluate on two real open-source Python libraries: Flipper, a feature flag management system, and Cerberus, a data validation library. In both cases, the LLM produced workable Alloy specifications and executable tests without any manual correction. For Flipper, our pipeline uncovered a genuine bug that the existing test suite had missed: the library silently accepts duplicate flag names, directly contradicting its documented uniqueness requirement. A direct LLM baseline–generating tests from the same README but skipping the Alloy step–achieved 68 For Cerberus, the code-derived specification captured an implicit abstraction over sized types that the documentation-derived spec omitted, producing two additional tests. Across both libraries, code-based specifications showed lower variance in test generation (mean SD = 2.15) than documentation-based ones (mean SD = 5.0), though whether this generalises remains an open question. Index Terms–formal specifications, Alloy, large language models, automated testing, specification drift, software validation.
