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Noshin Ulfat

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Preprint Jul 2026

PROGRESS: Property-Guided Regression Search for Semantic Falsification

Search-based regression-test generation effectively explores complex program structures, yielding high structural coverage, but its oracles are derived from the system under test: faults already present are recorded as expected behavior rather than exposed. Property-based testing offers independent semantic oracles, but depends on high-quality properties and gives little guidance for reaching deep states or satisfying selective preconditions. We present PROGRESS (PROperty-Guided REgression Search for Semantic Falsification), integrating intent-driven properties into coverage-guided, search-based evolutionary test generation to reach deep program states and detect violations of intended behavior. PROGRESS (1) extracts intent-bearing code context and uses a language-model pipeline to generate executable jqwik properties while limiting implementation leakage; (2) extends EvoSuite's DynaMOSA with a search objective and property-aware fitness function per property, rewarding progress through preconditions and prioritizing falsifying executions; and (3) binds property parameters and uses jqwik-provided generators to connect quantified inputs to evolving test sequences, steering generation toward coverage and bug-detection goals. We evaluate PROGRESS on 25 large-scale Java systems against regression-test generation, standalone property-based testing, and context ablations. PROGRESS detects 328/562 current-version bugs (58%) versus none for regression-test generation, and satisfies all preconditions for 70/150 hard-to-reach properties versus 18 for standalone jqwik. Ablations show documentation and caller/callee context are key to generating valid executable properties. PROGRESS preserves structural exploration while exposing faults missed by regression-derived assertions; we release a comprehensive artifact package.

D. Mo, Noshin Ulfat, Matthew B. Dwyer et al. · 0 citations
Open access Aug 2026

Multi-SALLM: a multilingual security assessment of generated code

Multi-SALLM, a benchmarking framework designed to systematically evaluate Large Language Models’ ability to generate secure code, reveals three key findings: functional correctness and security are closely related but not equivalent, and sampling strategy is a critical risk factor.

Mohammed Latif Siddiq, Noshin Ulfat, Nishat Raihan et al. · 0 citations