PROGRESS: Property-Guided Regression Search for Semantic Falsification
- URL: http://arxiv.org/abs/2607.27359v1
- Date: Wed, 29 Jul 2026 18:12:12 GMT
- Title: PROGRESS: Property-Guided Regression Search for Semantic Falsification
- Abstract summary: We present PROGRESS (PROperty-Guided REgression Search for Semantic Falsification)<n>It integrates intent-driven properties into coverage-guided, search-based evolutionary test generation to reach deep program states.<n>It 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.
- Score: 16.94326144914702
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step [74.83535434786145]
We introduce ScrambleToolBench, an interactive terminal benchmark designed to isolate behavioral reasoning.<n>The benchmark requires agents to uncover hidden tool behaviors entirely through trial-and-error interaction.<n>Our evaluation of state-of-the-art language models reveals that successful initial discovery does not translate into robust adaptation.
arXiv Detail & Related papers (2026-08-03T15:07:46Z) - RESTOR: Automated Test Oracle Generation for RESTful APIs via Reinforcement Learning [21.927343858709122]
Restor is a framework that generates executable test assertions from a single observed request-response pair in a black-box setting.<n>We evaluate Restor on an industrial dataset comprising over 2,300 API traces across 246 real-world services.
arXiv Detail & Related papers (2026-07-27T03:20:28Z) - RepoMirage: Probing Repository Context Reasoning in Code Agents with Perturbations [51.43574078961796]
We introduce RepoMirage, a two-stage evaluation suite built on SWE-Bench Verified.<n>RepoMirage-Perturb applies three types of semantics-preserving repository-level perturbations, revealing a clear performance drop when correct solving requires broader context access.<n>We propose RepoAnchor, a structure-first prototype workflow that separates repository exploration from downstream problem solving.
arXiv Detail & Related papers (2026-05-25T06:26:43Z) - LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation [75.05397479715576]
Large language models (LLMs) and agents have shown promising progress, but their true capabilities and failure modes remain unclear.<n>We present the first systematic and contamination-aware study of LLM- and agent-based formal specification generation for C programs.
arXiv Detail & Related papers (2026-05-02T11:31:33Z) - Dynamic analysis enhances issue resolution [53.50448142467294]
DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
arXiv Detail & Related papers (2026-03-23T14:48:54Z) - MIST-RL: Mutation-based Incremental Suite Testing via Reinforcement Learning [19.054149750597933]
MIST-RL (Mutation-based Incremental Suite Testing via Reinforcement Learning) is a framework that shifts the focus to "scaling-by-utility"<n>We introduce a novel incremental mutation reward combined with dynamic penalties, which incentivizes the model to discover new faults while it suppresses functionally equivalent assertions.<n>Experiments on HumanEval+ and MBPP+ demonstrate that MIST-RL outperforms state-of-the-art baselines.
arXiv Detail & Related papers (2026-03-02T03:22:44Z) - Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification [71.98473277917962]
Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving.<n>We propose an alternative paradigm: self-evolving the agent's ability by iteratively verifying the policy model's outputs, guided by meticulously crafted rubrics.<n>We present DeepVerifier, a rubrics-based outcome reward verifier that leverages the asymmetry of verification.
arXiv Detail & Related papers (2026-01-22T09:47:31Z) - Use Property-Based Testing to Bridge LLM Code Generation and Validation [38.25155484701058]
Large Language Models (LLMs) excel at code generation, but ensuring their outputs to be functionally correct is a persistent challenge.<n>This paper introduces Property-Generated Solver, a novel framework that leverages Property-Based Testing (PBT) to validate high-level program properties.<n>Property-Generated Solver employs two collaborative LLM-based agents: a Generator dedicated to code generation and iterative refinement, and a Tester that manages the PBT life-cycle.
arXiv Detail & Related papers (2025-06-23T06:01:12Z) - Attributing Response to Context: A Jensen-Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generation [52.3707788779464]
We introduce a novel Jensen-Shannon Divergence driven method to Attribute Response to Context (ARC-JSD)<n>ARC-JSD enables efficient and accurate identification of essential context sentences without additional fine-tuning, gradient-calculation or surrogate modelling.<n> Evaluations on a wide range of RAG benchmarks, such as TyDi QA, Hotpot QA, and Musique, using instruction-tuned LLMs in different scales demonstrate superior accuracy and significant computational efficiency improvements.
arXiv Detail & Related papers (2025-05-22T09:04:03Z) - Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models [20.71745514142851]
RetriGen is a retrieval-augmented deep assertion generation approach.<n>We conduct experiments to evaluate RetriGen against six state-of-the-art approaches.
arXiv Detail & Related papers (2025-02-22T04:17:04Z) - Towards Automatic Generation of Amplified Regression Test Oracles [44.45138073080198]
We propose a test oracle derivation approach to amplify regression test oracles.
The approach monitors the object state during test execution and compares it to the previous version to detect any changes in relation to the SUT's intended behaviour.
arXiv Detail & Related papers (2023-07-28T12:38:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.