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6 papers match

cs.SE2026

PROGRESS: Property-Guided Regression Search for Semantic Falsification

Davis Tocheuk Mo, Noshin Ulfat, Matthew B. Dwyer +1

PROGRESS combines property‑based testing with coverage‑guided, search‑based regression test generation to automatically create tests that reach deep program states and expose bugs…

#software testing#property-based testing#regression test generation#search-based software engineering
cs.SE2026

Specula: Scaling formal specifications for autonomous model checking of system code

Qian Cheng, Saad Mohammad Rafid Pial, Ruize Tang +6

Specula is an autonomous system that uses large language model agents to generate TLA+ specifications for complex system code and then applies model checking to discover bugs.

#formal specifications#model checking#large language models#system code
cs.SE2026

Metamorphic Coverage

Jinsheng Ba, Yuancheng Jiang, Manuel Rigger

The paper proposes Metamorphic Coverage, a metric that measures the distinct code exercised by pairs of inputs in metamorphic testing, and shows it correlates better with bug detec…

#metamorphic testing#coverage metrics#bug detection#software testing
cs.SE2026

FuncDroid: Towards Inter-Functional Flows for Comprehensive Mobile App GUI Testing

Jinlong He, Changwei Xia, Binru Huang +3

The paper introduces FuncDroid, a GUI testing tool for mobile apps that builds a Functional Flow Graph to model and explore interactions between functional units, achieving higher…

#mobile app testing#gui testing#inter-functional flows#functional flow graph
cs.PL2026

When is LLM-Based Program Reasoning Correct? A Completion Semantics for LLM-Based Code Inference

Zhiyuan Liu, Yihe Li, Trevor E. Carlson +3

The paper defines a completion semantics for reasoning about incomplete code fragments generated by large language models, and introduces a witness-generation workflow to validate…

#large language models#program reasoning#code inference#completion semantics
cs.SE2026

Knowledge-Guided Synthetic Bug Feedback for LLM-Based Unit Test Generation

Ziheng Wang, Maike Li, Chen Zhi

The paper proposes a framework that converts historical bug mechanisms into synthetic bugs to guide large language models in generating more effective unit tests that can detect re…

#unit test generation#large language models#bug detection#synthetic feedback