Agentic interpretation uses lattices to track LLM judgments on decomposed program claims during analysis.
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CodeSpecBench shows LLMs achieve at most 20.2% pass rate on repository-level executable behavioral specification generation, revealing that strong code generation does not imply deep semantic understanding.
SpecTune improves LLM-based automated program repair by deriving localized postconditions at execution checkpoints and using alpha and beta signals to produce precise fault-localization and patch-generation guidance.
MutDafny uses 40 mutation operators on 794 real-world Dafny programs to detect weak specifications, manually confirming five such cases at a rate of one per 241 lines.
ClassInvGen co-generates class invariants and tests with LLMs to outperform pure LLM generation and Daikon on C++ data structures.
Systematic review of 97 studies on breaking changes in five software ecosystems, producing a four-dimensional taxonomy, reason/impact categories, 43 detection approaches, and 66 mitigation strategies.
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MutDafny: A Mutation-Based Approach to Assess Dafny Specifications
MutDafny uses 40 mutation operators on 794 real-world Dafny programs to detect weak specifications, manually confirming five such cases at a rate of one per 241 lines.
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ClassInvGen: Class Invariant Synthesis using Large Language Models
ClassInvGen co-generates class invariants and tests with LLMs to outperform pure LLM generation and Daikon on C++ data structures.