Agentic Workflow Injection is a new injection vulnerability class in LLM-augmented GitHub Actions, with two patterns (P2A and P2S) detected via the TaintAWI tool yielding 496 confirmed exploitable instances across 13,392 workflows.
hub
Test Intention Guided LLM-Based Unit Test Generation
48 Pith papers cite this work. Polarity classification is still indexing.
48
Pith papers citing it
hub tools
co-cited works
years
2026 48representative citing papers
First empirical study of correctness bugs in torch.compile characterizes their patterns and proposes AlignGuard, which found 23 confirmed new bugs via LLM-guided test mutation.
A compositional algebraic decision diagram algorithm quantifies sensitivity in decision tree ensembles with certified error and confidence bounds, outperforming model counters on benchmarks.
LongMemEval-V2 is a new benchmark where AgentRunbook-C reaches 72.5% accuracy on long-term agent memory tasks, beating RAG baselines at 48.5% and basic coding agents at 69.3%.
CppPerf-Mine produces CppPerf-DB, a benchmark of 347 real-world performance-improving C++ patches (39% multi-file) from 42 repositories to evaluate repository-level repair tools.
ConCovUp uses static analysis to ground LLM test generation and backward tracing to produce concurrent test drivers that raise average shared-memory access pair coverage from 36.6% to 68.1% on nine real-world libraries.
Merlin generates CodeQL queries from natural language questions via RAG-based iteration and a self-test technique using assistive queries, achieving 3.8x higher task accuracy and 31% less completion time in user studies while finding additional software issues.
LLM2Ltac mines symbolic tactics from 11,725 Coq theorems using LLMs and integrates them into CoqHammer, improving proof rates by 23.87% on 6,199 theorems from four large verification projects.
MASPrism attributes failures in LLM multi-agent executions by extracting token-level negative log-likelihood and attention weights from a small model's prefill pass, then ranking candidates with a second prefill, achieving top accuracy on most benchmarks and 6.69x speedup over baselines.
SmellBench is the first benchmark showing LLM agents resolve 47.7% of architectural code smells while accurately spotting false positives, but aggressive repairs often introduce new smells and degrade overall quality.
VulKey reaches 31.5% repair accuracy on real C/C++ vulnerabilities by matching hierarchical expert patterns to guide LLM patch generation, beating prior baselines by 7.6%.
CASCADE finds code-documentation mismatches by running LLM-generated tests from docs and confirming failure only when documentation-derived code succeeds on the same test.
LeetProof achieves higher rates of fully certified program synthesis from natural language by using a multi-modal verifier in Lean to validate specifications via randomized testing and delegate proofs to AI tools, outperforming single-mode baselines on benchmarks while uncovering defects in prior参考.
Software engineering scope expands beyond executable code to semi-executable artifacts best diagnosed by the new six-ring Semi-Executable Stack model.
Atropos uses GCN on inference graphs for early failure prediction and hotswaps to larger LLMs, achieving 74% of large-model performance at 24% cost.
R2Eval is a new benchmark with 135 real-world code reasoning problems from Python projects that preserves complex data structures for more realistic LLM evaluation.
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.
A custom LLM agent achieves 94% manually verified success on a new benchmark of 35 software analysis setups, outperforming baselines at 77%, but struggles with stage mixing, error localization, and overestimating its own success.
ReCodeAgent uses a multi-agent system to translate and validate large code repositories across multiple programming languages, achieving 60.8% higher test pass rates than prior neuro-symbolic and agentic methods on 118 real-world projects.
FLARE extracts specifications from multi-agent LLM code and applies coverage-guided fuzzing to achieve 96.9% inter-agent and 91.1% intra-agent coverage while uncovering 56 new failures across 16 applications.
TRACE reveals that LLMs detect documentation bugs and contradictions better than subtle implementation drift, with asymmetric sensitivity and poor confidence calibration across seven models on 22k traces.
Developers using AI assistants exhibit more stable emotions and greater focus on code creation, evaluation, and verification, captured in a new four-dimensional S-IASE model from retrospective labeling of screen recordings, surveys, and interviews.
Code language models show no transferable security understanding from code diffs alone, rely on commit messages, miss over 93% of fixes at 0.5% false positive rate, and suffer large drops under group or temporal splits.
TestGeneralizer generalizes an initial test into a set of executable tests covering more diverse scenarios, delivering +31.66% mutation-based and +23.08% LLM-assessed scenario coverage gains over ChatTester on 12 open-source Java projects.
citing papers explorer
-
Demystifying and Detecting Agentic Workflow Injection Vulnerabilities in GitHub Actions
Agentic Workflow Injection is a new injection vulnerability class in LLM-augmented GitHub Actions, with two patterns (P2A and P2S) detected via the TaintAWI tool yielding 496 confirmed exploitable instances across 13,392 workflows.
-
Demystifying the Silence of Correctness Bugs in PyTorch Compiler
First empirical study of correctness bugs in torch.compile characterizes their patterns and proposes AlignGuard, which found 23 confirmed new bugs via LLM-guided test mutation.
-
AI-Assisted Unit Test Writing and Test-Driven Code Refactoring: A Case Study
AI models generated nearly 16,000 lines of unit tests in hours and enabled safe large-scale refactoring with up to 78% branch coverage in a case study.