The authors compare and evaluate 20 software configuration optimizers based on six assumptions about the data, and find that no single optimizer outperforms others across all budgets. They propose a t…

The paper introduces CHECKMATE, a novel framework that uses code evolution to automatically generate and optimize algorithms for complex combinatorial problems, outperforming state-of-the-art solvers.

This paper proposes a new paradigm for human-machine cooperation in software engineering, where machines amplify engineers' reasoning through causation.

AI-PROPELLER introduces a novel interprocedural code layout optimization system that uses an agentic evolutionary workflow to achieve significant, measurable performance gains in large-scale, real-wor…

This paper proposes a portable inlining-prediction framework for lightweight systems, using production compiler diagnostics, an extractor, and a trained predictor.

The paper proposes integrating dependent pattern matching into the unification process in dependently typed languages to synthesize functions defined by case analysis, addressing the limitation of exi…

KoAT is a tool that automatically infers complexity bounds and proves termination of integer programs using an alternating modular analysis approach and a portfolio of techniques.

This paper investigates the benefits of generating multiple solutions in each generation for Evolutionary Diversity Optimisation (EDO) and proposes efficient methods to achieve it.

This paper introduces Athena, a novel impact analysis approach that combines dependence graph information with conceptual coupling using deep representation learning.

This paper compares the effect of different initialization methods on the accuracy and complexity of solutions in genetic programming for symbolic regression, finding no significant differences.

AutoPass is a multi-agent framework for compiler performance tuning using a large language model, enabling it to query compiler-internal optimization states and analyze the intermediate representation…

The paper argues that current Software Bills of Materials (SBOMs) are fundamentally flawed due to a lack of shared understanding regarding what constitutes a 'component,' demonstrating that existing t…

The study found that while multi-agent LLM code generation architectures significantly affect code complexity, the added complexity does not translate into better functional correctness, suggesting ar…

The paper proposes a new binary format that embeds compiler-generated metadata into executables, making the binary structure more transparent and enabling reliable analysis, instrumentation, and recom…

The paper proposes a general, compiler-integrated framework for secure content composition that minimizes the syntactic difference between secure and insecure coding practices.

The paper conducts an empirical evaluation of automated vulnerability detection tools across multiple software ecosystems using a curated ground-truth dataset derived from OSV, highlighting systematic…

The paper proposes a novel semi-automated method to perform continuous threat modeling by inferring the actual system architecture from combined static configuration and dynamic network flow data, sig…

This paper introduces Pairwise Modular Smell (PairSmell) for identifying flawed architectural decisions and trains machine learning models to predict two forms of PairSmell using 19 pair characteristi…

This study conducts a large-scale longitudinal analysis of CodeQL, finding that while the tool is effective at detecting vulnerabilities, its detection capabilities are not guaranteed to be stable acr…