14 papers
ECLAIR: A Causally-Grounded AI Framework for Scientific Discovery in Empirical Software Engineering
Alejandro Velasco, Daniel Rodriguez-Cardenas, Dipin Khati +2
The scientific method has long guided empirical research in Software Engineering (SE), but the complexity of modern software systems often hinders its systematic application. This…
Rethinking Software Empirical Studies with Structural Causal Models
Daniel Rodriguez-Cardenas, Aya Garryyeva, David Nader Palacio +2
Causal Inference offers a fundamental approach for advancing empirical software engineering (ESE) beyond traditional statistical association, enabling researchers to rigorously ide…
Towards Enabling An Artificial Self-Construction Software Life-cycle via Autopoietic Architectures
Daniel Rodriguez-Cardenas, David Nader Palacio, Denys Poshyvanyk
Software engineering research has focused on automating maintenance and evolution processes to reduce costs and improve reliability. The emergence of foundation models (FMs) with s…
Enabling Global, Human-Centered Explanations for LLMs:From Tokens to Interpretable Code and Test Generation
Dipin Khati, Daniel Rodriguez-Cardenas, David N. Palacio +3
As Large Language Models for Code (LM4Code) become integral to software engineering, establishing trust in their output becomes critical. However, standard accuracy metrics obscure…
Towards Comprehensive Benchmarking Infrastructure for LLMs In Software Engineering
Daniel Rodriguez-Cardenas, Xiaochang Li, Marcos Macedo +5
Large language models for code are advancing fast, yet our ability to evaluate them lags behind. Current benchmarks focus on narrow tasks and single metrics, which hide critical ga…
Detecting and Correcting Hallucinations in LLM-Generated Code via Deterministic AST Analysis
Dipin Khati, Daniel Rodriguez-Cardenas, Paul Pantzer +1
Large Language Models (LLMs) for code generation boost productivity but frequently introduce Knowledge Conflicting Hallucinations (KCHs), subtle, semantic errors, such as non-exist…