4 citations · 15 across the 20 of their papers we have counts for
20 papers
CauSec: Unboxing the Causal Drivers of Static Vulnerability Analysis Performance
Akram Khan, Md Akram Khan, Daniel Rodriguez-Cardenas +3
Static Application Security Testing (SAST) tools are widely used in both industry and academia. Such tools often make design choices that sacrifice detection to achieve higher perf…
On Predicting Vulnerability Severity Using In-Context Learning: An Industrial Case Study
Daniel Rodriguez-Cardenas, David Nader Palacio, Anna Schmedding +8
Modern software systems require earlier and more scalable vulnerability severity assessment to reduce exposure to high-impact security flaws. Security analysts typically assign CVS…
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…
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…
Tricky: Towards a Benchmark for Evaluating Human and LLM Error Interactions
Cole Granger, Dipin Khati, Daniel Rodriguez-Cardenas +1
Large language models (LLMs) are increasingly integrated into software development workflows, yet they often introduce subtle logic or data-misuse errors that differ from human bug…
A Causal Perspective on Measuring, Explaining and Mitigating Smells in LLM-Generated Code
Alejandro Velasco, Daniel Rodriguez-Cardenas, Dipin Khati +3
Recent advances in large language models (LLMs) have accelerated their adoption in software engineering contexts. However, concerns persist about the structural quality of the code…
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…
Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives
Dipin Khati, Yijin Liu, David N. Palacio +2
Applications of Large Language Models (LLMs) are rapidly growing in industry and academia for various software engineering (SE) tasks. As these models become more integral to criti…
SnipGen: A Mining Repository Framework for Evaluating LLMs for Code
Daniel Rodriguez-Cardenas, Alejandro Velasco, Denys Poshyvanyk
Language Models (LLMs), such as transformer-based neural networks trained on billions of parameters, have become increasingly prevalent in software engineering (SE). These models,…
Testing Practices, Challenges, and Developer Perspectives in Open-Source IoT Platforms
Daniel Rodriguez-Cardenas, Safwat Ali Khan, Prianka Mandal +3
As the popularity of Internet of Things (IoT) platforms grows, users gain unprecedented control over their homes, health monitoring, and daily task automation. However, the testing…
How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study
Alejandro Velasco, Daniel Rodriguez-Cardenas, Luftar Rahman Alif +2
Large Language Models (LLMs) have shown significant potential in automating software engineering tasks, particularly in code generation. However, current evaluation benchmarks, whi…
Lost in Transmission: An Information-Theoretic Account of Unsupervised Software Traceability
Daniel Rodriguez-Cardenas, David N. Palacio, Logan Fecko +2
Traceability remains a critical capability to ensure system reliability, maintainability, and compliance in modern software development. Although unsupervised Information Retrieval…
Measuring Emergent Capabilities of LLMs for Software Engineering: How Far Are We?
Conor O'Brien, Daniel Rodriguez-Cardenas, Alejandro Velasco +2
The adoption of Large Language Models (LLMs) across multiple contexts has sparked interest in understanding how scaling model size might lead to behavioral changes, as LLMs can exh…
Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations
David N. Palacio, Daniel Rodriguez-Cardenas, Alejandro Velasco +3
Trustworthiness and interpretability are inextricably linked concepts for LLMs. The more interpretable an LLM is, the more trustworthy it becomes. However, current techniques for i…
Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code?
Alejandro Velasco, David N. Palacio, Daniel Rodriguez-Cardenas +1
This paper discusses the limitations of evaluating Masked Language Models (MLMs) in code completion tasks. We highlight that relying on accuracy-based measurements may lead to an o…
Benchmarking Causal Study to Interpret Large Language Models for Source Code
Daniel Rodriguez-Cardenas, David N. Palacio, Dipin Khati +2
One of the most common solutions adopted by software researchers to address code generation is by training Large Language Models (LLMs) on massive amounts of source code. Although…
Evaluating and Explaining Large Language Models for Code Using Syntactic Structures
David N Palacio, Alejandro Velasco, Daniel Rodriguez-Cardenas +2
Large Language Models (LLMs) for code are a family of high-parameter, transformer-based neural networks pre-trained on massive datasets of both natural and programming languages. T…