72 citations · 97 across the 11 of their papers we have counts for
15 papers
Selective Next-Location Prediction with MetaModelSelect: A Lightweight Post-Hoc Confidence Metamodel
Calvin Jary, Nafiseh Kahani
Next-location prediction can support proactive mobile-network decisions, but acting on unreliable predictions can waste resources. We apply MetaModelSelect, a lightweight post-hoc…
Complexity-Aware Evaluation of LLM Comprehension
Ali Mohammadi Esfahani, Nafiseh Kahani, Samuel A. Ajila
Large language models (LLMs) are increasingly used for software engineering tasks that require understanding existing source code, including behavior prediction, function explanati…
Image Augmentation as Test Generation for Deep Learning-Based Image Retrieval Systems
Yehan De Silva, Anirudh Sridhar, Armin Lotfy +6
Ensuring the reliability of deep learning-based image retrieval systems is a software engineering challenge. This paper presents a dual contribution: (1) a literature review of aug…
Runtime Compliance Verification for AI Agents
Nafiseh Kahani, Masoud Barati, Diana Addae
AI agents now handle personal data through tool use, function calls, and multi turn dialogue, which can create obligations under the General Data Protection Regulation (GDPR). Curr…
Teaching Machine Learning to Software Engineers
Nafiseh Kahani, Jason Jaskolka
Machine learning (ML) and Artificial Intelligence (AI) components are increasingly embedded in software products, yet undergraduate software engineering (SE) curricula rarely provi…
Testing Agentic Workflows with Structural Coverage Criteria
Nafiseh Kahani, Mojtaba Bagherzadeh
Multi-agent systems increasingly expose explicit workflow structure: agents, tools, tool-access rules, restrictions, and delegation paths. Existing evaluations rely largely on end-…
Prompt Optimization for LLM Code Generation via Reinforcement Learning
Ali Mohammadi Esfahani, Nafiseh Kahani, Samuel A. Ajila
Large Language Models (LLMs) can generate code from natural language, but their performance is highly sensitive to prompt formulation. We propose a reinforcement-learning-based fra…
A Privacy by Design Framework for Large Language Model-Based Applications for Children
Diana Addae, Diana Rogachova, Nafiseh Kahani +3
Children are increasingly using technologies powered by Artificial Intelligence (AI). However, there are growing concerns about privacy risks, particularly for children. Although e…
Understanding Defects in Generated Codes by Language Models
Ali Mohammadi Esfahani, Nafiseh Kahani, Samuel A. Ajila
This study investigates the reliability of code generation by Large Language Models (LLMs), focusing on identifying and analyzing defects in the generated code. Despite the advance…
Code Linting using Language Models
Darren Holden, Nafiseh Kahani
Code linters play a crucial role in developing high-quality software systems by detecting potential problems (e.g., memory leaks) in the source code of systems. Despite their benef…
An Accurate and Low-Parameter Machine Learning Architecture for Next Location Prediction
Calvin Jary, Nafiseh Kahani
Next location prediction is a discipline that involves predicting a users next location. Its applications include resource allocation, quality of service, energy efficiency, and tr…
Automated Test Case Repair Using Language Models
Ahmadreza Saboor Yaraghi, Darren Holden, Nafiseh Kahani +1
Ensuring the quality of software systems through testing is essential, yet maintaining test cases poses significant challenges and costs. The need for frequent updates to align wit…
Scalable and Accurate Test Case Prioritization in Continuous Integration Contexts
Ahmadreza Saboor Yaraghi, Mojtaba Bagherzadeh, Nafiseh Kahani +1
Continuous Integration (CI) requires efficient regression testing to ensure software quality without significantly delaying its CI builds. This warrants the need for techniques to…
Execution of Partial State Machine Models
Mojtaba Bagherzadeh, Nafiseh Kahani, Karim Jahed +1
The iterative and incremental nature of software development using models typically makes a model of a system incomplete (i.e., partial) until a more advanced and complete stage of…
Reinforcement Learning for Test Case Prioritization
Mojtaba Bagherzadeh, Nafiseh Kahani, Lionel Briand
Continuous Integration (CI) significantly reduces integration problems, speeds up development time, and shortens release time. However, it also introduces new challenges for qualit…