6 papers
What Breaks When LLMs Code? Characterizing Operational Safety Failures of Agentic Code Assistants
Alif Al Hasan, Sumon Biswas
Autonomous coding agents built on large language models (LLMs) are rapidly being integrated into development workflows, yet their operational safety properties remain poorly unders…
Improving Code Comprehension through Cognitive-Load Aware Automated Refactoring for Novice Programmers
Subarna Saha, Alif Al Hasan, Fariha Tanjim Shifat +1
Novice programmers often struggle to comprehend code due to vague naming, deep nesting, and poor structural organization. While explanations may offer partial support, they typical…
LLPut: Investigating Large Language Models for Bug Report-Based Input Generation
Alif Al Hasan, Subarna Saha, Mia Mohammad Imran +1
Failure-inducing inputs play a crucial role in diagnosing and analyzing software bugs. Bug reports typically contain these inputs, which developers extract to facilitate debugging.…
Learning Programming in Informal Spaces: Using Emotion as a Lens to Understand Novice Struggles on r/learnprogramming
Alif Al Hasan, Subarna Saha, Mia Mohammad Imran
Novice programmers experience emotional difficulties in informal online learning environments, where confusion and frustration can hinder motivation and learning outcomes. This stu…
SEAGET: Seasonal and Active hours guided Graph Enhanced Transformer for the next POI recommendation
Alif Al Hasan, Md. Musfique Anwar
One of the most important challenges for improving personalized services in industries like tourism is predicting users' near-future movements based on prior behavior and current c…
Redefining POI Popularity: Integrating User Preferences and Recency for Enhanced Recommendations
Alif Al Hasan, Md. Musfique Anwar, M. Arifur Rahman
The task of point-of-interest (POI) recommendation is to predict users' immediate future movements based on their previous records and present circumstances. Popularity is consider…