Browsing by Author "Alam, Lamia"

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    Bug Report Summarization with Large Language Models
    (Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2025-10-25) Karim, Shaira Sadia; Alam, Lamia; Rahim, Abrar Mahmud
    The unstructured and verbose nature of bug reports often impedes developers from quickly comprehending the context of a problem and fixing underlying issues. While bugreportsummarizationcan facilitate fastercomprehension,existingmethodsoften rely on surface-level textual cues, leading to broken or disorganized summaries and failing to capture deeper semantic nuances. Moreover, these methods often neglect supporting code samples, which are critical for accurately detecting and understand ing software issues. In this work, we propose Chunk-and-Fuse, a novel progressive code integration framework for LLM-based abstractive bug report summarization. Chunk-and-Fuse addresses the challenge of lengthy bug-related code snippets that exceed typical large language model (LLM) context windows by incrementally inte grating segmentedcodeandtextualcontent. Weevaluateourapproachonfourbench mark datasetsacross eightLLMs,achieving7.5%–58.2%improvementsoverextractive baselines and performance comparable to leading abstractive techniques. Our find ings demonstrate that jointly leveraging textual and code information can improve bug comprehension and accelerate software maintenance workflows.
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    Designing an Empirical Framework to Estimate the Driver’s Attention
    (IEEE, 13-May-2016) Chowdhury, Priyam; Alam, Lamia; Hoque, Mohammed Moshiul
    Driver inattention is thought to cause many automobile crashes. Therefore, it is really important to pay high
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    Inter Word Semantic and String Distance Using Ontological Techniques
    (Department of Computer Science and Engineering, Military Institute of Science and Technology, 2014-12) Sultan, Sanjid Habib; Noumen, Afrin Jahan; Alam, Lamia
    Ontologies are today a key part of every knowledge based system. They provide a source of shared and precisely defined terms, resulting in system interoperability by knowledge sharing and reuse. Unfortunately, the variety of ways that a domain can be conceptualized results in the creation of different ontologies with contradicting or overlapping parts. For this reason ontologies need to be brought into mutual agreement (aligned). Thusontologymatchingbetweenwordsisausefultechniquefordataintegrationanddatasharing. Two important methods for ontology matching is the comparison of words using semantic similarity and string distance metrics. In our thesis work, we have use dalexical database called WordNet to find semantic similarity between words. StringmetricbasedsimilarityhasbeenfoundoutbyusingJaroWinklerdistanceonthe basis of commonality and differences between two words.
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    Real-Time Distraction Detection Based on Driver’s Visual Features
    (Faculty of Electrical and Computer Engineering, CUET, 7-Feb-2019) Alam, Lamia; Hoque, Mohammed Moshiul
    Driver’s distraction has been listed as the