Chetan Arora
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View article: LLM-Driven Cost-Effective Requirements Change Impact Analysis
LLM-Driven Cost-Effective Requirements Change Impact Analysis Open
Requirements are inherently subject to changes throughout the software development lifecycle. Within the limited budget available to requirements engineers, manually identifying the impact of such changes on other requirements is both erro…
View article: microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification
microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification Open
Unsupervised adaptation of CLIP-based vision-language models (VLMs) for fine-grained image classification requires sensitivity to microscopic local cues. While CLIP exhibits strong zero-shot transfer, its reliance on coarse global features…
View article: AdaptiveGuard: Towards Adaptive Runtime Safety for LLM-Powered Software
AdaptiveGuard: Towards Adaptive Runtime Safety for LLM-Powered Software Open
Guardrails are critical for the safe deployment of Large Language Models (LLMs)-powered software. Unlike traditional rule-based systems with limited, predefined input-output spaces that inherently constrain unsafe behavior, LLMs enable ope…
View article: DecipherGuard: Understanding and Deciphering Jailbreak Prompts for a Safer Deployment of Intelligent Software Systems
DecipherGuard: Understanding and Deciphering Jailbreak Prompts for a Safer Deployment of Intelligent Software Systems Open
Intelligent software systems powered by Large Language Models (LLMs) are increasingly deployed in critical sectors, raising concerns about their safety during runtime. Through an industry-academic collaboration when deploying an LLM-powere…
View article: Why Stop at Words? Unveiling the Bigger Picture through Line-Level OCR
Why Stop at Words? Unveiling the Bigger Picture through Line-Level OCR Open
Conventional optical character recognition (OCR) techniques segmented each character and then recognized. This made them prone to error in character segmentation, and devoid of context to exploit language models. Advances in sequence to se…
View article: Investigating VR Accessibility Reviews for Users with Disabilities: A Qualitative Analysis
Investigating VR Accessibility Reviews for Users with Disabilities: A Qualitative Analysis Open
Accessibility reviews provide valuable insights into both the limitations and benefits experienced by users with disabilities when using virtual reality (VR) applications. However, a comprehensive investigation into VR accessibility for us…
View article: Multi-Modal Requirements Data-based Acceptance Criteria Generation using LLMs
Multi-Modal Requirements Data-based Acceptance Criteria Generation using LLMs Open
Acceptance criteria (ACs) play a critical role in software development by clearly defining the conditions under which a software feature satisfies stakeholder expectations. However, manually creating accurate, comprehensive, and unambiguou…
View article: “A Population-Specific Breast Cancer Risk Prediction Model for Indian Women (A Pilot Study): Advancing Beyond Traditional Assessment Tools”
“A Population-Specific Breast Cancer Risk Prediction Model for Indian Women (A Pilot Study): Advancing Beyond Traditional Assessment Tools” Open
Background Breast cancer is the most prevalent cancer among women in India, characterized by late-stage diagnoses and high mortality rates. Existing breast cancer risk prediction models, such as the Gail and Tyrer-Cuzick models, were prima…
View article: A deep learning approach for objective evaluation of microscopic neuro-drilling craniotomy skills
A deep learning approach for objective evaluation of microscopic neuro-drilling craniotomy skills Open
This study introduces the first-ever well-annotated unbiased microscopic neuro-drilling effectiveness dataset and automated skill evaluation system, which surpasses the performance of an independent expert evaluator. It can be used as an u…
View article: Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score
Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score Open
Vision-language models (VLMs) like CLIP excel in zero-shot learning by aligning image and text representations through contrastive pretraining. Existing approaches to unsupervised adaptation (UA) for fine-grained classification with VLMs e…
View article: Prompting without Panic: Attribute-aware, Zero-shot, Test-Time Calibration
Prompting without Panic: Attribute-aware, Zero-shot, Test-Time Calibration Open
Vision-language models (VLM) have demonstrated impressive performance in image recognition by leveraging self-supervised training on large datasets. Their performance can be further improved by adapting to the test sample using test-time p…
View article: Identifying Physically Realizable Triggers for Backdoored Face Recognition Networks
Identifying Physically Realizable Triggers for Backdoored Face Recognition Networks Open
Backdoor attacks embed a hidden functionality into deep neural networks, causing the network to display anomalous behavior when activated by a predetermined pattern in the input Trigger, while behaving well otherwise on public test data. R…
View article: Understanding VR Accessibility Practices of VR Professionals
Understanding VR Accessibility Practices of VR Professionals Open
View article: RAGVA: Engineering retrieval augmented generation-based virtual assistants in practice
RAGVA: Engineering retrieval augmented generation-based virtual assistants in practice Open
View article: Requirements-Driven Automated Software Testing: A Systematic Review
Requirements-Driven Automated Software Testing: A Systematic Review Open
Automated software testing has significant potential to enhance efficiency and reliability within software development processes. However, its broader adoption faces considerable challenges, particularly concerning alignment between test g…
View article: Requirements-Driven Automated Software Testing: A Systematic Review
Requirements-Driven Automated Software Testing: A Systematic Review Open
Automated software testing has the potential to enhance efficiency and reliability in software development, yet its adoption remains hindered by challenges in aligning test generation with software requirements. REquirements-Driven Automat…
View article: Classification or Prompting: A Case Study on Legal Requirements Traceability
Classification or Prompting: A Case Study on Legal Requirements Traceability Open
New regulations are introduced to ensure software development aligns with ethical concerns and protects public safety. Showing compliance requires tracing requirements to legal provisions. Requirements traceability is a key task where engi…
View article: Comparing Human and LLM Generated Code: The Jury is Still Out!
Comparing Human and LLM Generated Code: The Jury is Still Out! Open
Much is promised in relation to AI-supported software development. However, there has been limited evaluation effort in the research domain aimed at validating the true utility of such techniques, especially when compared to human coding o…
View article: Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Study
Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Study Open
Generative AI technologies, particularly Large Language Models (LLMs), have transformed numerous domains by enhancing convenience and efficiency in information retrieval, content generation, and decision-making processes. However, deployin…
View article: On the Impact of Requirements Smells in Prompts: The Case of Automated Traceability
On the Impact of Requirements Smells in Prompts: The Case of Automated Traceability Open
Large language models (LLMs) are increasingly used to generate software artifacts, such as source code, tests, and trace links. Requirements play a central role in shaping the input prompts that guide LLMs, as they are often used as part o…
View article: Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Study
Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Study Open
[Context] Generative AI technologies, particularly Large Language Models (LLMs), have transformed numerous domains by enhancing convenience and efficiency in information retrieval, content generation, and decision-making processes. However…
View article: <div> <div> <div> <p><span>Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Study&nbsp;</span></p> </div> </div></div>
<span>Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Study </span> Open
View article: LQ-Adapter: ViT-Adapter with Learnable Queries for Gallbladder Cancer Detection from Ultrasound Image
LQ-Adapter: ViT-Adapter with Learnable Queries for Gallbladder Cancer Detection from Ultrasound Image Open
We focus on the problem of Gallbladder Cancer (GBC) detection from Ultrasound (US) images. The problem presents unique challenges to modern Deep Neural Network (DNN) techniques due to low image quality arising from noise, textures, and vie…
View article: Who uses personas in requirements engineering: The practitioners’ perspective
Who uses personas in requirements engineering: The practitioners’ perspective Open
Context: Personas are commonly employed in software projects to better understand end-users needs. Despite their frequent usage, there is a limited understanding of their practical application and effectiveness. Objective: This paper aims …
View article: Towards Runtime Monitoring for Responsible Machine Learning using Model-driven Engineering
Towards Runtime Monitoring for Responsible Machine Learning using Model-driven Engineering Open
View article: Towards Global Localization using Multi-Modal Object-Instance Re-Identification
Towards Global Localization using Multi-Modal Object-Instance Re-Identification Open
Re-identification (ReID) is a critical challenge in computer vision, predominantly studied in the context of pedestrians and vehicles. However, robust object-instance ReID, which has significant implications for tasks such as autonomous ex…
View article: Fairness Concerns in App Reviews: A Study on AI-Based Mobile Apps
Fairness Concerns in App Reviews: A Study on AI-Based Mobile Apps Open
Fairness is one of the socio-technical concerns that must be addressed in software systems. Considering the popularity of mobile software applications (apps) among a wide range of individuals worldwide, mobile apps with unfair behaviors an…
View article: How do software practitioners perceive human-centric defects?
How do software practitioners perceive human-centric defects? Open
View article: Optimizing Large Language Model Hyperparameters for Code Generation
Optimizing Large Language Model Hyperparameters for Code Generation Open
Large Language Models (LLMs), such as GPT models, are increasingly used in software engineering for various tasks, such as code generation, requirements management, and debugging. While automating these tasks has garnered significant atten…
View article: Managing Human-Centric Software Defects: Insights from GitHub and Practitioners' Perspectives
Managing Human-Centric Software Defects: Insights from GitHub and Practitioners' Perspectives Open
Context: Human-centric defects (HCDs) are nuanced and subjective defects that often occur due to end-user perceptions or differences, such as their genders, ages, cultures, languages, disabilities, socioeconomic status, and educational bac…