arXiv subjects

Amila Indika

Publications and source records attributed to Amila Indika.

4 recordsLinked to original sources

arXiv2026

Practitioner Views on Mobile App Accessibility: Practices and Challenges

As mobile applications (apps) become ubiquitous in everyday life, it is crucial for developers to prioritize accessibility for users with diverse abilities. While previous research has identified widespread accessibility issues and raised awareness of developer challenges, there remains a lack of cross-platform, globally representative insights into how practitioners approach accessibility in practice. This paper presents findings from a mixed-methods survey of 110 mobile app developers across 43 countries, examining how platform ecosystems (iOS vs. Android) and developer experience shape accessibility practices. Results show that while developers recognize the importance of accessibility, they often rely on platform-specific guidelines and typically perform compliance testing late in the development process. Developers primarily implement text-focused features while struggling with API limitations and organizational constraints. Through systematic cross-platform comparison, we identify novel platform-specific barriers and demonstrate how accessibility practices differ across developer experience levels. Our findings offer new insights into the challenges of achieving accessibility in practice and provide actionable steps for various stakeholders to promote more consistent and inclusive app development.

cs.SE↗

arXiv2025

SODBench: A Large Language Model Approach to Documenting Spreadsheet Operations

Numerous knowledge workers utilize spreadsheets in business, accounting, and finance. However, a lack of systematic documentation methods for spreadsheets hinders automation, collaboration, and knowledge transfer, which risks the loss of crucial institutional knowledge. This paper introduces Spreadsheet Operations Documentation (SOD), an AI task that involves generating human-readable explanations from spreadsheet operations. Many previous studies have utilized Large Language Models (LLMs) for generating spreadsheet manipulation code; however, translating that code into natural language for SOD is a less-explored area. To address this, we present a benchmark of 111 spreadsheet manipulation code snippets, each paired with a corresponding natural language summary. We evaluate five LLMs, GPT-4o, GPT-4o-mini, LLaMA-3.3-70B, Mixtral-8x7B, and Gemma2-9B, using BLEU, GLEU, ROUGE-L, and METEOR metrics. Our findings suggest that LLMs can generate accurate spreadsheet documentation, making SOD a feasible prerequisite step toward enhancing reproducibility, maintainability, and collaborative workflows in spreadsheets, although there are challenges that need to be addressed.

cs.SE↗

arXiv2024

Exploring Accessibility Trends and Challenges in Mobile App Development: A Study of Stack Overflow Questions

The proliferation of mobile applications (apps) has made it crucial to ensure their accessibility for users with disabilities. However, there is a lack of research on the real-world challenges developers face in implementing mobile accessibility features. This study presents a large-scale empirical analysis of accessibility discussions on Stack Overflow to identify the trends and challenges Android and iOS developers face. We examine the growth patterns, characteristics, and common topics mobile developers discuss. Our results show several challenges, including integrating assistive technologies like screen readers, ensuring accessible UI design, supporting text-to-speech across languages, handling complex gestures, and conducting accessibility testing. We envision our findings driving improvements in developer practices, research directions, tool support, and educational resources.

cs.SE↗

arXiv2023

Performance Comparison of Binary Machine Learning Classifiers in Identifying Code Comment Types: An Exploratory Study

Code comments are vital to source code as they help developers with program comprehension tasks. Written in natural language (usually English), code comments convey a variety of different information, which are grouped into specific categories. In this study, we construct 19 binary machine learning classifiers for code comment categories that belong to three different programming languages. We present a comparison of performance scores for different types of machine learning classifiers and show that the Linear SVC classifier has the highest average F1 score of 0.5474.

cs.SE↗