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Shojibur Rahman

Publications and source records attributed to Shojibur Rahman.

2 recordsLinked to original sources

A Study of Consumers Cognitive Load in eCommerce Websites using Eye-tracking Technology

The aesthetics of e-commerce websites have a big influence on purchasing decisions and customers' satisfaction. Webpage complexity and high cognitive load are responsible for causing an unpleasant experience while shopping online. This research empirically inspects a correlation between users' cognitive load and product pricing, where price plays a vital role in causing web complexity. Therefore, we have experimented on 48 random individuals using eye-tracking technology to observe the eye movement calibration on some reputed e-commerce websites. We measured the cognitive load extracted from users' datasets by analyzing fixation count, saccades, fixation duration, and task completion time. Our study induces new findings on website complexity which varies on the similar product but different price ranges. This research also demonstrates a strong connection between customer perception and visual complexity while making online purchases. In addition, these findings will assist the developers and business analysts to improve consumers' shopping experience in e-commerce websites.

cs.HC

A Task-Level Evaluation of AI Agents in Open-Source Projects

In this paper, we present a comparative study of five autonomous coding agents using AIDev-pop, which is a public dataset containing thousands of AI-generated pull requests (PRs) across popular open-source repositories. We evaluate agents' performance along three task-aware dimensions spanning the PR lifecycle: (1) PR acceptance rate, (2) review discussion volume, and (3) commit message quality. Our quantitative analysis finds that Codex consistently achieves high PR acceptance rates across most task categories, while Copilot's PRs trigger the highest volume of both human and automated review discussions. In contrast, commit-level quality varies independently of acceptance outcomes. Claude and Cursor produce higher proportions of high-quality commit messages across several task types, and Codex exhibiting comparatively lower commit quality despite strong integration outcomes. Our findings inform selection and improvements of AI agents for their effective integration to collaborative software engineering.

cs.SE