An Empirical Study on Common Defects in Modern Web Browsers Using Knowledge Embedding in GPT-4o

Abstract: Technology is advancing at an unprecedented pace. With the advent of cutting-edge technologies, keeping up with rapid changes are becoming increasingly challenging. In addition to that, increasing dependencies on the cloud technologies have imposed enormous pressure on modern web browsers leading to adapting new technologies faster and making them more susceptible to defects/bugs. Although, many studies have explored browser bugs, a comparative study among the modern browsers generalizing the bug categories and their nature was still lacking. To fill this gap, we undertook an empirical investigation aimed at gaining insights into the prevalent bugs in Google Chromium and Mozilla Firefox as the representatives of modern web browsers. We used GPT-4.o to identify the defect (bugs) categories and analyze the clusters of the most commonly appeared bugs in the two prominent web browsers. Additionally, we compared our LLM based bug categorization with the traditional NLP based approach using TF-IDF and K-Means clustering. We found that although Google Chromium and Firefox have evolved together since almost around the same time (2006-2008), Firefox suffers from high number of bugs having extremely high defect-prone components compared to Chromium. This exploratory study offers valuable insights on the browser bugs and defect-prone components to the developers, enabling them to craft web browsers and web-applications with enhanced resilience and reduced errors.

An Empirical Study on Common Defects in Modern Web Browsers Using Knowledge Embedding in GPT-4.o

This paper offers an empirical analysis of defects prevalent in modern web browsers, namely Google Chromium and Mozilla Firefox, utilizing methods anchored in knowledge embedding with GPT-4.o. The authors frame their study within the rapid technological advancements and increased reliance on cloud technologies, which have intensified susceptibility to defects in web browsers. As previous research had not extensively explored comparative classifications of bugs across these prominent browsers, this study attempts to fill the gap by leveraging a LLM alongside traditional NLP techniques.

Methodological Approach

The investigation pivots on identifying common bug categories in Chromium and Firefox by employing both GPT-4.o's knowledge embedding and established NLP methods using TF-IDF and K-Means clustering. The knowledge embedding process involves transforming domain-related textual sources, such as commit messages and bug descriptions, into high-dimensional vectors, capturing intricate relationships and subtle patterns. These vectors are then compared with benchmark categories derived from browser documentation to achieve categorization. The paper reveals that GPT-4.o’s method significantly outperforms the traditional NLP approach, achieving an F1 score of 94.63%, compared to 64.01% in the latter, signifying enhanced semantic understanding in categorization.

Findings

The authors’ analysis brings to light several facets of modern browser defects:

1. Common Defect Categories: The analysis highlights that both Chromium and Firefox predominantly encounter bugs in the categories of "Developer Tools & Debugging", "UI/UX & Accessibility", and "File Handling & System Interaction". Notably, "Networking & Security" issues are identified as least frequent, indicating prioritization of security in development cycles.
2. Defect-Proneness: The study finds that Firefox exhibits a higher number of defects per component compared to Chromium, with components like DOM, Layout, and Extensions being particularly defect-prone. The researchers emphasize that this pervasive defectiveness in Firefox necessitates focused efforts towards these components to enhance software stability and user experience.
3. Effort-Consuming Bugs: The research identifies high-effort-consumption (HEC) bugs in both browsers but observes a clustering of such bugs within third-party components in Chromium. This finding suggests potential strategies for mitigating development bottlenecks, especially regarding third-party code integration.

Implications and Future Directions

The empirical evidence and subsequent discussions underscore a significant imperative for browser developers to address defects in prevalent categories, notably those impacting user interface and debugging tools—factors integral to user satisfaction and developer efficiency. Furthermore, the distinction between HEC bugs in third-party components points towards an avenue for reducing such burdens through improved third-party code management and assessment.

This study not only enriches the understanding of defects inherent to major web browsers but also sets a precedent for employing sophisticated AI models like GPT-4.o in software engineering research. The authors advocate for future research to explore temporal trends in defect occurrence, potential shifts in defect patterns across evolving software environments, and the applicability of similar methodologies in other software ecosystems, offering fertile ground for advancements in software quality assurance.

By identifying and analyzing these defects, the research contributes valuable insights into defect management and software maintenance strategies, potentially influencing future developments in browser technology and the broader field of software engineering.