A Heuristic Approach to Localize CSS Properties for Responsive Layout Failures

2026-05-25Software Engineering

Software Engineering
AI summary

The authors studied problems with webpage layouts not looking right on different screen sizes due to certain CSS settings. They created a method to find the exact CSS properties causing these layout issues instead of just making broad fixes. Their tool, LocaliCSS, detects the problem areas, identifies suspicious CSS properties near them, and ranks these properties by how likely they are responsible. When tested, LocaliCSS was often accurate and agreed well with what expert developers would pick to fix the issues.

Responsive Layout FailuresCSSWebpage LayoutLocalizationHeuristicRankingUser InterfaceFront-end DevelopmentEvaluation MetricsJavaScript
Authors
Tasmia Zerin, B M Mainul Hossain, Kazi Sakib
Abstract
Responsive Layout Failures (RLFs) typically arise from CSS properties that hinder proper layout behavior in different screen sizes. To find an accurate and effective solution for repairing RLFs, localization of those problematic properties is necessary. However, existing approaches only detect RLFs and apply broad CSS patches for them. The patches alter the entire layout without localizing the root cause of failure. To address this gap, we propose a heuristic approach to identify the specific CSS properties that developers would typically localize manually. The approach first detects the RLFs existing in a webpage and their affected elements. Next, it localizes the nearby HTML elements using RLF direction and relative alignment of the elements present in the RLF region. The involved CSS properties of those elements are then identified using a ranked search set of CSS properties, created by analyzing Quora and Stack Overflow queries. Finally, elements and their corresponding property pairs are ranked based on their impact on RLFs. We have implemented this approach into a tool called {\normalfont \textsc{LocaliCSS}} and evaluated it on a set of webpages using Top N Rank, MRR and P@K metrics. The tool achieved localization accuracy ranging from 45.2% (Top-1) to 92.86% (Top-7), with an MRR of 76% and a P@3 of 77.13%. Additionally, experienced front-end engineers manually localized the RLFs as part of our evaluation. Their preferred CSS properties matched the suggestions from our approach in 42.86% of cases for Top-1 rankings and up to 90.48% for Top-7 rankings.