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New multi-objective system improves bug localization in software development

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new multi-objective search-based system for automated bug localization in software development. This system aims to identify potentially buggy classes by maximizing similarity between bug reports and code while minimizing the number of suggested faulty files. Using the SPEA-2 evolutionary optimization algorithm on six open-source Java projects, the approach demonstrated higher precision and recall compared to existing methods, successfully identifying buggy classes for 88.5% of bug reports within the top 10 recommendations. The system's adaptability was further validated on an industrial Android project written in Kotlin. AI

RANK_REASON The cluster contains an academic paper detailing a new approach to bug localization using evolutionary algorithms. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New multi-objective system improves bug localization in software development

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The cluster contains an academic paper detailing a new approach to bug localization using evolutionary algorithms. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Maryam Bashir ·

    Bug Localization from Bug Reports: A Multi-Objective Approach

    Bug localization is a labor-intensive task, particularly in large software systems. When abnormal behavior occurs, developers must perform repetitive and time-consuming steps to identify faulty files. Previous studies have mainly focused on single-objective localization methods, …

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