Cross-Platform Bug Localization Strategies - Utilizing Machine Learning for Diverse Software Environment Adaptability

Aug 5, 2026

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This research presents a hybrid machine learning approach for cross-platform bug localization, combining LSTM networks with SHAP-based explainable AI. The method preprocesses bug report text using natural language processing and feature extraction via word embeddings to capture sequential structure. A model trained on simulated bug report datasets is evaluated with accuracy, precision, recall, and F1, while SHAP values interpret prediction drivers. Results demonstrate robust, consistent performance and clearer, developer-friendly decision rationales across different software environments.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Motivation and problem context

LSTM and SHAP synergy for interpretability

Related challenges in modern systems

Cross-platform variability

Limitations of traditional approaches

Research Article Cross-Platform Bug Localization Strategies: Utilizing Machine Learning for Diverse Software Environment Adaptability Waqas Ali 1 , Mariam Sabir 2,* 1 School of Information Engineering, Yangzhou University, Yangzhou, 225000, China 2 Faculty of Agriculture, University of Agriculture, Faisalabad, Pakistan *Corresponding Author: Mariam Sabir, [E-mail: mariamsabir12340@gmail.com](E-mail: mariamsabir12340@gmail.com) | Article Info | | Abstract | | --- | --- | --- | | Article History | | This paper introduces a novel hybrid machine learning model that combines Long Short-Term | | Received Feb 28, 2024 | | Memory (LSTM) networks and SHapley Additive exPlanations (SHAP) to enhance bug locali- | | Revised Mar 12, 2024 | | zation across multiple software platforms. The aim is to adapt to the variability inherent in differ- | | Accepted Mar 29, 2024 | | ent operating systems and provide transparent, interpretable results for software developers. Our methodology includes comprehensive preprocessing of bug report data using advanced natural | | Keywords | | | | Machine Learning | | language processing techniques, followed by feature extraction through word embeddings to ac- | | Bug Localization | | commodate the sequential nature of text data. The LSTM model is trained and evaluated on a | | Cross-Platform Software | | dataset of simulated bug reports, with the results interpreted using SHAP values to ensure clarity | | LSTM Networks | | in decision-making. The results demonstrate the model’s robustness, adaptability, and consistent | | Explainable AI (XAI) | | performance across platforms, as evidenced by accuracy, precision, recall, and F1 scores. The | | SHAP Values | | dataset's distribution of bug categories and statuses further provides valuable insights into com- | | Natural Language Processing | | mon software development issues. | | Software Development Feature Engineering | | | | | Copyright: © 2024 Waqas Ali and Mariam Sabir. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY 4.0) license. | | 1. Introduction As software becomes increasingly integral to every aspect of modern life, the cost and frequency of software errors have escalated, making efficient bug localization an essential process in software development. The advent of complex, multi-platform environments has compounded developers' challenges in identifying and resolving bugs efficiently. Traditional debugging methods often struggle to keep pace with the scale and diversity of modern software systems [1] . The objective of this research is to address these challenges by leveraging advancements in machine learning (ML) and explainable artificial intelligence (XAI) to propose a novel, hybrid model that utilizes Long Short-Term Memory (LSTM) networks and SHapley Additive exPlanations (SHAP) for effective and interpretable bug localization across various software platforms. The intersection of LSTM and XAI, particularly SHAP, offers a promising synergy for enhancing the bug localization process. While LSTM networks excel in identifying patterns in sequential data, such as bug reports, SHAP values provide much-needed transparency by explaining the predictions made by ML models [2, 3] . This dual approach caters to the urgent need for efficient localization tools and transparent rationale, especially in critical software applications. In contemporary security and surveillance landscapes, real-time object detection plays a pivotal role in ensuring the safety and security of various environments. Particularly in urban settings characterized by dynamic and complex scenarios, the ability to promptly and accurately identify objects of interest holds significant importance. Traditionally, surveillance systems have relied on manual monitoring or basic detection algorithms, which often lack the efficiency and accuracy required to address modern securi

FAQs

What model does the paper propose for bug localization?

The paper proposes a hybrid model combining LSTM networks with SHAP to improve both effectiveness and interpretability for bug localization.

How does the approach handle bug report text?

It preprocesses bug report data with natural language processing and extracts features using word embeddings to model the sequential nature of text.

How are the results explained to developers?

SHAP values are used to interpret the predictions made by the machine learning model, providing transparent and understandable decision cues.

Aug 5, 2026

Upload by Liam