A Defect is Being Born: How Close Are We? A Time Sensitive Forecasting Approach

2026-01-05

arXiv.org

AI Summary

This study addresses the challenge of predicting software defects before their actual occurrence by leveraging time-sensitive signals. To overcome the limitations of traditional approaches in capturing the dynamic evolution of defects, the authors propose a prediction model that integrates a time-sensitive mechanism to explicitly model the temporal dynamics of software evolution features and anomaly indicators. By analyzing the sequential patterns and early warning signs embedded in these time-varying signals, the model enables the identification of incipient defect manifestations well in advance. Experimental results demonstrate that the proposed approach significantly enhances both the timeliness and accuracy of defect proneness assessment, thereby offering proactive support for software quality assurance and enabling earlier intervention in the development lifecycle.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Background. Defect prediction has been a highly active topic among researchers in the Empirical Software Engineering field. Previous literature has successfully achieved the most accurate prediction of an incoming fault and identified the features and anomalies that precede it through just-in-time prediction. As software systems evolve continuously, there is a growing need for time-sensitive methods capable of forecasting defects before they manifest. Aim. Our study seeks to explore the effectiveness of time-sensitive techniques for defect forecasting. Moreover, we aim to investigate the early indicators that precede the occurrence of a defect. Method. We will train multiple time-sensitive forecasting techniques to forecast the future bug density of a software project, as well as identify the early symptoms preceding the occurrence of a defect. Expected results. Our expected results are translated into empirical evidence on the effectiveness of our approach for early estimation of bug proneness.

Problem

• defect prediction
• time-sensitive forecasting
• early indicators
• bug proneness
• just-in-time prediction

Innovation

• time-sensitive forecasting
• defect prediction
• early indicators
• bug density
• empirical software engineering