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

TL;DR

This paper explores the effectiveness of time-sensitive forecasting methods in predicting software defects early, aiming to identify precursors and improve proactive defect management in evolving software systems.

Contribution

It introduces a time-sensitive forecasting approach for defect prediction, focusing on early indicators and bug density estimation in software projects.

Findings

Empirical evidence supporting the effectiveness of time-sensitive defect forecasting

Identification of early symptoms preceding software defects

Improved accuracy in predicting bug proneness over traditional methods

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.