A Defect is Being Born: How Close Are We? A Time Sensitive Forecasting Approach
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914233363464192 |
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| author | Robredo, Mikel Esposito, Matteo Palomba, Fabio Peñaloza, Rafael Lenarduzzi, Valentina |
| author_facet | Robredo, Mikel Esposito, Matteo Palomba, Fabio Peñaloza, Rafael Lenarduzzi, Valentina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01921 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Defect is Being Born: How Close Are We? A Time Sensitive Forecasting Approach Robredo, Mikel Esposito, Matteo Palomba, Fabio Peñaloza, Rafael Lenarduzzi, Valentina Software Engineering Artificial Intelligence Information Retrieval Machine Learning 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. |
| title | A Defect is Being Born: How Close Are We? A Time Sensitive Forecasting Approach |
| topic | Software Engineering Artificial Intelligence Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2601.01921 |