Systematic literature review on forecasting and prediction of technical debt evolution
<|reference_start|>Systematic literature review on forecasting and prediction of technical debt evolution: Context: Technical debt (TD) refers to the additional costs incurred due to compromises in software quality, providing short-term advantages during development but potentially compromising long-term quality. Accurate TD forecasting and prediction are vital for informed software maintenance and proactive management. However, this research area lacks comprehensive documentation on the available forecasting techniques. Objective: This study aims to explore existing knowledge in software engineering to gain insights into approaches proposed in research and industry for forecasting TD evolution. Methods: To achieve this objective, we conducted a Systematic Literature Review encompassing 646 distinct papers published until 2023. Following established methodology in software engineering, we identified and included 14 primary studies for analysis. Result: Our analysis unveiled various approaches for TD evolution forecasting. Notably, random forest and temporal convolutional networks demonstrated superior performance compared to other methods based on the result from the primary studies. However, these approaches only address two of the fifteen identified TD types, specifically Code debt and Architecture debt, while disregarding the remaining types. Conclusion: Our findings indicate that research on TD evolution forecasting is still in its early stages, leaving numerous challenges unaddressed. Therefore, we propose several research directions that require further investigation to bridge the existing gaps. Keywords: Systematic literature review, Technical debt, Technical debt prediction, Technical debt forecasting, Technical debt metrics<|reference_end|>
@article{ajibode2024systematic,
    title={Systematic literature review on forecasting and prediction of technical
  debt evolution},
    author={Adekunle Ajibode, Yvon Apedo, Temitope Ajibode},
    journal={arXiv preprint arXiv:2406.12026},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12026},
    primaryClass={cs.SE}
}