  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
