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Technical Lag as Latent Technical Debt: A Rapid Review

Shane K. Panter, Nasir U. Eisty

TL;DR

This rapid review reframes technical lag (TL) as a latent form of technical debt (LTD) that accrues passively through outdated dependencies, offering a consolidated view of its definitions, detection metrics, and management strategies. It maps TL across six metrics categories, highlights ecosystem-specific challenges, and identifies five key research gaps to advance LT measurement, data quality, automation, security integration, and practitioner guidelines. By linking TL to LTD, the paper provides a foundation for integrating TL into existing debt-management frameworks, enabling more reliable maintenance of large codebases with external dependencies. The work emphasizes practical implications for software sustainability, reliability, and security through improved detection, monitoring, and debt-aware decision making.

Abstract

Context: Technical lag accumulates when software systems fail to keep pace with technological advancements, leading to a deterioration in software quality. Objective: This paper aims to consolidate existing research on technical lag, clarify definitions, explore its detection and quantification methods, examine underlying causes and consequences, review current management practices, and lay out a vision as an indicator of passively accumulated technical debt. Method: We conducted a Rapid Review with snowballing to select the appropriate peer-reviewed studies. We leveraged the ACM Digital Library, IEEE Xplore, Scopus, and Springer as our primary source databases. Results: Technical lag accumulates passively, often unnoticed due to inadequate detection metrics and tools. It negatively impacts software quality through outdated dependencies, obsolete APIs, unsupported platforms, and aging infrastructure. Strategies to manage technical lag primarily involve automated dependency updates, continuous integration processes, and regular auditing. Conclusions: Enhancing and extending the current standardized metrics, detection methods, and empirical studies to use technical lag as an indication of accumulated latent debt can greatly improve the process of maintaining large codebases that are heavily dependent on external packages. We have identified the research gaps and outlined a future vision for researchers and practitioners to explore.

Technical Lag as Latent Technical Debt: A Rapid Review

TL;DR

This rapid review reframes technical lag (TL) as a latent form of technical debt (LTD) that accrues passively through outdated dependencies, offering a consolidated view of its definitions, detection metrics, and management strategies. It maps TL across six metrics categories, highlights ecosystem-specific challenges, and identifies five key research gaps to advance LT measurement, data quality, automation, security integration, and practitioner guidelines. By linking TL to LTD, the paper provides a foundation for integrating TL into existing debt-management frameworks, enabling more reliable maintenance of large codebases with external dependencies. The work emphasizes practical implications for software sustainability, reliability, and security through improved detection, monitoring, and debt-aware decision making.

Abstract

Context: Technical lag accumulates when software systems fail to keep pace with technological advancements, leading to a deterioration in software quality. Objective: This paper aims to consolidate existing research on technical lag, clarify definitions, explore its detection and quantification methods, examine underlying causes and consequences, review current management practices, and lay out a vision as an indicator of passively accumulated technical debt. Method: We conducted a Rapid Review with snowballing to select the appropriate peer-reviewed studies. We leveraged the ACM Digital Library, IEEE Xplore, Scopus, and Springer as our primary source databases. Results: Technical lag accumulates passively, often unnoticed due to inadequate detection metrics and tools. It negatively impacts software quality through outdated dependencies, obsolete APIs, unsupported platforms, and aging infrastructure. Strategies to manage technical lag primarily involve automated dependency updates, continuous integration processes, and regular auditing. Conclusions: Enhancing and extending the current standardized metrics, detection methods, and empirical studies to use technical lag as an indication of accumulated latent debt can greatly improve the process of maintaining large codebases that are heavily dependent on external packages. We have identified the research gaps and outlined a future vision for researchers and practitioners to explore.
Paper Structure (24 sections, 2 figures, 2 tables)

This paper contains 24 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Goal and Research Questions
  4. Search Strategy
  5. Selection Procedure
  6. Extraction Procedure
  7. Results
  8. RQ1: Primary Causes
  9. Ecosystem Factors
  10. Developer Decision Patterns
  11. Infrastructure and Tooling Impact
  12. RQ2: Metrics and Methods
  13. RQ3: Research Gaps
  14. Research Gap 1 Methods and Metrics
  15. Research Gap 2 Data Quality and Coverage
  16. ...and 9 more sections

Figures (2)

  • Figure 1: Results from the initial database query ➀ were filtered by our inclusion and exclusion criteria ➁. Two rounds of forward and backward snowballing were completed ➂, ➃ to yield a final set of studies.
  • Figure 2: A Technical Lag feedback loop for Technical Debt.