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MALTA: Maintenance-Aware Technical Lag, Estimation to Address Software Abandonment

Shane K. Panter, Nasir U. Eisty

TL;DR

MALTA identifies a substantial discordant population invisible to Version Lag by distinguishing resolvable lag from terminal lag caused by upstream abandonment, and identifies a substantial discordant population invisible to Version Lag by distinguishing resolvable lag from terminal lag caused by upstream abandonment.

Abstract

Context: Open-source ecosystems rely on sustained package maintenance. When maintenance slows or stops, Technical Lag (TL), the gap between installed and latest dependency versions accumulates, creating security and sustainability risks. However, some existing TL metrics, such as Version Lag, struggle to distinguish between actively maintained and abandoned packages, leading to a systematic underestimation of risk. Objective: We investigate the relationship between Version Lag and software abandonment by (i) identifying which repository-level signals reliably distinguish sustained maintenance from long-term decline, (ii) quantifying how Version Lag magnitude and persistence differ across maintenance states, and (iii) evaluating how maintenance-aware metrics change the identification of high-risk dependencies. Method: We introduce Maintenance-Aware Lag and Technical Abandonment (MALTA), a scoring framework comprising three metrics: Development Activity Score (DAS), Maintainer Responsiveness Score (MRS), and Repository Metadata Viability Score (RMVS). We evaluate MALTA on a dataset of 11,047 Debian packages linked to upstream GitHub repositories, encompassing 1.7 million commits and 4.2 million pull requests. Results: MALTA achieves AUC = 0.783 for classifying active versus declining maintenance. Most significantly, 62.2% of packages classified as "Low Risk" by Version Lag alone are reclassified as "High Risk" when MALTA signals are incorporated. These discordant packages average 2019 days since their last commit, with 9.8% having archived repositories. Conclusions: Version Lag metrics systematically miss abandoned packages, a blind spot affecting the majority of dependencies in distribution ecosystems. MALTA identifies a substantial discordant population invisible to Version Lag by distinguishing resolvable lag from terminal lag caused by upstream abandonment.

MALTA: Maintenance-Aware Technical Lag, Estimation to Address Software Abandonment

TL;DR

MALTA identifies a substantial discordant population invisible to Version Lag by distinguishing resolvable lag from terminal lag caused by upstream abandonment, and identifies a substantial discordant population invisible to Version Lag by distinguishing resolvable lag from terminal lag caused by upstream abandonment.

Abstract

Context: Open-source ecosystems rely on sustained package maintenance. When maintenance slows or stops, Technical Lag (TL), the gap between installed and latest dependency versions accumulates, creating security and sustainability risks. However, some existing TL metrics, such as Version Lag, struggle to distinguish between actively maintained and abandoned packages, leading to a systematic underestimation of risk. Objective: We investigate the relationship between Version Lag and software abandonment by (i) identifying which repository-level signals reliably distinguish sustained maintenance from long-term decline, (ii) quantifying how Version Lag magnitude and persistence differ across maintenance states, and (iii) evaluating how maintenance-aware metrics change the identification of high-risk dependencies. Method: We introduce Maintenance-Aware Lag and Technical Abandonment (MALTA), a scoring framework comprising three metrics: Development Activity Score (DAS), Maintainer Responsiveness Score (MRS), and Repository Metadata Viability Score (RMVS). We evaluate MALTA on a dataset of 11,047 Debian packages linked to upstream GitHub repositories, encompassing 1.7 million commits and 4.2 million pull requests. Results: MALTA achieves AUC = 0.783 for classifying active versus declining maintenance. Most significantly, 62.2% of packages classified as "Low Risk" by Version Lag alone are reclassified as "High Risk" when MALTA signals are incorporated. These discordant packages average 2019 days since their last commit, with 9.8% having archived repositories. Conclusions: Version Lag metrics systematically miss abandoned packages, a blind spot affecting the majority of dependencies in distribution ecosystems. MALTA identifies a substantial discordant population invisible to Version Lag by distinguishing resolvable lag from terminal lag caused by upstream abandonment.
Paper Structure (64 sections, 18 equations, 10 figures, 11 tables)

This paper contains 64 sections, 18 equations, 10 figures, 11 tables.

Table of Contents

  1. Introduction
  2. Research Questions
  3. Contributions
  4. Background and Motivation
  5. Package Manager
  6. Package
  7. Technical Lag and Abandonment
  8. Motivation
  9. Related Work
  10. Project Abandonment
  11. Technical Lag
  12. Methodology
  13. Problem Definition
  14. Observation Windows and Data Sources
  15. Development Activity Score (DAS)
  16. ...and 49 more sections

Figures (10)

  • Figure 1: Version Lag: The upstream (latest) version for libExample.so is (v2.3.0) while the packages for two distributions (downstream) are still at v2.0.0 (apt) and v2.1.0 (dnf). The TL of the application is 0.3.0 for Debian and 0.2.0 for Fedora.
  • Figure 2: Construction pipeline of the empirical dataset with each step detailed in Section \ref{['sec:methodology-dataset']} and summarized in Table \ref{['tab:dataset-stats']}.
  • Figure 3: Distribution of MALTA Component Scores
  • Figure 4: Distribution of MALTA Component Scores by PVAC Activity State
  • Figure 5: ROC Curves: (a) Active vs. Declining, (b) Archived prediction
  • ...and 5 more figures