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