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

2026-03-10

AI Summary

Traditional technical lag metrics, such as version lag, fail to distinguish between actively maintained and abandoned open-source packages, leading to underestimation of security and sustainability risks. This work proposes MALTA, the first maintenance-aware framework for assessing technical lag, which introduces three novel dimensions: Development Activity Score (DAS), Maintainer Responsiveness Score (MRS), and Repository Metadata Viability Score (RMVS) to identify irreparable lag caused by upstream abandonment. Evaluated on 11,047 Debian packages with 1.7 million commits and 4.2 million pull requests from their GitHub repositories, MALTA achieves an AUC of 0.783 in differentiating active from declining maintenance states. Notably, 62.2% of packages previously classified as low-risk were reclassified as high-risk, exhibiting a median time since last commit of 2,019 days and 9.8% archived repositories, thereby exposing critical blind spots in conventional lag metrics.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Metareasoning and Metaheuristics
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Sustainability and climate impact of web technologies

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.

Problem

• Technical Lag
• Software Abandonment
• Version Lag
• Open-source Ecosystems
• Dependency Risk

Innovation

• Technical Lag
• Software Abandonment
• Maintenance Awareness
• Dependency Risk
• Open-Source Ecosystems