Combined Program Analysis Techniques: A Systematic Mapping Study

2026-05-19

AI Summary

This work addresses the inherent limitations of individual program analysis techniques—particularly their constrained precision, coverage, and insight—which hinder comprehensive software reliability assurance. Through a systematic mapping study of 248 relevant publications, the paper presents the first taxonomy of combined program analysis approaches explicitly centered on synergistic effects and interaction patterns. The proposed multidimensional classification framework is structured around three core dimensions: collaboration objectives, workflow architectures, and types of mapping functions. This framework systematically uncovers commonalities and distinctions in the design of existing methods, offering a clear conceptual foundation for understanding, comparing, and developing novel combined analysis techniques. Furthermore, it delineates current research trends and identifies promising directions for future investigation.

Abstract

Context. Since the eighties, the combination of program analysis techniques has been increasingly recognized as a promising approach to overcome the limitations of standalone methods. While individual techniques, based on either static or dynamic analysis, address important challenges in software dependability, their integration often yields synergistic effects on precision, coverage and insights. Objective. This paper surveys a significant portion of the modern literature on combining program analysis techniques, consisting of 248 primary studies, with the aim of cataloging the types of interactions and synergies that were exploited to define combined-program-analysis techniques so far. The goal is to provide a structured understanding of why and how program analysis techniques can be conjoined, and which benefits can arise from their interactions. Method. We devise an original taxonomy that classifies combined-program-analysis techniques according to their aimed synergistic effects, inter-analysis workflows and interaction schemata (to which we refer to as mapping functions). We then map the primary studies to the taxonomy, answering research questions on which synergistic effects those studies pursued via the combination of analysis techniques, which inter-analysis workflows they embodied, and which types of mapping functions they exploited. Conclusion. Our taxonomy and literature mapping reveal the commonalities and the differences, in terms of goals and patterns, in the design of combined-program-analysis techniques. Thereby we provide a framework of concepts that can foster the ability of researchers and practitioners to reason on existing combined-program-analysis techniques, and steer further research on new useful combined-program-analysis techniques and analysis frameworks.

Problem

• program analysis
• combined techniques
• systematic mapping
• synergistic effects
• software dependability

Innovation

• combined program analysis
• systematic mapping study
• taxonomy
• synergistic effects
• mapping functions