Combined Program Analysis Techniques: A Systematic Mapping Study

TL;DR

This systematic mapping study categorizes and analyzes 248 research papers on combining program analysis techniques, revealing common patterns, goals, and benefits to guide future research and practice.

Contribution

It introduces a novel taxonomy for classifying combined-program-analysis techniques and maps existing studies to this framework, enhancing understanding of their interactions and benefits.

Findings

Revealed common goals and patterns in combined analysis techniques

Identified different interaction schemata and workflows used in the literature

Provided a structured framework to guide future research in combined program analysis

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.